Data Migration: Handling Data Types

Handling the structure, and deciding which fields, data types, or data points, relate to which fields or target elements within the destination database are one thing. Normally you can make an intelligent decision about the information that is being transferred and how that structural information should be handled.

The actual data can be a completely different problem. There are so many potential problems here with simply parsing and understanding the data that we need to look at some of the more common issues and how they should be addressed.

For most cases, the content and structure of the individual types is about understanding the two dimensions of the problem

  • Supported types – that is, whether the target database understands, or even identifies the underlying type. For example, within most RDBMS environments, most data types are very strictly enforced, but in NoSQL or datawarehouse environments they corresponding engines may either not care, or only care in specific situations.
  • Parseable information – the information may be readable or storable, but not to the same precision or definition. For example, with time values, some store hours, minutes and seconds, some store hours and minutes, and a whole variety store different lengths of fractions of seconds, to one or even 15 points of precision. At the end of the same spectrum, some dates are represented by seconds, others by a string in various formats.

Let’s look at the finer details of understanding these different types and how they can be stored, exchanged and ultimately parsed.

Basic Types

There are four basic types that make up all data and that, on the whole, get represented within a database system. Identifying these four types will help us to set the groundwork for how we handle, identify and treat the rest of the data:

  • Numeric – basically any kind of number or numeric value, from integers through to floating point and even ‘Big’ numbers.
  • Strings – Strings seem like a very simple list of characters, but they aren’t as straightforward as you might think.
  • Dates – Dates are a special type all of their own. Although they look like the worst parts of numbers and strings combined, ensuring that they are identifiable as a date in the target database relies on understanding the different formats, separators and structures.
  • Binary – any type of data that does not fall into the previous three groups is binary data. Here the issue is one of identity and the ability to interpret the content once it reachs the destination database.

Remember that in all of these situations, we are not only talking about one time formatting of the information. We could be dealing with frequent and regular exchanges of this information between the databases, and even need to perform these changes and differences regularly if the data is integrated across multiple database environments.

When combining, for example, MongoDB data with Oracle information for the processes of reporting, you need to do more than change the format once. It needs to be in a common representable format for both databases throughout the life of the data, while simultaneously ensuring that the information is best stored within each database to get the performance you need.

Strict and Relaxed Translation

Whenever you are moving data between different databases you need to determine whether the type and structure of information is important enough, or critical enough, that it must be represented in its native format.

That may sound like a completely nonsensical approach to data – surely the quality of the data is absolutely critical and should be represented as such everywhere? In theory it should, but different database storage environments treat and handle the data in different ways according to the basic type in question.

To understand why this is important, we need to look back both historically and technically why information was stored in the strict formats we described in the last section.

In any old, and particularly RDBMS-based database solution, data was stored into fixed types and with fixed lengths so that the record could be manipulated as efficient as possible. We saw some examples of this in Chapter 1. For numerical values, it is much more efficient to store a 32-bit integer as just 4 bytes of data than it is to store the string 2147483647 (which would take 9 bytes).

Similarly, with string types, the primary consideration has always been to minimize the amount of storage reserved for the string because handling bigger strings, or bigger potential blocks for strings, was more expensive in computing time, memory space, and disk space. Back when databases ran on machines with 512KB of RAM, devoting massive blocks of memory to non-usable space allocated but not used to store data just wasn’t an option. This is why 8 character filenames and two or three letter codes for a variety of databases and storage methods became common.

In modern systems of course, we actually have almost the opposite problem. Data sizes are now regularly so large that we need to be prepared to handle massive blocks of information whereas before that might have been impossible. This is fine when we are moving data from a traditional RDBMS to say Hadoop, because we move from a strict environment to a very relaxed one. But when moving in the opposite direction this is not true.

To make matters worse, in many Big Data environments, including most of the Hadoop database layers like Hive, the datatype is only significant at the time the data is queried. Within Hive you can load a CSV file that contains a variety of different types, but unless you explicitly tell Hive that the fifth column is a string or a number, Hive doesn’t really care, and let’s you export and query the data as normal.

For example, here’s a table with a mixture of different column types, here using the strict datatypes to define the actual content for each column:

hive> select * from stricttyping;
1      Hello World   2014-10-04 12:00:00   439857.34
1      Hello World   2014-10-04 12:00:00   157.3457
1      Hello World   2014-10-04 12:00:00   4.8945796E7

Now we can view the same data, this loaded into a table where each column has been defined as a string:

hive> select * from relaxedtyping;
1      Hello World   2014-10-04 12:00:00   439857.345
1      Hello World   2014-10-04 12:00:00   157.3457
1      Hello World   2014-10-04 12:00:00   48945797.3459845798475

The primary differences are in the handling of floating point values – the top strict table loses precision (the value was a FLOAT), and at the bottom the value is represented as a DOUBLE with a loss of precision digits. In fact, within Hive, the data is not parsed into the corresponding type until the data is used or displayed. If you examine the raw CSV file:

$ hadoop fs -cat stricttyping/sample_copy_1.csv
1,Hello World,2014-10-04 12:00:00,439857.345
1,Hello World,2014-10-04 12:00:00,157.3457
1,Hello World,2014-10-04 12:00:00,48945797.3459845798475

In fact, many people deliberately don’t explicitly load the data into fixed type columns; they define the column types as strings and then import the date and ultimately ignore the real type until they have to parse or understand it for some reason.

Similarly, in NoSQL environments, the data types may really only be for explicitly representation requirements, and have no effect on the ability to either store or display and query the information. Even in a traditional RDBMS, there is no requirement to explicitly store certain values in certain column types, but certain operations may be limited. For example, most RDBMSs will not perform a SUM() operation on a string column.

The bottom line is that you will need to think about whether to explicitly make use of these columns because you need them as specific types in the target database, or whether to ignore them completely.

  • Strict transformations – Use strict datatypes when the information you want to store must be correctly interpreted within the target database, and it provides some form of performance advantage, unless doing so reduces the validity or precision of the information.
  • Relaxed transformations – Use relaxed transformations whenever the processing or target system does not support the required precision, or in those cases where the processing of the information is irrelevant. Most t ransfers to NoSQL and Big Data environments fit this model automatically.

With this options to you in mind, let’s look at some of the more specific types available.

Handling Numeric Values

Simple, plain, integers are supported by nearly all databases as explicit and identifiable types. Even document databases such as MongoDB and Couchbase understand the significance of a numeric value over a string representation.

However, if you are transferring big integers, be conscious of the limitations of the target database. Some environments explicitly support very large integers. Hive, for example, supports the BIGDECIMAL datatype, which holds numbers with up to 10 to the power of 308. Others do not.

Floating Point Issues

The biggest problem with floating point values is one of precision and storage capability. There are large variations between the supported types, how much is stored and how precise it can be. Further more, some databases specifically differentiate between decimal and floating point values and have different rules for how these should be represented and stored, and the two are not necessarily compatible

For floating-point values, the main issues are:

  • Representation – float values are generally displayed as a decimal value, for example:
  • There are no specific rules for this, but many values are based on the support of the operating systems own data type support. On modern 32-bit (and 64-bit) systems, floating-point values tend to have 7 digits of precision after the decimal point. This is due to the nature of the structure used to store and define them internally. A double has twice the precision, up to 15 or even 16 digits past the decimal point.
  • Parsing – these values properly is critical if you are storing the data; unfortunately rounding-errors, both made when the data is output, and when it is parsed back, are notoriously difficult, and not always religiously honoured.
    For this reason, some database explicitly support a DECIMAL type. Unlike the float, the DECIMAL type works more like two integers either side of the decimal.

Processing these values reliably, and storing them in a target database may lead to problems if the target system doesn’t support the datatype size, precision, or structure properly. Moving the data may lose the precision or content. On a simple movement of the data in an export/import environment might parse or store it correctly, or it may lose or truncate the precision entirely.

If you are replicating and regularly exchanging data from one database to the other and back again, these precision errors can build up to translate and convert a number from one value to one statistically significant.  If the double type within the databases environment does not support the complexity or precision of the values involved, consider using one of the big integer types and a suitable multiplier.

Finally, if the target database does not support the precision and detail that you need, consider moving the data using relaxed methods, for example by importing the data into a string, rather than a numerical type so that it can be reliabily stored.

Base-N Numbers

If you are exchanging numbers in other than base 10, for example, octal, hexadecimal, or others, ensure that the target database supports the required number format. If an explicit number format is not supported, either translate the number to decimal and handle the output and formatting of the data as the corresponding type within the target database and application, or use the relaxed method and keep it as a string.

