ETL (Extract, Transform, Load)
ETL (Extract, Transform, Load)
What is ETL?
ETL, which stands for extract, transform and load, is a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system.
As the databases grew in popularity in the 1970s, ETL was introduced as a process for integrating and loading data for computation and analysis, eventually becoming the primary method to process data for data warehousing projects.
ETL provides the foundation for data analytics and machine learning workstreams. Through a series of business rules, ETL cleanses and organizes data in a way which addresses specific business intelligence needs, like monthly reporting, but it can also tackle more advanced analytics, which can improve back-end processes or end user experiences. ETL is often used by an organization to:
- Extract data from legacy systems
- Cleanse the data to improve data quality and establish consistency
- Load data into a target database
ETL vs ELT
The most obvious difference between ETL and ELT is the difference in order of operations. ELT copies or exports the data from the source locations, but instead of loading it to a staging area for transformation, it loads the raw data directly to the target data store to be transformed as needed.
While both processes leverage a variety of data repositories, such as databases, data warehouses, and data lakes, each process has its advantages and disadvantages. ELT is particularly useful for high-volume, unstructured datasets as loading can occur directly from the source. ELT can be more ideal for big data management since it doesn’t need much upfront planning for data extraction and storage. The ETL process, on the other hand, requires more definition at the onset. Specific data points need to be identified for extraction along with any potential “keys” to integrate across disparate source systems. Even after that work is completed, the business rules for data transformations need to be constructed. This work can usually have dependencies on the data requirements for a given type of data analysis, which will determine the level of summarization that the data needs to have. While ELT has become increasingly more popular with the adoption of cloud databases, it has its own disadvantages for being the newer process, meaning that best practices are still being established.
How ETL works
The easiest way to understand how ETL works is to understand what happens in each step of the process.
During data extraction, raw data is copied or exported from source locations to a staging area. Data management teams can extract data from a variety of data sources, which can be structured or unstructured. Those sources include but are not limited to:
- SQL or NoSQL servers
- CRM and ERP systems
- Flat files
- Web pages
In the staging area, the raw data undergoes data processing. Here, the data is transformed and consolidated for its intended analytical use case. This phase can involve the following tasks:
- Filtering, cleansing, de-duplicating, validating, and authenticating the data.
- Performing calculations, translations, or summarizations based on the raw data. This can include changing row and column headers for consistency, converting currencies or other units of measurement, editing text strings, and more.
- Conducting audits to ensure data quality and compliance
- Removing, encrypting, or protecting data governed by industry or governmental regulators
- Formatting the data into tables or joined tables to match the schema of the target data warehouse.
In this last step, the transformed data is moved from the staging area into a target data warehouse. Typically, this involves an initial loading of all data, followed by periodic loading of incremental data changes and, less often, full refreshes to erase and replace data in the warehouse. For most organizations that use ETL, the process is automated, well-defined, continuous and batch-driven. Typically, ETL takes place during off-hours when traffic on the source systems and the data warehouse is at its lowest.
ETL and other data integration methods
ETL and ELT are just two data integration methods, and there are other approaches that are also used to facilitate data integration workflows. Some of these include:
- Change Data Capture (CDC) identifies and captures only the source data that has changed and moves that data to the target system. CDC can be used to reduce the resources required during the ETL “extract” step; it can also be used independently to move data that has been transformed into a data lake or other repository in real time.
- Data replication copies changes in data sources in real time or in batches to a central database. Data replication is often listed as a data integration method. In fact, it is most often used to create backups for disaster recovery.
- Data virtualization uses a software abstraction layer to create a unified, integrated, fully usable view of data—without physically copying, transforming or loading the source data to a target system. Data virtualization functionality enables an organization to create virtual data warehouses, data lakes and data marts from the same source data for data storage without the expense and complexity of building and managing separate platforms for each. While data virtualization can be used alongside ETL, it is increasingly seen as an alternative to ETL and to other physical data integration methods.
- Stream Data Integration (SDI) is just what it sounds like—it continuously consumes data streams in real time, transforms them, and loads them to a target system for analysis. The key word here is continuously. Instead of integrating snapshots of data extracted from sources at a given time, SDI integrates data constantly as it becomes available. SDI enables a data store for powering analytics, machine learning and real-time applications for improving customer experience, fraud detection and more.
The benefits and challenges of ETL
ETL solutions improve quality by performing data cleansing prior to loading the data to a different repository. A time-consuming batch operation, ETL is recommended more often for creating smaller target data repositories that require less frequent updating, while other data integration methods—including ELT (extract, load, transform), change data capture (CDC), and data virtualization—are used to integrate increasingly larger volumes of data that changes or real-time data streams.
In the past, organizations wrote their own ETL code. There are now many open source and commercial ETL tools and cloud services to choose from. Typical capabilities of these products include the following:
- Comprehensive automation and ease of use: Leading ETL tools automate the entire data flow, from data sources to the target data warehouse. Many tools recommend rules for extracting, transforming and loading the data.
- A visual, drag-and-drop interface: This functionality can be used for specifying rules and data flows.
- Support for complex data management: This includes assistance with complex calculations, data integrations, and string manipulations.
- Security and compliance: The best ETL tools encrypt data both in motion and at rest and are certified compliant with industry or government regulations, like HIPAA and GDPR.
In addition, many ETL tools have evolved to include ELT capability and to support integration of real-time and streaming data for artificial intelligence (AI) applications.
The future of integration - API using EAI
Application Programming Interfaces (APIs) using Enterprise Application Integration (EAI) can be used in place of ETL for a more flexible, scalable solution that includes workflow integration. While ETL is still the primary data integration resource, EAI is increasingly used with APIs in web-based settings.
ETL, data integration, and IBM Cloud
IBM offers several data integration tools and services which are designed to support a business-ready data pipeline and give your enterprise the tools it needs to scale efficiently.
IBM, a leader in data integration, gives enterprises the confidence they need when managing big data projects, SaaS applications and machine learning technology. With industry-leading platforms like IBM Cloud Pak for Data, organizations can modernize their DataOps processes while using best-in-class virtualization tools to achieve the speed and scalability their business needs now and in the future.
For more information on how your enterprise can build and execute an effective data integration strategy, explore the IBM suite of data integration offerings.
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