ETL Automation: Streamline Data, Empower Decisions
The data warehousing market is booming, projected to reach nearly $86 billion by 2032. But how can you leverage this astronomical growth for your business? The answer lies in ETL automation.
This article will equip you with the knowledge to harness the power of automated ETL and transform your data game.
What is ETL Automation?
ETL automation is the use of advanced tools to perform the Extract, Transform, and Load process without human intervention. Traditionally, this was a manual and time-consuming task. ETL automation streamlines the process using software tools, freeing up valuable IT resources and ensuring faster, more reliable data pipelines.
Evolution of ETL to Automated ETL
Companies interested in using big data to guide the decision-making process need a way to combine multiple data sources into a single repository. Extract, Transform, and Load ensures consistency and prevents duplicates from entering the data warehouse. Before we dive into the use of automation tools to streamline the ETL process, let’s examine the standard approach to ETL.
With traditional ETL, a software engineer must apply formatting rules, convert data types, and perform other tasks to ensure formatting errors don’t derail an organization’s efforts to integrate data successfully. As you can imagine, manual workflows are costly and time-consuming.
ETL automation makes it possible to design, implement, and test data pipelines without human intervention.
Automation doesn’t eliminate the need for engineers, but it does give them more time to focus on gathering business requirements and establishing the right data architecture for your company’s needs.
Why Should Businesses Use ETL Automation?
Using automated ETL tools has several benefits. Whether you’re the CTO of a startup or the business intelligence director at a Fortune 500 company, here’s why you should automate as many data transformation activities as possible.
Data integration
The whole point of ETL is to take raw data from multiple sources and load it into a single repository, creating a unified view for users. If you rely on manual processes, performing Extract, Transform, and Load activities takes much longer. The longer it takes to complete the ETL process, the longer it takes for users to have access to real-time data that can help them make better business decisions.
ETL automation makes every aspect of data integration easier, from data profiling to data validation.
Data quality improvement
One of the biggest benefits of using ETL automation tools is that automation increases data quality. No matter how skilled your software engineers are, there’s a chance that they’ll make at least one mistake during the ETL process. Mistakes are even more likely if you have more than one person working on the same project.
For example, perhaps two ETL developers are working to combine multiple data sources. If both of them transfer the same data set to your new repository, you’ll end up with duplicate records.
One or two duplicates may not cause problems, but what happens if the data sets relate to the amount of revenue your company earns or the number of employees with specific certifications? Executives and middle managers could end up making decisions based on inaccurate data, leading to serious consequences for your company.
ETL automation also reduces the risk of data misinterpretation and makes it easier to set business rules, both of which contribute to increased data quality.
Key Components of ETL Automation
ETL automation includes the following components:
- Data extraction: Extraction involves collecting data from various sources. For example, you might have some data stored in an HRIS, some in a legacy MRP system, and some in an accounting system. You need to extract data from each source before you can combine it all.
- Data transformation: Your automated ETL tools now convert the source data into a usable format. Data transformation involves removing duplicates, applying formatting rules, and taking other steps to ensure all data is formatted correctly. In other words, data transformation is a form of data processing.
- Data loading: During the final step of the ETL process, you bundle the data and move it to your data warehouse. Data loading makes it possible for team members to use data analytics to their advantage without having to comb through multiple databases to find what they need.
ETL automation also makes it easier to complete the testing process, which ensures that the data loaded into the central repository meets your requirements for consistency, accuracy, reliability, and integrity. Think of ETL testing as a type of auditing that ensures your data warehouse is an asset instead of a liability.
Automated ETL testing includes the following components:
- Identifying business requirements
- Designing test cases
- Preparing test data
- Building reports
- Analyzing reports
ETL Automation Processes
ETL automation is just one aspect of data management, but it’s an important one. Whether you’re interested in test automation or other methods of automating ETL activities, here are a few processes to try:
- Custom coding: One of the best ways to automate the Extract, Transform, and Load process is to use SQL, Python, R, or another scripting language to create custom code. It’s possible to create code that handles each aspect of the process without human intervention, ensuring your company can benefit from ETL automation.
- Cloud services: Custom coding gives you complete control over your ETL activities, but it’s time-consuming and requires at least one skilled coder on your team. Cloud services are an attractive alternative, as they can handle large data volumes without using your on-premises resources. If you want a serverless solution to your ETL challenges, consider using Azure Data Factory, Informatica, AWS Glue, or a similar service.
- ETL tools: Talend, SSIS, and other tools simplify the ETL process and reduce the risk of coding errors. One of the many benefits of using ETL tools is that they come with pre-built connectors, which make it possible to transfer data efficiently. Another benefit of using ETL tools is that they have drag-and-drop functionality, ensuring that team members don’t get lost in the proverbial weeds when they try to access transformed data.
- Workflow orchestration tools: Some tools, such as Airflow, make it easier to manage the workflows involved in ETL activities. For example, certain tools handle dependencies or track pipeline performance. Like ETL tools, workflow orchestration tools eliminate the time-consuming processes involved in data extraction, data transformation, data testing, and data migration.
Advantages of Implementing ETL Automation
Switching from manual ETL to automated ETL processes offers the following advantages:
- Reduced costs: Eliminating manual workflows limits the number of ETL developers and other software engineers needed to manage the ETL process. The result? Lower labor costs for your company.
- Increased efficiency: When software engineers aren’t bogged down by manual processes, they have more time for ETL testing and other critical activities. Who knows? Maybe one of your engineers will use the extra time to solve one of your most pressing business challenges.
- Better customer service: Have you ever asked a vendor a question and had to wait hours or even days for them to respond because they didn’t have the answer at their fingertips? How frustrating! ETL automation gives team members access to dashboards and other tools to help them deliver better service to your most important customers.
- Enhanced scalability: By keeping your company’s costs in check, ETL automation helps your revenue increase much faster than your costs, the very definition of a scalable business.
- Shorter development lifecycle: If your company follows the DevOps philosophy, you know how important it is to focus on continuous delivery. You can’t afford to have engineers using standard ETL testing tools, relying on cumbersome user interfaces, or searching through relational databases for data to extract. ETL automation speeds up each step in the ETL process, reducing the amount of time it takes to introduce new products or update existing ones.
ETL Automation Challenges
ETL automation gives you more control over your data flows, but it’s not perfect. Yes, it can improve your data quality, but it can also introduce inaccuracies and duplicates. Whether you’re choosing a data platform or testing a data-driven approach to decision-making, you need to choose the right tools and learn how to use them correctly.
In some cases, ETL automation also creates problems with data governance. For example, if you use data lakes to store both raw data and structured data, you may need to tweak your company’s policies regarding who can read or download certain types of data. It’s also important to document each data source to increase transparency.
Finally, automation introduces the need for additional testing, which involves checking for errors and discrepancies. Although ETL test automation is an option, you need to continually refine your testing processes.
Future Prospects of ETL Automation
As data warehousing becomes more popular, the need for ETL automation will only increase. You can “get in on the ground floor,” so to speak, by automating your extraction, transformation, loading, testing, and optimization activities.
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