Extract, transform, load
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"ETL" redirects here. For other uses, see ETL (disambiguation).
Extract, transform, and load (ETL) is a process in data warehousing that involves
- extracting data from outside sources,
- transforming it to fit business needs, and ultimately
- loading it into the data warehouse.
ETL is important, as it is the way data actually gets loaded into the warehouse. This article assumes that data is always loaded into a data warehouse, whereas the term ETL can in fact refer to a process that loads any database.
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[edit] Extract
The first part of an ETL process is to extract the data from the source systems. Most data warehousing projects consolidate data from different source systems. Each separate system may also use a different data organization / format. Common data source formats are relational databases and flat files, but may include non-relational database structures such as IMS or other data structures such as VSAM or ISAM. Extraction converts the data into a format for transformation processing.
[edit] Transform
The transform phase applies a series of rules or functions to the extracted data to derive the data to be loaded. Some data sources will require very little manipulation of data. However, in other cases any combination of the following transformations types may be required:
- Selecting only certain columns to load (or, if you prefer, null columns not to load)
- Translating coded values (e.g., if the source system stores M for male and F for female but the warehouse stores 1 for male and 2 for female)
- Encoding free-form values (e.g., mapping "Male" and "M" and "Mr" onto 1)
- Deriving a new calculated value (e.g., sale_amount = qty * unit_price)
- Joining together data from multiple sources (e.g., lookup, merge, etc.)
- Summarizing multiple rows of data (e.g., total sales for each region)
- Generating surrogate key values
- Transposing or pivoting (turning multiple columns into multiple rows or vice versa)
[edit] Load
The load phase loads the data into the data warehouse. Depending on the requirements of the organization, this process ranges widely. Some data warehouses merely overwrite old information with new data. More complex systems can maintain a history and audit trail of all changes to the data.
[edit] Challenges
ETL processes can be quite complex, and significant operational problems can occur with improperly designed ETL systems.
The range of data values or data quality in an operational system may be outside the expectations of designers at the time validation and transformation rules are specified. Data profiling of a source during data analysis is recommended to identify the data conditions that will need to be managed by transform rules specifications.
The scalability of an ETL system across the lifetime of its usage needs to be established during analysis. This includes understanding the volumes of data that will have to be processed within Service Level Agreements. The time available to extract from source systems may change, which may mean the same amount of data may have to be processed in less time. Some ETL systems have to scale to process terabytes of data to update data warehouses with tens of terabytes of data. Increasing volumes of data may require designs that can scale from daily batch to intra-day micro-batch to integration with message queues for continuous transformation and update.
A recent development in ETL software is the implementation of parallel processing. This has enabled a number of methods to improve overall performance of ETL processes when dealing with large volumes of data.
There are 3 main types of parallelisms as implemented in ETL applications:
- Data: By splitting a single sequential file into smaller data files to provide parallel access.
- Pipeline: Allowing the simultaneous running of several components on the same data stream. E.g. performing step 2: lookup a value on record 1 at the same time as step 1: add two fields together is performed on record 2.
- Component: The simultaneous running of multiple processes on different data streams in the same job. E.g. doing a sort on input file 1 at the same time that the contents of input file 2 are deduped.
All three types of parallelisms are usually combined in a single job.
An additional difficulty is making sure the data being uploaded is relatively consistent. Since multiple source databases all have different update cycles (some may be updated every few minutes, while others may take days or weeks), an ETL system may be required to hold back certain data until all sources are synchronized. Likewise, where a warehouse may have to be reconciled to the contents in a source system or with the general ledger establishing synchronization and reconciliation points is necessary.
[edit] Tools
While an ETL process can be created using almost any programming language, creating them from scratch is quite complex. Increasingly, companies are buying ETL tools to help in the creation of ETL processes.
A good ETL tool must be able to communicate with the many different relational databases and read the various file formats used throughout an organization. ETL tools have started to migrate into Enterprise Application Integration, or even Enterprise Service Bus, systems that now cover much more than just the extraction, transformation and loading of data. Many ETL vendors now have data profiling, data quality and metadata capabilities.