Strings and Character Encoding

More problems are created by strings than you might think, and the most significant is usually the character set used to store, transfer, and import the data. Character sets used to refer to the difference between the byte-level encoding for things like EBCDIC and ASCII. Today, they span a much wider array of issues as the number of character sets and the use of a wider range of languages, characters, and ideographs increases.

The best way to encode strings when moving the data between databases is to use either UTF-8 (which encodes Unicode character in 8-bit bytes) or one of the high-bitrate encodings if your data requires it. For example, if you are specifically storing foreign-language, katana, or Chinese characters, using UTF-16 or UTF-32 may be more reliable, if not necessarily more efficient. UTF-8 can be used for a very wide range of different Unicode characters and is rarely a hindrance.

Also be aware that some databases identify character encoding capabilities and data types differently. For example, the VARCHAR2 type within Oracle can be used to store strings with an optional size (byte or character) declaration, but the NVARCHAR2 type is the Unicode (byte) sized datatype. The definition of the column and size can also be different. In Amazon RedShift for example, the size of VARCHAR column is defined in bytes, but in MySQL it’s defined in characters, so a VARCHAR(20) in MySQL has to be a VARCHAR(80) in RedShift. Sneaky.

A secondary issue is one of storage space. Different database environments support different representations of the different character storage types, and sometimes have wildly different performance characteristics for these different types.

Within a NoSQL or Big Data environments, the length (or size) of the string is rarely a problem, as they don’t have fixed or strict datatypes. However, for most RDBMS environments there are specific lengths and limits. Oracle supports only 4000 bytes in VARCHAR2 for example; MySQL supports 255 bytes in a CHAR, or 65535 bytes in a VARCHAR.

Finally, when transferring the information you may need to pay attention to any delimiters. Using CSV, for example, and using quotes to define the field limits only works when there aren’t quotes in the field content.

Dates and Times

Of all the different data types that we have covered up to now there have been problems with understanding and parsing the values because of differences in the types, format, or structure of the data, but all of them were largely covered within some simple limits and structure.

Dates are another problem entirely. In particular:

  • Date precision and format
  • Time precision and format
  • Dates or Epochs?
  • Time Zones

All go together to make for one of the most complicated of the all the types supported when transferring data, because there are so many times where it can go wrong.


Epoch values are those where the data is represented as an integer counting, usually, the seconds from a specific reference point in time, from which the current date can be calculated. For example, Unix-based Epoch times are represented as the number of seconds that have elapsed since Jan 1st 1970 at 00:00:00 (12:00am) GMT. Counting forward from this enabels you to represent a date. For example, the value:


Is in fact 12th October 2014.

There are two issues that arise from Epoch dates, time drift and date limits.

Time drift occurs if the date has been stored as an epoch that is relative to the current timezone. This can actually happen more frequently than you realize if dates are reconstituted back to an Epoch from a local time based balue into an Epoch. For example, some libraries that parse a date without an explicit timezone will assume that the date is within the current timezone of the system.

This is a particularly thorny problem when you realize that epochs have no timezones of their own. This means that the Epoch value:


Is 15:56 BST, but 07:56 PST. If you now transfer a PST-based epoch to GMT and then use it without conversion, all your times will be out by 8 hours. If you ran batch jobs on the imported data at 1am, that time would actually refer to a completely different day.

If you must use epoch values, ensure that you either know what the timezone was, or adjust the value so that it is against GMT and you can translate to the appropriate timezone when you need to. Also see the secion on timezones below.

The secondary problem is date limits. Traditionally epoch values were stored as 32-bit integers, which limits the date between 1970 and 2038. While this is fine for current times (at least for the next 24 years or so), for any future dates, this can be an issue.

If you are porting epoch values to a target that only supports 32-bit dates, and the date is beyond 2038, don’t transfer it using the epoch value, translate it into an explicit date that can be parsed and stored in whatever local format is required for the target environment. For example, within MySQL you can use the FROM_UNIXTIME() function to translate your epoch date into something more usable.

Date Formats

When transferring dates, use a format that is unambiguous and supported by the target system. Different locations and systems have different ways of representing dates, including the different seaprators that are used, and the different orders of the components. Even the use of the prefix for some numbers differs between regions. Some examples are shown in the table below.

Location/Format Example
USA Month.Day.Year
Japan Year-Month-Day
Europe Day.Month.Year
UK Day/Month/Year

Different locations and date formats

The best format to use is usually the ISO format:


With a zero prefix added to each value to pad it to the correct number of characters. For example, the the 1st of January:


Or the year 1:


The ISO format is not only readable on just about every single platform, it also has the advantage of being sortable both numerically and by ASCII code, making a practical way of exporting and loading data in date order without having to explicitly order data by dates.

Time Formats

Time is usually restricted to a fairly obvious format, that of:


Or in some regions and standards:


Aside from the timezone issue, which we will look at next, the other problem is the level of precision. Some databases do not support any precision beyond seconds. For example, within Oracle you can store precision for eseconds up to 9 decimal points. Amazon RedShift supports only 6 digits of precision.

Also be aware that some environments may not support explicit date and time types, but only a unified datetime or timestamp type. In this case, the structure can be even more limited. For example, within Amazon RedShift, the timestamp datatype is actually formatted as follows:


With the date in ISO format but without explicit date separators.

Time Zones

Every time represented everywhere is actually a time within a specific timezone, even if that timezone is UTC (Universal Time Coordination). The problem with timezones is that the timezone must either be explicitly stored, shared, and represented, or it should be stated or understood between the two systems that the time is within a specific known timezone. Problems occur either when the timezone is correctly represented, or assumptions are made.

For example, the following time:

2014-09-03 17:14:53

Looks clear enough. But if this has come from the BST (British Summer Time) timezone and gets imported into a database running in the IST (India Timezone) then you start to get the time stored in the wrong format if the timezone is not explicitly specified.

Another issue is when there are timezone differences when data is transferred, not because of the physical time difference, but because of the effect of daylight savings time. Transferring data from, say, GMT to PST is not a problem if you know the timezone. Transfer the data over during a daylight savings time change, and you can hit a problem. This is especially true for timezones that have different dates for when daylight savings time changes.

Finally, be prepared for databases that simply do not support the notion of timezones at all. To keep these databases in synchronization with other databases with which you might be sharing information, the easiest method is to use GMT.

In general, the easiest solution for all timezone related data is to store, transfer, and exchange the data using the UTC timezone and let the target database handle any translation to a localized timezone. If you need to explicitly record the timezone – perhaps because the data refers to a specific timezone as part of the day – then use the time type that supports it, or store a second field that contains the timezone information.

Compound Types

We’ve already looked at some of the issues in terms of the structural impact of compound types. Even if you have the ability to represent a value as a compound structure within your target data, you need to understand the limitations and impact of compound types, as not all systems are the same. Some of these limitations and effects are database specific, others are implementation specific.

For example, within MySQL and PostgreSQL, the ENUM type enables you to store a list of fixed string-like values that can be chosen from a fixed list. For example:


The benefit of this from a database perspective is that internally each string can be represented by a single number, but only reconstituted into the string during output. For targets that do not support it, therefore, the solution is to translate what was an ENUM column in MySQL into a string in the target database.

MySQL also supports the SET type, which is similar to ENUM, except that the value can refer to muiltiple options. For example:


The SET type enables you to record not only the specific day, but maybe a group of days, for example:

INSERT INTO table VALUES (‘Mon,Wed,Fri’)

Again, interally this information is represented this time as a BIT, and so the actual data is implied and displayed as a compound type.

When translated to a database that doesn’t support the type, you may either want to create an additional column for each value to store it, or, if you are using a document database, you may want the set firled converted to an array or hash of values:

    ‘Mon’ => 1,
    ‘Wed’ => 1,
    ‘Fri’ => 1

Always consider how the data was used and will be searched on either side of the database transfer. In an SQL RDBMS queries normally take the form:

SELECT * FROM TABLE where FIND_IN_SET(‘Mon’,days)>0;

That is, return all the values where the field contains the value ‘Mon’. In a database that supports searching or indexing on individual values (MongoDB, Couchbase), the key-based transfer, where we effectively set the member of a hash to a meaningless value so that we can do key-based lookups. We’ll examine this in more detail when we examine the rnvironments of these databases.

Serialized and Embedded Formats

For a whole variety of different reasons, some people store more complex formats into their database so that they can bmore easily be manipulated and used within the depper element sof their application.

For example, serializing an internal structure, for example, a Perl object or a Java object so that it can be stored into a field or BLOB within the database is a good way of making use of complex internal structures and still have the ability to store and manipulate the the more complex data within the application environment.

