To stay competitive in today’s environment, enterprise teams must have an in-depth understanding of their data and processes to act quickly on trustworthy metrics.
Of course, that’s much easier said than done. Even for companies with internal resources for analyzing data, collecting all the necessary information from numerous connected sources is extremely complex.
Extract, transform and load (ETL) was designed to streamline the data management process for companies handling big data from a variety of sources. ETL automation enables teams to optimize the process and gain deeper insights faster.
Read on to learn about:
- The ETL process and data warehousing
- What’s involved in ETL automation and its benefits
- The importance of ETL testing and some popular tools
- How to select the right ETL automation tool
What happens in the ETL process?
ETL is the process of extracting data from diverse sources, transforming it into a consistent format and loading it into a data warehouse or target system.
The data extraction phase involves retrieving data from multiple sources, including databases, flat files, APIs and cloud platforms. Once extracted, it undergoes data transformation, where it’s cleaned, validated and standardized to meet specific business requirements.
In the final ETL stage, the transformed data is loaded into a target system or data platform, typically a data warehouse or relational database, where it can be accessed for analysis and reporting.
Data transformation
Data transformation is a crucial step in the ETL process that enables organizations to convert raw data into a consistent and meaningful format. This requires applying business rules, data cleansing techniques, data validation and aggregation operations to ensure accuracy, data quality and integrity.
What is a data warehouse?
A data warehouse is a central repository that stores structured and organized data from various sources. It’s a consolidated data source for business intelligence (BI) tools, data analytics and reporting.
Data warehouses enable organizations to leverage historical and real-time data to gain insights, identify trends and make data-driven business decisions. They provide a scalable and reliable storage solution for large volumes of structured and semi-structured data.
What about ETL testing?
The ETL testing process is the act of validating and verifying ETL workflows to ensure transformed data is accurate and loaded into the target system without errors or inconsistencies. It involves creating test cases, designing test data sets and executing tests to detect issues. This testing uncovers duplicate data, missing data and data integrity and data quality problems.
ETL testing is a critical part of the data transformation process — a way to verify that the final data set is reliable. An ETL tester is responsible for regression testing to make sure new changes don’t negatively impact functionality and performance testing to verify efficiency under different conditions.
Creating a comprehensive testing strategy and establishing a dedicated testing environment are important to validate the target database, maintain the integrity of the given data model and check for data loss.
ETL testing tools
ETL testing can add significant value in ensuring systems are running properly and data quality is consistent.
There are several ETL testing tools available to build comprehensive coverage of test scenarios. Some popular ETL tools are Informatica, Oracle Data Integrator, Snowflake and Amazon Redshift.
ETL testing automation tools have features for data validation, metadata validation, source-to-target mapping verification, data profiling and more. They help streamline the testing process, reduce manual effort and improve overall data quality.
Automating the ETL process
ETL automation leverages automation tools and technologies to streamline and optimize ETL workflows. By automating repetitive and time-consuming tasks, organizations can improve efficiency, reduce errors and accelerate data integration and transformation.
Automation solutions for ETL provide a visual interface for designing and managing data pipelines. This allows users to define data sources, transformations and target systems. These tools often come with pre-built connectors and integration capabilities, enabling seamless connectivity with various data sources and formats.
Automating ETL processes plays a crucial role in the DevOps framework. It enables effective integration and management of data pipelines, continuous integration and continuous delivery (CI/CD), data quality and testing and version control configuration and management.
Benefits of ETL automation
Implementing ETL automation can generate many benefits for organizations, including greater efficiency, better data quality, time and cost savings and increased scalability.
- Automated ETL eliminates time-consuming manual tasks, reducing the risk of errors while enabling faster data processing and better data flow. Organizations can process large volumes of data at a faster pace to make more strategic decisions benefiting their bottom line.
- Incorporating data validation and cleansing techniques to preserve data accuracy and consistency can improve data quality. By automating data quality checks, organizations can identify and rectify issues promptly.
- Automated ETL processes significantly reduce the time to transform and load data, leading to cost savings and better resource allocation. ETL tools that enable automation can handle growing data volumes and accommodate changing business needs. Their scalability allows teams to adjust data pipelines, add new data sources and modify transformations as needed.
