Extract, transform, load

Extract, Transform, and Load (ETL) is a process in data warehousing that involves

  • extracting data from outside sources,
  • transforming it to fit business needs (which can include quality levels), and ultimately
  • loading it into the end target, i.e. 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. ETL can also be used for the integration with legacy systems. Usually ETL implementations store an audit trail on positive and negative process runs. In almost all designs, this audit trail is not at the level of granularity which would allow to reproduce the ETL's result if the raw data were not available.


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 Information Management System (IMS) or other data structures such as Virtual Storage Access Method (VSAM) or Indexed Sequential Access Method (ISAM), or even fetching from outside sources such as web spidering or screen-scraping. Extraction converts the data into a format for transformation processing.

An intrinsic part of the extraction is the parsing of extracted data, resulting in a check if the data meets an expected pattern or structure. If not, the data is rejected entirely.


The transform stage applies to a series of rules or functions to the extracted data from the source to derive the data to be loaded to the end target. Some data sources will require very little or even no manipulation of data. In other cases, one or more of the following transformations types to meet the business and technical needs of the end target may be required:

  • Selecting only certain columns to load (or selecting null columns not to load)
  • Translating coded values (e.g., if the source system stores 1 for male and 2 for female, but the warehouse stores M for male and F for female), this is called automated data cleansing; no manual cleansing occurs during ETL
  • Encoding free-form values (e.g., mapping "Male" to "1" and "Mr" to M)
  • 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 store, and for each region)
  • Generating surrogate key values
  • Transposing or pivoting (turning multiple columns into multiple rows or vice versa)
  • Splitting a column into multiple columns (e.g., putting a comma-separated list specified as a string in one column as individual values in different columns)
  • Applying any form of simple or complex data validation; if failed, a full, partial or no rejection of the data, and thus no, partial or all the data is handed over to the next step, depending on the rule design and exception handling. Most of the above transformations itself might result in an exception, e.g. when a code-translation parses an unknown code in the extracted data.


The load phase loads the data into the end target, usually being the data warehouse (DW). Depending on the requirements of the organization, this process ranges widely. Some data warehouses might weekly overwrite existing information with cumulative, updated data, while other DW (or even other parts of the same DW) might add new data in a historized form, e.g. hourly. The timing and scope to replace or append are strategic design choices dependent on the time available and the business needs. More complex systems can maintain a history and audit trail of all changes to the data loaded in the DW.

As the load phase interacts with a database, the constraints defined in the database schema as well as in triggers activated upon data load apply (e.g. uniqueness, referential integrity, mandatory fields), which also contribute to the overall data quality performance of the ETL process.


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. This will lead to an amendment of validation rules explicitly and implicitly implemented in the ETL process.

DW are typically fed asynchronously by a variety of sources which all serve a different purpose, resulting in e.g. different reference data. ETL is a key process to bring heterogeneous and asynchronous source extracts to a homogeneous environment.

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 or real-time change data capture for continuous transformation and update.

Parallel processing

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. An example would be looking up a value on record 1 at the same time as adding together two fields on record 2.Component: The simultaneous running of multiple processes on different data streams in the same job. Sorting one input file while performing a de-duplication on another file would be an example of component parallelism.

All three types of parallelism 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.


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.

By using an established ETL framework, you are more likely to end up with better connectivity and scalability. 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.

Open-source ETL frameworks

See also


  • Kimball, Ralph; Joe Caserta (2004). The Data Warehouse ETL Toolkit. Wiley.
  • Kimball, Ralph; Margy Ross (2002). The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling. 2nd edition, Wiley.
  • Kimball, Ralph; et al. (1998). The Data Warehouse Lifecycle Toolkit. Wiley.

External links


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