If all you want is to transfer these the serialized format from one database to another, then the basics are unlikely to change. You may need to use the binary translation methods in the next section to realisitically get the data over into the new database reliably, but otherwise, the transfer should be straightforward.

However, there is also the possibility that you want to be able to query or extract dta that may have been embedded into the serialized object.

In this case, you need to change the way that you use and manipulate the information as part of the data migration process. In this case, you may want to take the information and either expand the data to expose the new fields as transferrable data.

Or, you may more simply want to change the content of the information from its serialized format into a more universal format, such as JSON.

Binary and Native Values

Binary data, that is, data that is explicitly stored and represented in a binary format is difficult to process when moving data.

  • Binary means that single-character delimiters become useless, even control characters. 0x01 is just as likely to come up in binary data as it is when used as a field separator.
  • Pure, native, binary data suffers from the problems of ‘endianess’, that is, the byte order of the individual bytes. Test and numerical translations don’t tend to suffer from this because systems know how to parse text. When exchanging binary data, the endianness of the data applies.

Binary data can also be affected by any translation or migration process that is expecting a string representation of information. For example, it is not uncommon for UTF8 to be used when reading binary data, which leads to interpretation and storage problems.

In general, the best advice for true binary information is for the data to be encoded into one of the many forms of binary-to-hex translation formats. This can include solutions such as raw hex conversion, where the data is quite literally expanded to a two-character hex string for each binary byte. For example, we can translate any byte strinf into hex values with tools like Perl:

$ perl -e "print unpack('H*','Hello World')"

Or use uunencode:

begin 666 HelloWorld

Or use the MIME64 standard that is employed in many modern email and Web environments for transferring attachments, as it ensures that even multi-byte cahracters are effectively transferred.

All of these solutions can be easily processed on the other side back into the binary format according to the endianess of the host involved.

Data Migration: Moving the Actual Data

There are two key elements to the exchange of any information between databases. One is the data structure used for the exchange, and the other is the transformation required to reach those structures.

Some of these are driven by the source database, others by the target database. For example, when moving data from RDBMS to NoSQL database generally requires constructing documents from what might be tabular, or joined-tabular data. This may involve both join elements on the relational side, as well as formatting on the NoSQL side. The eventual aim is to ensure that the data reaches the target database in both the right format, and without corruption, and also in a format that is most appropriate or efficient. That ultimately depends on what you are using the transferred data for.

The other aspect is the difference between source and target data types – that is the format and construction of the individual fields or components of the data. Document databases and Big Data stores tend not to care about the data type, whereas RDBMS cannot live without them.

Some important considerations for how we use this information:

  • The data structure must be retained (i.e., we must be able to identify the fields, columns or other elements of the data).
  • The data format and integrity must be maintained (the data should not be corrupted, shortened or reduced in any way).
  • The data must be able to be efficiently transferred (sending a 1GB file that only contains 15KB of valid information is not efficient).

In this chapter we’ll examine some of the key differences and problems with transferring data that transcend the mechanics of the process, and how to deal with them effectively. Although the three primary aspects, basic formatting, structural comparisons and data type limitations are handled separately here, there are few occasions when you can truly treat these elements individually. We’ll see some examples of this as we go through.

Basic Interchange Formats

When you start to move data efficiently between the different database types that you exist you will find a number of different possible interchange formats, and the primary issue with all of them is exactly how efficiently, and more importantly accurately, they enable the information to be exchanged.

First, the data will need to have been encoded with some kind of encapsulation format. This format is what describes the individual structure, and is entirely dependent on the underlying data that is being exchanged. For example, if the data has a very rigid structure then that will obviously normally enforce the format of the information. Row-based data, for example, can normally be encoded using CSV or even a fixed-width record format.

The second aspect is the encoding and formatting of the information itself. Exchanging data using CSV is fine, providing that you can correctly identify the format of the text itself. Should it be encoded in UTF-8? Or UTF-32? Is plain ASCII better? What if it contains a mix of these characters, should UTF-8 be used as the standard and the actual encoding handled by the database target when the data is imported?

In fact, many of the principles about translating information between different databases also rely on basic best practice for how you design and structure the data in the target system to begin with. Normalisation of the data structure for information can normally be abpplied to any database, even those that might have a loose or undefined structure have conventions. It’s unlikely, for example, that you will call a recipe title field ‘title’ in one record and ‘recipename’ in another record of the same database because your application will be a mess.

Of course, there are times when you may be merging, combining or otherwise consolidating data from a wide variety of different documents, records or blocks of information. There it is up to you to ultimately pick a standardisation for it to be useful to you once it’s been moved into the target system.

As a rough guide for the types of operation and translation that might take place, the following table highlights the kind of structural transformation and changes you might need to make when moving between some of the most common database environments.

Table 2-1: Structural Mappings between database environments

RDBMS Columnar Store Document Database Freetext/unstructured data store
RDBMS Vendor specific only Vendor specific only Field mappings only Application specific
Columnar Store Vendor specific only Vendor specific only Field mappings only Application specific
Document Database Field mappings only Field mappings only Vendor specific only Application specific
Freetext/unstructured data store Application specific Application specific Application specific Application specific


  • Vendor specific only changes are those that are directly related to the capabilities of the source or target database. MySQL for example supports the ENUM and SET compound field types, whereas Oracle, PostgreSQL and SQL Server do not. Moving from one to the other may required changes.
  • Field mappings only refers to how you map the source fields or columns to the target fields/columns. Depending on the target this may include compound and/or JOIN based translation. For example, when moving from a document database to an RDBMS you might convert a compound field into a single field, or a lookup table. When translating from an RDBMS to a document store, the data might be combined using a JOIN into a single target field.
  • Application specific changes are those that will entirely depend on how you to use the information. Translating document data into freetext databases is unlikely to require any changes. But converting freetext info into an RDBMS format is going to require some significant identification and translation.

Let’s dig into some more of the specific challenges.

Row-Based Data

For row-based data, the information can generally represented and formatted as one of the regularly used and displayed formats, such as Character Separated Values (i.e. CSV), or in a fixed width format. Row-based data (which includes the column-based data used in big data stores) is probably one of the easiest formats of data to exchange. In nearly all cases the list of columns is usually pretty well fixed and the format of the data is well known because the columns are fixed.

Character Separated Values (not Comma-separated values) is one of the oldest methods of exchanging fixed format data like this, it was often used as the only available method for exchanging information in a reliable fashion. Historically most tabulated data like this tended to be financial information, and so the content and format of the information was relatively simple. As such, the most common format was to use carriage-returns (or the operating system equivalent, which could be newlines or carriage-return and newline characters) to separate the records, while the individual fields in each row of data were separated by a comma (hence Comma-Separated Values as the CSV).

For example:

1085,Creamy egg and leek special,,4,1,0,0,0.0,0.0,0
87,Chakchouka,A traditional Arabian and North African dish and often accompanied with slices of cooked meat                      ,4,1,0,0,0.0,0.0,0
347,Mozzarella and olive pizza,A very simple pizza base made without yeast topped with traditional Italian ingredients. Look out for a low fat type of Mozzarella or Cheddar cheese if a low fat diet is being followed.,4,1,0,0,0.0,0.0,0
720,Savoury pancakes,Basic pancake recipe. Allow 30 min standing time for batter.,8,1,0,0,0.0,0.0,0
477,Seafood filling for pancakes,,8,1,0,0,0.0,0.0,0

The problem with commas and carriage-return characters is that, as computers got more complex, and the data they stored got equally more complex, how do you determine between a comma in some text, and a comma separating a field? What if you transfer a text string that contains a newline or carriage return. You don’t want that interpreted as the end of the record if it happens to part of the field. The initial solution is to use some kind of further delimiter. For example, using double-quotes:

"1085","Creamy egg and leek special","","4","1","0","0","0.0","0.0","0"
"87","Chakchouka","A traditional Arabian and North African dish and often accompanied with slices of cooked meat                      ","4","1","0","0","0.0","0.0","0"
"347","Mozzarella and olive pizza","A very simple pizza base made without yeast topped with traditional Italian ingredients. Look out for a low fat type of Mozzarella or Cheddar cheese if a low fat diet is being followed.","4","1","0","0","0.0","0.0","0"
"720","Savoury pancakes","Basic pancake recipe. Allow 30 min standing time for batter.","8","1","0","0","0.0","0.0","0"
"477","Seafood filling for pancakes","","8","1","0","0","0.0","0.0","0"

This doesn’t fix the problem, it just diverts your attention for long enough to realize that now what happens if one of the delimiting characters needs to be used in the text? We could escape it, by prefixing it with a backslash:

"700","Garlic mushroom kebabs","The longer you leave these mushrooms to marinate, the better they will taste.\nGood for barbecue.","8","1","0","0","0.0","0.0","0"

But now we’re getting complex, both to read and write the information, the level of complexity is increasing to point of introducing further possible methods of corrupting the data as it gets transferred.

