A successful data management and BI strategy must answer some key questions, such as:
- Where does the required information exist?
- How can data from disparate systems be integrated to create a unified view?
- How to transform big data into a format that can be easily analyzed to extract actionable insights?
Extract, Transform, Load or ETL is a technology that is commonly employed to find answers to these questions and create a single version of truth. Although primarily a type of data integration, ETL is also employed to facilitate data migration, data warehousing, and data profiling.
What is ETL?
As the name suggests, ETL is a three-step process in which data is extracted from one or more data sources, converted into the required state, and loaded to a database or data warehouse. In addition to these three steps, developers are also required to perform error handling as and when required.
Step 1: Extract
Businesses collect data from a variety of internal and external sources. This data is kept within various databases, and therefore, data processing is needed to create an integrated and complete view of all information assets. The process starts with identifying data that is significant in supporting organizational decision-making. Once the data sources have been identified, connections are built to the required databases to extract data for transformation.
Step 2: Transform
Transformation is done to bring uniformity to the disparate data definitions of information collected from different data sources. A set of business rules (such as aggregation, joins, sort, union functions) are used to transform data into a consistent format for reporting and analysis and to ensure data consistency across the organization.
Step 3: Load
Loading transformed data into a data warehouse, database, data mart, or any other kind of data repository is the last step of the ETL process. Depending on the volume of data, target database, and the BI needs of the business, any of the following two types of loading methods can be used:
- Full Load – Full load refers to the initial data load that is performed to bring data into the data repository for the first time. Since this usually involves the transfer of large volumes of data, it is important to optimize the process using various techniques, such as parallel processing, load balancing, pushdown optimization, bulk data loading, concurrent workflow execution, and more.
- Incremental Load – Incremental load is used to synchronize new or updated data between the source database and the target data repository. Using incremental load, enterprises can keep the data warehouse updated with the most recent transactional data available, while saving the computing resources and time required to perform a full load every time new data is added to the source.
Why ETL is Important
Since its introduction, ETL has become a ubiquitous process in the world of data processing and management. From preparing large and disparate datasets for BI and analytics to handling complex data integration scenarios, the use of ETL is broadening beyond simple data movements.
Here are a few use-cases where ETL is commonly employed by enterprises:
ETL and Data Migration
Data migration is defined as the process where data is transferred between databases, data formats, or enterprise applications. There are various reasons why an organization may decide to migrate data to a new environment, such as to replace legacy applications with modern tools, switch to high-end servers, or consolidate data post-merger or acquisition.
Regardless of the underlying reason, ETL remains a proven method that many organizations rely on to respond to data migration needs. Using ETL tools, businesses can surface data from different data repositories as well as consolidate data from external and internal sources to offer business users a unified, well-rounded view of all business operations.
ETL and Data Warehousing
Data warehousing is a complex process as it involves integrating, rearranging, and consolidating massive volumes of data captured within disparate systems to provide a unified source of BI and insights. In addition, data warehouses must be updated regularly to fuel BI processes with fresh data and insights. ETL is a key process used to load disparate enterprise data together in a homogenized format to a data repository. Besides, with incremental loads, ETL also enables near real-time data warehousing, thereby providing business users and decision makers fresh data for reporting and analysis.
ETL and Data Quality
From erroneous data received from online forms to lack of integration between data sources and ambiguous nature of data itself, there are a number of factors that impact the quality of incoming data streams, thereby diminishing the value businesses can extract from its data assets.
ETL is a key data management process that helps enterprises ensure that only clean and consistent data makes it to their data warehouse and BI tools. Here are some of the ways businesses can use ETL to enhance data quality:
- Data profiling and standardization
- Data consolidation
- Data enhancement
- Data cleansing and verification
ETL and Application Integration
For a better view of enterprise information assets, integrating data stored in disparate applications such as Salesforce.com and MS Dynamics is mission critical. An end to end ETL process helps integrate data from applications, massage the data for ensuring data quality, and load it into a target destination such as a data warehouse or database.