Selecting an ETL automation tool
Choosing the right ETL automation solution is crucial for optimizing data integration workflows and ensuring robust data management. With a myriad of options available, consider your specific use cases, user interface design, automated testing capabilities and more to make an informed decision.
Use cases
Before vetting solutions, you should clearly define how your organization will use ETL automation. Determine whether you need this tool for regular data integration tasks, complex data transformations, data migration projects or a combination of these.
Each use case will involve different priorities. For example, data migration projects require diverse data type handling, robust data quality testing and efficient transformation logic. If you’ll primarily use the tool for data testing and validation, you’ll need it to offer data profiling, validation rules and automated checks.
User interface
An intuitive and user-friendly interface is essential for effective ETL automation. Ideally, you’ll have a visual representation of data pipelines so users can design, manage and monitor workflows easily. Drag-and-drop functionality can simplify the process of defining source systems and target data warehouses. In the end, going with a simplified UI can significantly reduce the learning curve and reduce costs.
Other key features
Your company size and IT resources may also drive your final decision. If you have limited technical expertise on hand, no-code ETL tools could be highly beneficial. With a larger or more skilled team, you might be able to take advantage of open-source tools such as Apache NiFi that can be customized for your specific needs and have an active community for development and support.
Your selection should ultimately facilitate confident decision-making by giving your team easy access to powerful data management and data workflow orchestration features.
Perfecting ETL automation with ActiveBatch
ActiveBatch is an ETL automation tool that enables organizations to design, schedule and monitor ETL processes efficiently.
It provides extensive connectivity options, including APIs, database connectors and cloud data warehouse integrations. Through a robust library of connections, teams can integrate ActiveBatch’s workload automation software with the tools and systems they’re already using, including BI tools, SAP, Oracle, Microsoft SQL, Amazon, Informatica and more. And the Super REST API makes it easy to authenticate and create customer extensions and connections for third-party services.
IT teams can manage infrastructure strategy by deploying automation processes on-premises, in the cloud or in hybrid environments. Interactive dashboards track the progress of automated ETL processes, and predictive monitoring with corrective actions can reprioritize processes to ensure on-time delivery. Even data lake updates can be automated for increased data quality and reporting.
Advanced scheduling capabilities include the ability to trigger data warehousing and ETL processes based on external conditions. Job triggers can include email, file events, data transformations and more.Get a personalized demo of ActiveBatch.
ETL automation process FAQs
The five steps of the ETL process are:
1. Data extraction: Retrieving data from various sources like databases, flat files, APIs and cloud platforms
2. Data transformation: Cleaning and validating data and standardizing it to meet specific business requirements
3. Data loading: Moving the transformed data into a target system like a data warehouse or relational database
4. Data validation: Ensuring the transformed data is accurate and consistent
5. Data integration: Combining data from different sources to create a unified view for analysis and reporting
Learn more about simplifying big data with data orchestration
There are several types of ETL testing. Unit testing is testing individual components of the ETL process to make sure they function correctly. Integration testing verifies that the components work together as expected.
In regression testing, an ETL tester or tool investigates the impact of changes on existing ETL processes. Then, in performance testing, they evaluate the efficiency of the process under various loads and conditions.
Data validation testing is checking for data accuracy, integrity and consistency.
Learn more about IT automation and how it relates to ETL automation and testing.
To automate the ETL process, select a tool like ActiveBatch that supports data management and workflow orchestration. Specify the sources you’ll need to extract data from and the target systems where you want to load the data.
Next, create workflows that outline the extraction, transformation and loading steps. Use your automation tool to schedule ETL jobs and monitor their progress.
Finally, set up mechanisms to handle errors, address bottlenecks and send alerts in case of failure or anomaly.
Learn more about IT automation for data warehousing projects.
ETL testing typically includes test planning, design, execution, defect management and reporting. In planning and design, you define the scope and strategy and create test cases, scripts and data sets. During execution, you run the tests to validate ETL processes. Then, you’ll log and track defects identified during testing. Lastly, the framework calls for documenting test results and issues.
Discover the role of job orchestration frameworks in enhancing task execution and the efficiency of your operations.