The alternative is to use a different delimiter that is unlikely to be used within the text in any form. Hadoop in fact follows this model, using the hex characters, 0x01 and 0x0A to delimit records and fields. As binary characters these are unlikely to be used in what is normally human-readable text. Of course, once you start transferring binary data, you need to find another method, such as hex-encoding binary data.

"700","Garlic mushroom kebabs",VGhlIGxvbmdlciB5b3UgbGVhdmUgdGhlc2UgbXVzaHJvb21zIHRvIG1hcmluYXRlLCB0aGUgYmV0dGVyIHRoZXkgd2lsbCB0YXN0ZS5cbkdvb2QgZm9yIGJhcmJlY3VlLgo=,"8","1","0","0","0.0","0.0","0"

The other alternative is to use a fixed width format. This has the advantage that providing you know the exact widths of the individual fields, encoding errors are eliminated because the characters are no longer significant in the format of the information.

The downside of the fixed-width format is that the size of the fields, records, and ultimately files, can become prohibitively large if you are exchanging potentially large or unlimited fields. For example, BLOB types in most databases can be MB or GB in size; expressing that in fixed width format is obviously not a practical solution.

Record-Based Data

Record based is information that may not necessarily be identifiable or resolvable by an easy to use row specific format or structure such as that used in CSV exchange. Complex table data, or information that that is made up of a combination of fixed fields and BLOB fields, for example, is unlikely to reliably, or efficiently, transferred. The problems of character and structural formats will ultimately make using that information difficult or computationally expensive when actively sharing the information – for example by making the file sizes too big to be practically exchanged.

A very typical example of record based information is either information from a document-based database, a free texrt database, or where the information that makes up the majority of the content is in fact really an attachment or noit inline field and database data. Think of an email message; the message, the address, from, subject are all examples of easily identifiable and classifiable database information. But what do you do with an attachment that might have been sent along with the recored?

How about documents generally? Metadata about those documents could be represented in a typical RDBMS row, but not the document itself. But the combination of the two – the metadata and the actual document together make up a ‘record’ that you may want to effectively share all or part of with another database.

When transferring record-based data, think first about what constitutes the record and how that can be represented in the different environments. Then move on to understand how the fields and individual data points can be translate into a format suitable for the target database. With record-based data, it may be that you have a massive volume of data and compound records that when move from a document store to a transactional RDBMS require 20, 30 or 200 rows of data to be represented properly; this is not a problem, providing you find a method for identifying all of the row data that refers to the record is handled correctly.

In general with a record based database the easiest approach is to actually translate the data at the source into something that can be imported directly into the target format. For example, from a record-based environment you can generate four different blocks of CSV import data, one for each table and portion of the source information.

The opposite is actually also true; when converting data from a column or table store into a record based format, it normally makes sense to do this on the basis of translating the key data into the new structure before doing the transfer. As a general rule, either use the native target format if you can, or make use of representable formats such as JSON to do the actual encapsulation of the information. Many record or document-based environments already use JSON, or a format similar to this.

    "title": "Fried chilli potatoes",
    "preptime": "5"
    "servings": "4",
    "totaltime": "10",
    "subtitle": "A new way with chips.",
    "cooktime": "5",
    "ingredients": [
            "ingredtext": "chilli powder",
            "ingredient": "chilli powder",
            "meastext": "3-6 tsp"
            "ingredtext": "potatoes, peeled and cut into wedges",
            "ingredient": "potatoes",
            "meastext": "900 g"
            "ingredtext": "vegetable oil for deep frying",
            "ingredient": "vegetable oil for deep frying",
            "meastext": ""

One final consideration is those situations where there is no structure – or the structure is so ephemeral or complex that there is no way to map information. You cannot, indeed should not, either impose a structure just for the sake of one, or inversely rely on sub-standard features in the target database just because it makes your life easier.

Some examples of this include trying to extract key fields or information from unstructured data that are complicated either to identify, or to map back to the original. Extracting a telephone number from a massive text string just because you can does not mean that the telephone number you have extracted is really the one that should be associated with this record in your database. Equally, relying on full-text searching engines within RDBMS environments can be problematic.

Is that a Column or a Field?

Not all fields and columns are created equal, and many of these difficulties come from the complexities or abilities of the database environment being used. Specifically, at which point do you treat a fragment of the data that you are dealing with as a column, or a field, or even just a uniquely identifiable piece of information?

As you move between different environments, the differences become more pronounced or more difficult to identify. True columnar stores, for example, tend to have a significantly reduced number of datatypes and support, and that often limits your ability to store certain values and information. For example, compound values, or specialist types, such as XML, GeoData and spatial points may be storable in one database but not another.

Consider this record, taken from a CouchDB (JSON document) database:

   "title" : "Chicken Curry",
   "Keywords" : [
   "id" : "8023754"

Now is the ‘Keywords’ compound object in the document a field, or is it a column? In MySQL we could translate this into a SET datatype, a special datatype, providing we knew what all the possible values for that column are. In Oracle, a field that has multiple possible values like this would normally either be split into separate columns as a bit or boolean value, or it would converted to a lookup table, as in the diagram below.


Depending on your use case, within a strict columnar environment such as Cassandra or HP Vertica you might actually consider going the other way and repeating the information with the keyword data in individual rows, like this:

dbadmin=> select * from recipes_kwbytext;
   id    |        title         |     kw
 8023754 | Chicken Curry        | Indian
 8023754 | Chicken Curry        | Chicken
 8023754 | Chicken Curry        | Okra
 8023754 | Chicken Curry        | Spicy
(4 rows)

With a column store this can be more efficient if what you are looking for is patterns in the data, because repeated data like this is easy to extract and identify. In this case, what we’ve done is convert something that is a compound field in a document store into multiple rows with the same ID in a column store. This solution can also be used in environments where there are no JOIN operations, or a JOIN is expensive, but where the information is still required at each level. Good examples here are many of the document stores and structureless environments such as Hadoop.

Can you Bypass Datatypes?

Another temptation when translating data between very different database formats is simply to ignore the formatting, and especially the very strict datatypes, that might normally define the data being stored.

This is particularly a problem within those environments where there may be a very limited set of datatypes to work with and can be application specific. For example, the Hive database environment within Hadoop is reqally little more than a thin veneer over a text format used to store the actual data. When you define a table within Hive and then select the rows from the table, Hive parses each row and uses that to display the value in the corresponding format.

This can cause problems for certain data, for example, numbers that are too big, dates that don’t match the very limited set of date formats supported by the Hive parser. In the long term, this causes corruption of the data that you have transferred.

For this reason, some people choose to create tables within Hive that use the Text datatype to display the information rather than the true underlying Integer or Floating Point value as it ensuires the raw value, not the interpreted value will be used.

The same process can be used when moving data; extract the ‘raw’ value rather than hope the source or target database will interpret, store and display the information in the right format.

If you are only sharing or displaying the information in the new target database then there is probably no reason to worry. If you start processing or actively using the data, this is where corruption can occur if you are not storing the information correctly. For example, if an integer is stored and then incremented, you want 10,000 to become 10,001, not 100001.

The bottom line, you can bypass the datatype, but probably shouldn’t if you hope to use the information in the format in which you’ve decided to store it. If you have a datatype, and can identify it from the source material, then use it if the target environment can handle and interpret it correctly. See the notes later in this chapter on limitations in different environments.

Optimization and Performance

Irrespective of the the database environment and the reason for you moving the data, the end goal should always be to move data into a target in a format that will be efficient to use at the target end.

In many document or unstructured systems, or those with very flexible storage mechanism such as Hadoop, the performance will often be predicated not on the structure of the information, but what information is closest to you, or what can be pre-mapped or organized through a map reduce or index generation exercise.

Conversely, RDBMS require highly structured and organized data structures both with and without indexing to provide the best performance. Columnar stores are often much more efficient if you can logically group or sort information together. Some will handle this automatically for you, otherwise are more efficient if you can pre-determine the distribution of the data on which you are most likely to sort and query on. That might mean that when you transfer the data, you sort the generated file by that column or columns before loading. In some extreme examples it may be that you load the data in an unordered format and then move again into a new table with the right column structure.

Don’t be afraid of making the wrong decision, because you can often sort this structure out during a secondary or tertiary stage, but equally don’t ignore it. Having to parse or process large bodies of data a second or third time will be impractical if you are sharing or replicating data compared to single, isolated, dumps.

