Extract, transform, and load (ETL) is a term used to describe the movement and transformation of data between systems involving high data volumes and complex business rules. Enterprise databases typically merge data from many different sources that have a plethora of formats and purposes. ETL software brings all the data together in a standard, homogeneous environment.
ETL processes are especially important in today’s growing big data world. Collecting and storing terabytes of data is useless unless it can be leveraged in a meaningful way. ETL provides data quality and profiling to ensure the trustworthiness of data, and transforms it so it can be used for business intelligence.
Automated ETL tools are widely used in data integration, data migration, and master data management projects and are critical for data warehouses, business intelligence systems, and big data platforms because they can be used to retrieve data from operational systems and process it for further analysis by reporting and analytics tools.
The reliability and timeliness of the entire business intelligence platform depend on ETL processes.
Benefits of ETL Software
Efficiency - Reusable components that can be automated to perform data movements jobs on a regular basis
Performance - Supports massive parallel processing for large data volumes
Development - Because only relevant data is extracted and processed, development time is reduced and because only targeted data is loaded, the warehouse only contains relevant data
Hardware - The reduced data in the data warehouse requires less storage memory
Administration – Reduced data also requires less security overhead
ROI - Designed to be very efficient, scalable, and maintainable
The first part of an ETL process is to extract the data from the source system. In this stage the data is converted into a single format to prepare it for the transformation stage. Extracting data correctly is critical since it is the foundation for the rest of the ETL process, and, if not done correctly, can result in failure of the entire project. Because most ETL tools consolidate data from multiple sources/systems, it can be a challenge to integrate data that is often in disparate formats.
This stage of extract, transform, load applies a series of rules or functions to the extracted data to transform it into the finished product that will be loaded into the destination. This involves cleaning, applying business rules, checking for data integrity, etc. Some data requires no transformation, but often one or more transformations may be required to meet the business and technical needs of destination, including joining data, transposing data, disaggregation, lookup, and simple or complex validations.
• Build an enterprise data warehouse, departmental data mart, or operations data source
• Easily accommodate assorted data sources and massive volumes
• Leverage a flexible batch window that includes scheduled batch, near-real-time integration, and real-time integration
• Built-in tools for dimension table management, lookup caching, and data profiling
• Load data incrementally with the advanced change data capture functionality
• Built in advanced transformations for efficiently processing complex data
• Robust parallel processing engine handles large volumes quickly and efficiently
• Sophisticated job scheduling and management capabilities
• Easy-to-use interface enables business users to be productive, lowering reliance on IT resources
The load phase of an ETL application moves the cleaned and transformed data into the destination, usually a data warehouse, data mart, or operational data store. This process varies widely depending on the requirements of the organization. Some data warehouses update existing information with the extracted data on a daily, weekly, or monthly basis. Other data warehouses may add new data in an historical form at regular intervals—for example, hourly. The timing and scope for replacing or appending data are choices made depending on the time and resources available, as well as business needs.