What to Look for in an Enterprise ETL Tool?
Although a fairly simple process to understand, ETL can grow in complexity as the volume, variety, and veracity of data being transformed increases. Generally speaking, the following factors can impact the scope and complexity of an ETL task:
- The number and variety of data sources and destinations involved
- The number of tables created
- The type of transformations required. This may range from simple look-ups to more complex transformation data flows, such as flattening the hierarchy of an XML, JSON, or COBOL file or normalizing data
To successfully address these challenges and use ETL to create a comprehensive, accurate view of enterprise data, businesses need high-performance ETL tools that offer native connectivity to all the required data sources, capabilities to handle structured, semi-structured, and unstructured data, and built-in job scheduling and workflow automation features to save the developer resources and time spent on managing data.
Here is a round-up of the features businesses should look for in an enterprise-ready, high-performance ETL tool:
- Library of Connectors – A well-built ETL tool should offer native connectivity to a range of structured and unstructured, modern and legacy, and on-premise and cloud data sources. This is important because one of the core jobs of an ETL tool is to enable bi-directional movement of data between the vast variety of internal and external data sources that an enterprise utilizes.
- Ease of Use – Managing custom-coded ETL mappings is a complex process that requires deep development expertise. To save developer resources and transfer data from the hands of developers to business users, you need an ETL that offers an intuitive, code-free environment to extract, transform, and load data.
- Data Transformations – The data transformation needs of a business may vary from simple transformation jobs such as lookups and joins to more complex tasks like denormalizing data or converting unstructured data into structured tables. Therefore, to cater to these data manipulation needs, the ETL tool you select should offer a range of both simple and more advanced transformations.
- Data Quality and Profiling – You only want cleansed, accurate data to be loaded into your data repository. To ensure this, look for an ETL solution that offers data quality and profiling capabilities to determine the consistency, accuracy, and completeness of the enterprise data.
- Automation – Large enterprises handle hundreds of ETL jobs daily. The more of these tasks you can automate, the faster and easier it will be for you to extract insights from data. Therefore, look for an ETL solution with job scheduling and process orchestration and automation capabilities.
While these are a few important features a good ETL tool must have, the right selection of ETL software will depend on the volume, variety, velocity, and veracity of data your enterprise handles.
How to Improve ETL Performance
Long-running data transformation and load jobs that run for hours are not an unusual occurrence for ETL administrators. As data volumes and disparity grow, ETL processes and data flows may become more complex, taking up more computing resources and developers’ time.
Here are a few ways you can optimize the performance of your ETL jobs:
Data management solutions with a parallel processing engine support faster processing of large data files by splitting them into small chunks. Each chunk can then be processed separately in parallel, ensuring optimal utilization of computing resources and accelerating the data pipeline.
Pushdown Optimization (ELT)
Pushdown optimization or Extract, Load, Transform (ELT) is a variation of ETL that involves pushing down the transformation logic from the staging area to the source or target database. This is done to avoid unnecessary movement of data and speed up ETL performance.
Incremental Data Load
Incremental data load, a technique that involves loading only the changed data to the destination, helps save time and computing resources by eliminating the need to perform full data loads every time data needs to be refreshed in the data repository. More about Change Data Capture (CDC) or incremental data loads can be found here.
Streamline Your ETL Process with an Automated ETL Tool
Astera Centerprise is an enterprise-grade ETL solution that integrates data across multiple systems, enables data manipulation with a comprehensive set of built-in transformations, and helps move data to a data repository, all in a completely code-free, drag-and-drop manner.
The ETL tool utilizes a high-performance cluster-based architecture, industrial-strength ETL engine, and advanced automation capabilities to simplify and streamline complex ETL processes. With support for pushdown optimization, incremental data load, and connectivity to modern and legacy data sources, Astera Centerprise helps businesses integrate data of any format, size, or complexity with minimal IT support.