Ensure Two-way Validity

Without good reason, you should always try and avoid making any kind of translation of format that cannot be either reversed, undone, or translated back into it’s original format, even if that might make the process a little more complicated. Remember that data is often a living organism when being actively used and employed. Therefore doing too much to format, combine, extract or otherwise manipulate the information can then make it difficult to be used again elsewhere.

Note that this is not about normalization. Normalization in typical database parlance means finding the right, fixed, database type for the field data, making it the most efficient choice, and understanding the limits and structure of the data so that you can decide whether a field should be 10 bytes or 12 bytes long. Doing this normally results in identifying the data structure, lookup tables, relations and joins so that you have the right structure. In this context, normalization is really about making the data look like a typical table structure in an RDBMS; normalization for document databases is entirely different. Normalisation for data interoperability is another level still, and we’ve already seen a number of different examples of that.

Instead, think about the quality of the data and how it should be used, while keeping in mind that the structure required for efficiency in an RDBMS may be completely different to the efficient storage of the same information in a document DB, or when resolved down to a text fragment in an unstructured data store.

To return to a well-trodden example, in the section ‘Is that a Column or a Field?’ we looked at the translation of compound values into single or multiple fields. Using a comma to separate the potential values means that we could split the value back out. If the field had been correctly translated either to boolean columns or a linked table is easier to translate back again into a whole variety of formats.

When representing a compound type, think about how you would reverse the structure so that it could be used the other way round. For example, if you decide to dump the information out to another table or column structure, make sure that you add identifiers so that you can track the record it came from (which you’ll probably need anyway), and can reformat it back into that format. If you’ve split it out into multiple rows in a columnar store, make sure you know how to combine it back and deduplicate the information again if you need to send it the other way.

The compound types are the most complex single field type here because there are so many opportunities for you to mess up the translation, but the same is also true for basic structural information, and even more so if you decide that you only want to transfer a smaller number of fields of data from one database to another. Either transfer everything, or transfer what you need and include information (like a unique identifier) so that you can associate it back with the data you extracted it from. Once you’ve lost the context of the information, it can be impossible to get it back and the result is a useless dataset.

Database Metadata

We’ve concentrated very heavily on the actualy data you are storing and want to work with, but what about metadata:

  • Sequences, current auto-increment values?
  • Whether you transfer or record these is going to depend on exactly how you want to use the information when it reaches your destination (or comes back).
  • How about the definition of the structure that you are using? Do you want to be able to share and use that? What happens when the structure changes. Do you want to track and identify those changes?
  • When doing a one-time export of information from one database to another you can be sure about the structure and what you expect to get from it. But what happens when you repeat the same export multiple times? Or when replicating?
  • If you are sharing data between two different systems and integrating them, knowing the sequence number may be irrelevant unless you can synchronize the generation of the number so that it can be used by the multiple databases in an effective manner. Perhaps your databases could use a better unique identification method, rather than relying on a monotonically increasing sequence number, such as UUIDs or using a central identifier registry?

How to address these different problems will be covered in later chapters, but it’s important to think about it here as it has a knock on effect to other areas. For example, when moving unstructured or document based databases into multiple separate tables, you need to identify and tie that information together, where a UUID is important, and it therefore becomes a critical part of the data structure that you swap.

Data Migration: Database Terms and Structures

In the previous post we looked at a number of different database types and solutions, and it should be clear that there are a huge range of different terms for the different entities that make up the database structure. All the different entities fit into one of four categories, and they have significance because when moving and migrating data you need to know the source and destination type and whether you should be creating a database for every document (bad) or a document for every record (good). The components can be described as shown in Figure 1-6.

Figure 1-6.png

Figure 1-6: Database Terms and Structures

Most databases support the notion of four different components:

  • Field – generally the smallest piece of addressable data within any database. However, not all databases identify information down to the field level. Others don’t even recognise fields at all.
  • Record – a group of fields, or, a single block of identifiable information. For example, your contact information is a record made of the fields that define your name, your address, and your email address. Some databases only support the notion of a block of information and don’t care what it contains, whether that is fields or a binary string of data. Records may also involve either a fixed set of fields, or a variable group.
  • Table – a group of records. Some databases assign a specific group of fields to a specific table. Others just use a table to hold or identify a collection of records with largely similar information. Some database types, such as NoSQL, do not support a table, but immediately jump from record to database.
  • Database – a group of tables. Not all databases support this additional level of organisation, and in fact it tends to be those that have a significant structure at the lower levels (field, record). The database is usually used in the role of multi-tenancy, that is, the ability to store a collection of data related to a single application.

Of course, the problem is that different databases apply and support these terms differently, many use different terms, and some may blur the lines between each term to such an extent that it is impossible to tell where the different elements exist.

Let’s explain this a little further by providing some explicit examples:

  • MySQL, Oracle database, IBM DB2, Microsoft SQL Server, Microsoft Access, and other relational databases tend to support all four levels with a very rigid structure in place, as you would expect from a structured RDBMS.
  • Memcached knows only records (values) identified by a supplied key, and those records have no fields.
  • CouchDB, MongoDB and Couchbase support different databases, and within those databases you have documents, which are logically similar to records. These documents have fields, but there is no requirement for the fields within each document to be the same from document to document. MongoDB also supports collections, which are akin to tables.
  • Hadoop in it’s bare Highly Distributed File System (HDFS) native structure doesn’t understand anything, although you can place files into different directories to mimic a structure. If you use a system on top of HDFS, such as Hive, HBase or Impala, you are normally implying a typical 4-level data architecture.

In general, the ability to identify different components within the database depends on the database type, and a summary of these is provided in the table below.

Database Fields Records Tables Databases
RDBMS Yes Yes Yes Yes
NewSQL Yes Yes Yes Yes
NoSQL Mostly Documents/Rows Maybe Yes
Key/Value Stores No Yes, by ID No Maybe
Unstructured No No No Maybe

Now let’s have a look at the specific example database solutions, including the term used for the corresponding value:

Database Type Database Fields Records Tables Databases
RDBMS Oracle Yes Yes Yes Yes
MySQL Yes Yes Yes Yes
PostgreSQL Yes Yes Yes Yes
NewSQL InfiniDB Yes Yes Yes Yes
TokuDB Yes Yes Yes Yes
NoSQL CouchDB Yes, embedded in JSON Documents No Yes
Couchbase Yes, embedded in JSON Documents No Buckets
MongoDB Yes, embedded in BSON Documents Collections Yes
Cassandra Implied in column family Yes, implied by key ID Implied in Column Family No
HBase Implied in columns Yes Implied in Column Family No
Key/Value Memcached No Yes, by key ID No Maybe
Redis Yes Yes, key/value pair No No
Riak Yes Yes Schema No
Unstructured Hadoop/HDFS No No No By HDFS directory
Hive Yes, if implied Yes, if implied Yes Yes


Although it wont be covered in this series to any significant degree, these different levels also tend to support one further distinction, and that is security. Different database solutions provide security at a variety of levels and some allow you to restrict access down to the record level. For all database systems where different databases are supported and their is some level of security or protection between them, these databases are called multi tenant databases.

As we start moving the data between databases, understanding the importance of these elements is critical. For example, when moving data from an RDBMS to Hadoop, the distinction of table or database may disappear, and the significance of individual records may be deliberately removed entirely to enable the information to be processed effectively.

In contrast, moving data from MongoDB into MySQL is easier because we can identify specific elements such as a database and a table. Where we start to become unstuck is that although documents contain a collection of fields, they may not contain the same fields across each document.

Homogeneous vs. Heterogeneous

The primary issue with exchanging information is whether you are moving data between homogeneous or heterogeneous databases. Homogeneous databases are those that are of the same type, for example, moving data from Oracle to MySQL; both are RDBMSs, both have databases, tables, records and fields, and therefore the complexity of moving data between the database is straightforward from a structural perspective. But the datatypes supported are not the same. What do you do about CLOB or RAW datatypes in Oracle when migrated to MySQL?

In a similar vein, the actual procedural process of moving data between database types is similarly affected. MongoDB and Couchbase, for example, support the same structure; JSON and BSON are largely identical, and although there are some differences, reading the data from MongoDB and writing it to Couchbase can be achieved with functions that are almost identical – get the document by it’s ID on MongoDB and set the document on Couchbase with the same ID.

Most RDBMSs can be accessed through SQL and front-ends like JDBC or ODBC, opening two connections and reading/writing are easy to do. Most support the SELECT INTO and LOAD DATA INFILE style SQL to export and import data in larger chunks. But in heterogeneous deployments the same tools are not always available. A quick, but not always accurate, description of these elements across different databases is shown in this table.

Issue Homogeneous Heterogeneous
Data structure No Yes
Data types Yes Yes
Data Loading No Yes
Data Usability Yes Yes

Defining the Problem

Now that we have a good grasp of the different databases, their abilities, and their differences, it is time to take a closer look at what we mean by moving and migrating data and the problems associated with this kind of operation. Now we can finally start to define the problem of exchanging data between different databases and how that process can be tackled and resolved.

All of the following aspects must be considered in entirety before you start to exchange data, but think about it logically and holistically – you have to decide how data will be formatted, how the data is going to look (structure), how the data physically going to be transferred, and finally how it is going to be used.

Altering the Format

All data is not created the same, or in the same format, and furthermore, not all data is supported or acknowledged. Within NoSQL, for example, there may be no datatypes other than string, so you need to consider how you are going to move the data to the right type and the right format without (unnecessarily) losing data. The main considerations are:

Differences in supported types – you may have to choose between migrating to the next nearest, or most appropriate type. NoSQL and all Big Data targets tend not to have strong datatypes, whereas RDBMS database have very strong typing. You must choose a type that is able to handle the data in the way you want, and be able to hold the size of the information being inserted. Large text data, for example, may be too long to fit in a CHAR or VARCHAR column, and may need to be inserted into a BLOB or RAW column.

Differences in type definitions – databases have different definitions of different types. For example, Amazon RedShift supports only 19 digits of precision for floating-point values, while MySQL supports up to 53. Dates and times are also typically represented different, with some only supporting an explicit date type, or supporting a combined date time, or supporting a time with heavily restricted precision. All these differences mean that you may wish to store values outside the given range as a different type; for example, storing dates or timestamps-point values and dates as strings so as not to lose data.

Differences in type interpretation – generally a difficult problem to resolve without extensive testing, some datatypes can be interpreted incorrectly when the data is moved into a target database. String encoding – for example ASCII and Unicode, or bit-specific fields can cause issues. Also timestamps which may be interpreted during import as being subject to time differences; for example, if you exported on a server using Pacific Standard Time (PST) but imported on a different database using Central European Standard Time (CEST).

These issues must be considered in entirety before you exchange data; getting it wrong could lead to incorrect, invalid, and even completely corrupt information.

Altering the Structure

It should be clear right now that there are differences in the structure of the different database types. What may not be clear is that there are more options available to you than a simple direct association from one type to another. Instead you must make sure that the data is exchanged in an effective manner appropriate the information that is being exchanged.

For certain combinations the structure may appear obvious, but there is always the possibility that you the structure and information can be more effectively organised. For example, when moving from an RDBMS to a document store, the first intention is simply to place the different tables and structure them as different documents within the target database. This is fine, but adds complications you may want to avoid when you come to use it. Instead, merging the different tables into one larger document with nested components may simplify the use of the data in the target application.

The same can be true in reverse, exploding a single document into multiple, related, tables. Alternatively, you may want to take advantage of specific functionality in the RDBMS, such as XML fields, sets, enums or even convert the information to embedded JSON or serialised language variables if that makes sense to your application.

Loading the Information

Physically transferring the information seems like the most mundane of the processes in the entire scheme of exchanging data between systems, but in actual fact, it is is less clear than you might think. We’ll look at this in more detail when examining specific examples and database exchange projects, but some upfront issues to consider:

Does the solution include a native bulk loading system. Some databases specifically support a method of importing data, whether larger or small. For example, in MySQL the LOAD DATA INFILE SQL statement can do this for you. Cassandra supports a COPY command in CQL, and various Hadoop interfaces such as HBase and Hive enable you to access CSV files directly without explicitly importing them.

Custom loading may be required if no built-in solution exists. This can take many forms, including writing your own, or if they are available using specialised tools like Tungsten Replicator or  Sqoop. The exact method is going to depend on the data exchange type, data size, and complexity of the load process.

Application loading can be used in those situations where the application is running and a different version or format of the information is used. For example, when caching with a NoSQL engine on top of an RDBMS, you might adapt your application to automatically generate the NoSQL record. Similarly, during a migration, you might configure your application to look in the new database, and if it doesn’t exist, load it from the old database and generate the new record.

Data sizes must be a consideration. It seems ridiculous in this age when disk sizes are so large, but database sizes can be huge too. A recent project I was involved in required migrating just under 150TB of information. Storing all of that data in one go would have a required a mammoth sized disk array before the data was loaded into a Hadoop/Hive database. There are solutions for moving and migrating such large volumes of data without it ever touching the disk and using up all that space.

Depending on your data exchange requirements, any, or all of these may be an issue you have to contend with.

Making the Data Usable

Exchanging data between systems is only any good if once there the data is usable. Nobody would consider releasing a physical book in the USA, and a digital book in France, and not translating it. The same is true of data. Exchanging the data between databases requires you to take these issues into account during the movement of the data; it’s no good just blindly copying the data over and hoping it will be usable.

To make the data usable the following aspects must be considered:

  • Data accessibility – we’ve already talked about the key structural translation that needs to take place, but you also need to think about the effect on elements such as searching and indexing. Certain indexing methods are more complex (and therefore computationally expensive) than others. Some are more efficient. Some database environments support a limited number, quantity or complexity of indexing and querying that can only be addressed if the format and structure of the data is correct to begin with.
  • Data validity – if you change the structure of the data, does that change the ability to validate or otherwise ensure the quality of the information? For example, moving from RDBMS to NoSQL you may lose the ability to single out duplicate entries for certain types and fragments of the dataset. Relational constraints are not enforced within non-relational databases. Data format differences may also present problems; in a NoSQL database, for example, the same strict database types, such as dates, times or numbers do not exist. How do you prevent an invalid date being inserted into a date column, or worse, a non-date value into a date column that would have been identified during a database write?
  • Application usability – if the data is moved, can you still access and update it in the same way? RDBMSs tend to be transactional, providing stability and support, NoSQL databases do not as a rule, particularly across multiple silos. If an invoice is updated, how do I guarantee that the customers account is also updated, especially if one operation, or the database itself, fails during the process?

These are some, but not all, of the issues you need to be aware of. Regardless of the actual method though, you want to actually use the data at the end, so don’t forget how you might query or index the data once it’s moved. Keep in mind that not all data moves require heavy consideration. If you are exporting the data to be loaded for a mail merge for example, the usability aspects may be minor compared to the format and quality of the information.

Data Migration: Understanding the Challenges

Data migration – that is, the practice of sharing and distributing information between databases – requires some very careful consideration. Are you moving the data permanently, temporarily, sharing it between applications? Do want to share all of it, some of it? Are you changing databases, or trying to move some data to access or use the data in a more efficient system?

Let’s start by looking at what we mean by a database, and what the myriad of different databases are out there.


Walk up to any person at an IT conference or gathering twenty five years ago and ask them to name a database most would have probably selected one of a couple of the available tools at the time. All of the databases would have been the same type. That type would have been some kind of fixed record database management system, along the lines of dBase III+ or Oracle.

These had some very specific layouts and formats – the record would have had a fixed size, based on fixed fields, often with fixed widths. The reasons for this were largely for technical reasons – the way to store data efficiently was in records of a fixed size. Each record was made up of fields, each with a fixed size. To read a record, you needed the definition and then just extracted the bytes, as shown in Figure 1-1.

Figure 1-1.png

Figure 1-1: Fixed Record and Field Sizes

To access a different record, you could ‘seek’ ahead in the file according to the size of the records, and the number of the record you wanted to update. For example, to read record number 15 you would skip forward by physically reading the bytes from a file at 14 x RECORDSIZE.bytes, reading RECORDSIZE bytes, and then extracting the field data using the known record structure. This meant that records were treated as one, big, long block of bytes, as shown here in Figure 1-2.

Figure 1-2.png

Figure 1-2: Fixed Records as a stream of data

In fact, this was a very simple data model that was (and still is) thoroughly practical – many young developers and programmers may well have created a database using this very model. It even works if you use indexes – you can point directly to a record using the same system.

It may surprise you to know that for some databases this is still the fundamental model at the lower levels, although there may be some additional complexities and features. But over those same 25 years some other things have changed in two different directions, data formats, and data diversity. Those two have lead to a level of complexity in terms of the database systems that manage.

Although it may be useful to understand these low-level data formats about how the data is actually physically stored by the database, the focus of this series is one level higher. We want to consider how the data is structured, fields, records, documents, and also about the formatting and character structures and information, and finally how the entire database appears and is usable within your chosen database system. More importantly, we want to know how to move it all elsewhere. Before we get there, let’s look at the top level, database types.

Database Types

My earliest database – at age eight – was one that I built to catalogue my book collection using my Sinclair ZX81, with the software written entirely in BASIC. By the time I was 13 I had started to build custom applications using dBase III+ to manage my fathers accounts. When I left college, my first job was to move data, first from an old Digital Unix system to the new Sun Solaris 2 using the same database, and then from that database engine called BRS/Search, to Oracle. BRS/Search was a completely free-form database.

The aim of this process was to move that free-form store into a structured format – Oracle, an RDBMS – and to access it using a front-end built using a Macintosh specific RDBMS engine called 4th Dimension. In the background, we also started putting different classes of data into the then-brand-new Macintosh specific database called Filemaker.

Since those early days I’ve worked with (and on) PostgreSQL, MySQL, Oracle, Microsoft SQL Server, Microsoft Access, CouchDB, Berkeley DB, SQLite, Couchbase, MongoDB, Cassandra, DB2, and most recently Hadoop, to name just a few. They all have different characteristics – this is the primary reason they exist at all, in fact – and capturing the essential essence of each group of databases is our first step on the road to understanding how to move data between these databases.

The point here is not that I’ve got experience of (although hopefully that helps explain the reason and experience behind the content here), but instead, to demonstrate that there is a huge array of choice out there today. They all have different parameters, different methods of storing data, different supported formats, and a huge array of methods for reading, querying and extracting the information.

But what exactly moves a collection of data from just that – a string of bytes – into a database? And how does affect how we move data between them? Let’s look at some basic database principles. This will not be new information, but they are vital concepts to understand so that we can translate and refer to these elements through the rest of the series.

Database Principles

What is a database?

That is not an innocent question, and the answer depends entirely on the database system, type and individual solution before you can really provide an answer.

However, it can be summed up in two sentences:

A database enables the storage of individual, addressable blocks of information to be stored efficiently. These blocks can also be retrieved and potentially searched and indexed to enable the information to be effectively retrieved.

Whenever you look at a database and how to store, retrieve and update the information, you need to consider how the information within the database is accessed.

All databases share the same basic principles when it comes to working with the information itself, they must all share the following functionality referred to as CRUD; Create, Read, Update, Delete:

  • Create – data must be able to be created within the database, and this can be done on record or block basis, or in a batch mode where data is created in bulk.
  • Read – data must be able to be read back out. By their very nature, all databases must be able to do this on a selective basis, either by record, or by a group of records. More complex databases enable you to achieve this more selectively, for example, by selecting all of cars that are blue, or all the invoices raised for Acme Inc.
  • Update – data must be able to be updated. Again, as with reading, this must be possible on a record by record basis. Updates may also involve bulk modification of multiple records and even multiple fields simultaneously.
  • Delete – data must be deletable or removable on a record by record basis, involving either single or multiple records simultaneously.

Understanding the significance of these different operations within different databases is important to getting the movement and migration of information correct. Some databases can, by design, only support certain levels of these operations. Some provide implicit and explicit deletion of records, and others may deliberately not support update operations.

To further complicate matters, performance should always be a consideration for certain types of data migration. Most analytical and data warehouse platforms benefit from large, batched, or combined updates. Hadoop, for example, works badly with a large number of small files, because these cannot easily be distributed across the cluster. Hadoop is also, by design, an append-only system, which means updates are more complex to handle.

Contrast this with Memcached, where bulk writes or updates are supported, but where for reasons of cache efficiency you do not want large batches of data to be updated simultaneously as it would invalidate large portions of the cache.

Data Formats

Different databases store and structure information differently. Some use records, some use fields, some use documents. Some expect data to be highly structured, where a single ‘database’ may consist of tens, hundreds or even thousands of different tables for different pieces and types of information. At the opposite end of the scale, some just have a record with no further classification or identification.

These principles and how to migrate between them will be discussed throughout the series, but some general principles about the different structures and how to move between them will be examined in closer detail in a future post, when we look at Data Mapping and Transformations.


Depending on the database in use, different databases may use or enforce specific datatypes on the data that is stored. For example, there may be both character (string) and numeric datatypes.Although it is possible to store numeric information into a string column, there are often benefits to the numerical identity, including more efficient storage (and therefore faster operation), and the ability to run or perform specific operations, such as a SUM() or AVERAGE() function on a numeric column without having to translate each individual string into an integer or floating-point value.

Datatypes and their identification and translation are a major focus of a future post on  Data Mapping and Transformations.


All databases are predicated on the need to access the information within them very quickly. Consider a simple contact database with just 20 records in it. To look for the record with the name ‘MC Brown’ in it requires us to look at every record until we find the matching one. Of course, there may be more than one such record, so even if we find that the first record matches, we still have to iterate over 20 records to find all the matching entries.

With 20 records this isn’t a problem, with 20,000,000 records this is inefficient. Indexes bridge the gap by allowing the database to be addressed more efficiently. There are different algorithms for creating indexes that are beyond the scope of this text, but in all cases, the role of the index is to provide quicker access to information than could be achieved through a sequential sort.

Database Types

There are a myriad of different ways in which you can identify and classify different databases, and the dissection mechanism depends on what aspect of the database you are looking at. For example, SQL was for a long time associated exclusively with structured RDBMS engines, but has now become a data interface standard of it’s own and is used in both RDBMS and non-RDBMS environments. For the purposes of our understanding, we’ll examine them according to how they organise and classify their data.

Through the rest of this series, we concentrate on three major types, the RDBMS, NoSQL and Big Data.

Structured and Relational Database Management Systems (RDBMS)

Examples: Oracle, MySQL, PostgreSQL, Microsoft SQL Server, Microsoft Access, Filemaker Pro

Most structured database systems tend to have a relational database core (RDBMS), and most often, but not always, are interacted through the Structured Query Language (SQL). When talking to people about any databases, an RDBMS and SQL is what people will think of first, because it matches the idea of a strict database and types. The highly structured and rigid nature requires a rigid method of storing and retrieving information. It also places limitations and rigidity to your database types and structure. A simple layout is shown in Figure 1-3.

Figure 1-3.png

Figure 1-3: A structured RDBMS table diagram

Structured databases have a few specific characteristics:

  • Strict data structure – data is stored within fixed named silos (databases), within named tables, and with each table having a fixed number of named columns. Every single record within each table has the same number of fields (columns), and each column is used for a specific purpose or piece of information.
  • Strict data types – for example, an RDBMS will store integers and floats differently, and may have additional data types designed to provide fast access to specific information, for example, the SET and ENUM types within MySQL.
  • Data Definition Language (DDL) – related to the elements above, the DDL within any database is important because it provides a reference structure which can be used to replicate that structure in other database. Depending on the database system, the DDL may either be implicit in the way the data is accessed or stored, or in the API and interfaces provides, or the DDL could be more explicit, as in the dialects in SQL and similar statement-based interfaces.
  • Data manipulation language (DML) – Typically, but not always, SQL. The DML enables you to perform the correct CRUD operations to enable the information to be managed. Like DDL, the exact interface is very database specific. Some databases and systems rely entirely on a statement based language like SQL, which has it’s own dialects and structures for performing the updates. Others rely entirely on the API that interfaces between client applications and the database storage.
  • Relational capability – because the data is in a fixed format and with fixed types, it is possible to create specific relations between the field in one table with the field in other tables. This enables the data to be JOINed together to provide a unified output. For example, if you have orders and invoices, it’s possible to link the order and the invoice by a unique ID, and the database can either use or explicitly enforce the relationship. Joins are actually further characterised by their type, enabling many-to-one relationships (for example, multiple invoices relating to one client), one-to-many relationships (one invoice number referring to multiple invoice lines) and one-to-one (invoice to payment received).
  • Constraints and Indexes – constraints enable data to be created within a limited subset, or to identify rows uniquely. For example, a primary key constraint can force the table to create new records only with a new unique identifier. Indexes are used to create efficient methods for looking up and identifying data according to criteria. Within an RDBMS indexes are generally used to speed up access on a specific column, or multiple columns, to improve the speed of access during specific queries. Without an index, the RDBMS will default to performing a full table scan.

Structured/RDBMS solutions provide some of the easiest methods for exchanging data – it is generally easier to move data from a structure store to elsewhere. However, most destination databases do not have support the same range of indexes. Conversely, moving data from unstructured databases of any kind into Structured/RDBMS because you have to decide what goes where.

NewSQL Databases

Examples: Clustrix, VoltDB, InfiniDB, TokuDB

Traditional RDBMS and SQL databases are designed to run on a single machine. This has performance and hardware limitation issues. There is only so much memory and hard disk space that can be installed in a single machine, and if your database or performance requirements are high enough, a single server is not the solution. There are strategies, such as sharding the database (specifically splitting it up by an identifiable key, such as ID, name or geographical location), or more specifically dividing the database across machines, but these place a different load on your application layer, and are beyond the scope of this book.

NewSQL databases are a modification of the Structured/RDBMS that use multiple machines in a cluster to support the database requirements. Unlike the sharding and other methods, NewSQL solutions automatically distribute the load across the machines and handle the interface, indexing and querying required to access the data.

The main elements of the database and structure, such as databases, records and fields, and all other data migration considerations are the same as for traditional RDBMS environments.

NoSQL/Document Databases

Examples: Couchbase, CouchDB, MongoDB, Cassandra, HBase

NoSQL databases actually span a wide range of different databases, originally classified by their rejection of SQL as the DDL and DML language of choice, more usually resorting to the use of a direct API for accessing information. There was a resurgence of these different solutions in the early 2000s as people sought alternatives that were faster and simpler than the transactional RDBMS for web applications and websites.

Most NoSQL databases rely on simpler methods for accessing the information, for example by using a single document ID to retrieve a record of information. This document ID could be extracted from the users email address, so when a user logs in or register on a website, the document associated with that email address is accessed, rather than ‘looking-up’ the record in a larger table of user records.

NoSQL databases of this type can be roughly split into two groups, the columnar/tabular databases, and the document databases. The columnar/tabular type include Cassandra, Apache Hbase (part of Hadoop), and Google’s BigTable. Data is organised through an identifiable row ID, and a collection of associated column IDs that classify the data structure. They can look, and even act and operate in a similar fashion to the structured RDBMS table/row/column structure. A sample column style database (in this case Cassandra) looks roughly like that in Figure 1-4.

Figure 1-4.png

Figure 1-4: A columnar (Cassandra) database structure

Document databases are completely different. Unlike the table structure, data is instead organised into a document, usually using JSON or a JSON-like structure. Unlike the table structure, a document often combines different fragments of information together – for example, a contact record may store all the phone numbers, email addresses and other components within the single document for a given person. Documents, especially JSON based documents, are also very flexible and consist of fields that are nested, such as an array of phone numbers, or even entire nested structures, such as the individual rows (qty, product id, description, price) for an invoice or order, all encapsulated into a single document. A simple document database structure can be seen in Figure 1-5.

Figure 1-5.png

Figure 1-5: Document Databases

Perhaps most importantly, documents in a document database do not need to be identical. In a structured RDBMS environment, every record contains every field, even if the field is not actually used for that record. In a document database, different documents, even if within the same database or group may have only one field, or may have 20. The variable nature makes them appealing for this very reason, but represents an area of complexity when migrating information.

Most NoSQL systems have no idea of an explicit relation or join – this is often one of the aspects that makes the system faster. However, the lack of this element means that different techniques are required to store and interact with complex data.

Depending on the NoSQL solution, you may or may not have access to an index or quicker method of accessing the data. In CouchDB and Couchbase, for example, the fields of a document can be used to generate an index that provides quick searching and retrieval of information.

NoSQL databases can be easy to interact and migrate data to and from, providing there is (or isn’t) a strict schema, accordingly. For example, moving from an RDBMS to a document-based NoSQL database can be a case of converting the table records into documents identified by the primary key. It can also pay off in the long term to perform a more concerted conversion and translation of the source tables into unified documents.

Key/value (KV) Stores

Examples: Memcached, Redis, Riak

For most global declarations, key/value stores are treated as NoSQL, but I’ve split them out here because they have some interesting attributes that affect data exchange. A key/value store is exactly what it sounds like. A single blob of data (the value) is stored against a given key identifier. You store the information by giving the key, and retrieve the information by giving the same key. In most cases, the information can only be retrieved if you know the key. Iteration over the stored data, or indexes, are generally not available.

The roots of the key/value store go back to the attempt to speed up access to data where a given identifier is known, such as user id or email address. The best known key/value store is probably memcached which was originally developed to make use of the spare RAM of machines supporting a website (LiveJournal, a blogging platform) and enable fast access to blog entries. Since the ID of the blog could be derived from the URL being accessed, the entry could easily be looked up in memcached. If it didn’t exist, it was looked up from a MySQL database, and the formatted/retrieved version placed into the cache with the identifying URL.

Most document databases are really a modification of the key/value store. The value portion can be any data you like, from a simple string, through to a serialised object from C, Java or other languages, or a JSON document. In fact, some databases actually support both, and the only distinction between a key/value store and a document database is whether the database engine itself can identify and interact with the embedded structure. MongoDB and Couchbase, for example, have this distinction; MongoDB enables the database engine to update fields within the BSON (JSON-like) values, while Couchbase supports indexing of the JSON fields.

Key/Value stores are some of the harder databases to migrate and move data between. The lack of a structure, or the custom nature (for example a serialised language object), and the requirement to identify the record by a specific ID make exchanging data more complex.

Big Data (aka Unstructured, Semi-structured and Implied Structure Databases)

Examples: Hadoop, Apache Solr, ElasticSearch, Lucene

BRS/Search was, for the time and technology, relatively ground breaking in that it was a full-text retrieval system. Today we would probably classify this as a ‘document’ based database, that is, one that has a structured format, although the power behind BRS/Search was the ability to perform a free-text search across an entire collection.

Today, we generally referred to these types of database as unstructured, that is, there is no discernible format or structure to the information. Although there are many different examples of this, probably the best known today is Hadoop. Without getting into the functionality or history of Hadoop, the power of Hadoop comes from it’s ability to distribute the raw data and also to process and extract usable information from the unstructured data into something usable.

Within Hadoop, the normal workflow is to load Hadoop with raw data, for example, the text from tweets, or web-pages, and then use that information to build an index or data structure around the information so that it can be analysed or searched. Solutions such as Solr, Lucene and ElasticSearch work in similar ways, accessing the raw text and either indexing it so that the data can be indexed and searched, or using the structure that is available to provide searching and indexing by a more specific area.

This is an example where ‘semi-structured’ data applies. Twitter data for example consists of the twitter name, the tweet itself, and any tags or twitter users the tweet was directed to. The fixed fields and the tweet go together to make it semi-structured, as it consists of both structured and free-form information.

Implied structure databases are those where the structure of the data is implied by the database, even though the underlying data may only be partially structured and described. Apache Hive, part of Hadoop, is an example of this. Hive can natively read text files and interpret them with a specific structure, converting CSV files into columns so that they can be queried by HiveQL, a simplified form of SQL. Hive can also parse more complex data, including CSV that embeds JSON and serialised data structures, all so they can be queried through a familiar interface.

However, unlike a true RDBMS, Hive only interprets the underlying format, and it performs this interpretation every time the data is accessed. At no time does the data have to be translated into Hive format (nor, really, is there one), and no indexes are created to enable quick access to the data.

All of these individual types are wrapped up into what I’ve classed as ‘Big Data’. This is not to say that the data needs to be of specific size or complexity, only that it may consist of structured, unstructured, or all variants in between.

Moving data to and from unstructured, semi-structured, and implied structure databases entirely depends on what the information is, what structure is available, and how that structure can be used (or ignored) accordingly.

Data Mining in a Document World

As databases evolve, learning how to get the best out of the different solutions out there is the key to understanding and extracting the data in the way you need from your required data store. Document databases, like MongoDB, CouchDB, Couchbase Server and many others provide a completely different model and set of problems for interfacing and extracting data.

You need to be able to understand your structure, how you can query the information, and how to perform different data mining techniques on what is very obviously a completely different structure of information.

In this article, I try to take you through the basics of data mining when using a document database.

Read: Data mining in a document world

Data Mining Techniques

I have a new article on the basics of data mining techniques so that you can better understand some of the key principles behind the different methods and principles of data mining. 

From the abstract:

Many different data mining, query model, processing model, and data collection techniques are available. Which one do you use to mine your data, and which one can you use in combination with your existing software and infrastructure? Examine different data mining and analytics techniques and solutions, and learn how to build them using existing software and installations. Explore the different data mining tools that are available, and learn how to determine whether the size and complexity of your information might result in processing and storage complexities, and what to do.

Read: Data Mining Techniques

The Technology Behind Couchbase

Couchbase Server is one product, but it’s made up of a combination of different components that work together in order to produce server product, including memcached and the spidermonkey JavaScript engine, and more recently the CouchDB engine for storage and index creation. 

An article covering more of the detail is available here: