16 ETL Best Practices to Follow for Efficient Integration

ETL (Extract, Transform, and Load) is essentially the most important process that any data goes through as it passes along the Data Stack. It stands for Extract, Transform, and Load. Extracting is the process of getting data from its source. This is followed by changing or transforming the data suitably. The final step is to load the data to the desired database or warehouse. There are several ways to perform this process, including manually or by using automated tools like Hevo Data. 

This article will guide you through some ETL best practices and process design principles. You will also get a brief overview of ETL in further sections. Let’s get started.

What is ETL?

The Modern Data Analytics Stack uses the ETL process to extract data from a variety of data sources, including social media platforms, e-mail/SMS services, consumer service platforms, and more, in order to acquire important and actionable customer insights or store data in data warehouses.

What is the importance of ETL?

• Handles Diverse Data Sources: ETL tools collect, read, and migrate large volumes of raw data from various sources and platforms, ensuring seamless integration.
• Enables Data Consolidation: These tools consolidate data into a single database, data store, or warehouse, simplifying access and management.
• Simplifies Data Transformation: ETL processes perform tasks like sorting, joining, reformatting, filtering, combining, and aggregating to make data more comprehensible and usable.
• Improves Data Accessibility: By breaking down data silos, ETL tools empower data scientists to access and analyze integrated data efficiently.
• Speeds Up Decision-Making: ETL tools streamline the data preparation process, enabling faster insights and better business decisions.

ETL Best Practices

Every organization’s data management strategy revolves around extract, transform, and load (ETL) procedures. Establishing a set of best practices will improve the robustness and consistency of these processes. When moving data from, let’s say, Salesforce to Redshift, adhering to these best practices is essential to prevent data loss, maintain data quality, and optimize data flow. Let’s look at some of the best practices in ETL that organizations utilize.

1. Understand and Analyze the Source 

It is important to understand the type and volume of data you will be handling. To best process your data, you need to analyze the source of the data. This includes being familiar with the data types, schema, and other details of your data. These sources can consist of SaaS (Software-as-a-Service) applications such as Salesforce, HubSpot, or even another database. Also, you can use a staging table to make various decisions and then move the data to an actual table.

Example: A retail company extracting sales data from Salesforce needs to analyze schemas for columns like customer_id, product_id, and sales_date. By staging the data first, they detect discrepancies like null values in customer_id.

2. Solving Data Issues

Data is the biggest asset for any company today. Processing it with utmost importance is essential. Thus, it is crucial to solve any data issues that arise in one run of the ETL cycle so that it doesn’t repeat itself in the next cycle. Some ways of doing this include adding autocorrect tasks for predictable errors, adding data validation constraints, and talking to source partners if the error persists.

Example: An e-commerce firm encounters duplicate entries during ETL. They implement an autocorrect task to remove duplicates based on unique transaction_id.

3. ETL Logging

ETL logging includes documenting all events occurring before, during, and after an ETL process. An ETL process cannot be decided on through a cookie-cutter approach; every business is different and requires a unique solution. Maintaining proper logs helps you make this choice and tailor your ETL process. 

4. Checkpoint for Recovery

It is wise to set up checkpoints through the ETL process. Unexpected errors or failures are not uncommon when moving large amounts of data. Checkpoints help track where the error occurred so that the process does not have to be restarted from the beginning.

Example: During a 1TB data transfer, a failure occurs after 500GB is loaded. The checkpoint ensures the process resumes from 500GB instead of restarting.

5. Auditing

Auditing ensures that the ETL process is going on as desired. This would act as the insurance policy if you consider the ETL process an automobile. ETL auditing lets you ensure no abnormalities in the data, even when there are no errors.

Example: An auditing script compares record counts between the source and target tables, ensuring no data is lost or duplicated.

6. Modularity

Modularization is the process of abstracting ETL processes into smaller, reusable blocks. This helps simplify the process and reuse a single code block for multiple processes. This can be done by breaking down the code into several functions while leveraging the different object-oriented programming concepts. It reduces duplication in future work, makes unit testing easier, and establishes a standard that every process must follow. 

Example: An insurance company’s ETL process is broken into reusable modules for extraction, transformation, and loading, so when a new data source is added, only the extraction module needs updating.

7. Secure Data Prep Area

Cleaning and preparing your data is a big part of ETL. Keeping the data prep area secure requires high discipline, but it is essential. This involves restricting access to this area, cautiously granting permissions, and maintaining security regulations.

Example: A healthcare organization encrypts sensitive patient data in its ETL staging area, ensuring only authorized personnel access the data, maintaining HIPAA compliance and preventing unauthorized access.

8. Alerting

Setting up an alert system in case of an error is one of the best practices for ETLs. It helps you correct the error immediately. This is especially important in case of unauthorized access or any other security breach.

Example: A logistics company sets up alerts for ETL failures. If shipment data isn’t loaded as expected, an email notification is sent to the operations team to ensure quick resolution.

9. Optimizing ETL Solution

This involves general practices that help make the ETL process quicker. This consists of using parallel processes wherever possible. Ensuring that your hardware can handle the ETL process, capturing each running time, and comparing them periodically are some other best practices you can follow. Making simple changes like disabling checks and foreign key constraints or separating triggers into a complete task can reduce the running time of an ETL cycle. 

Example: A telecom company optimizes its ETL by parallelizing extraction, processing call detail records from different regions simultaneously, reducing job runtime from 6 hours to 2 hours.

10. Understand Your Organizational Requirements

ETL technologies help your data scientists access and analyze data and turn it into business knowledge by breaking down data silos. In a nutshell, ETL tools are the first and most crucial phase in the data warehousing process, allowing you to make better decisions in less time. 

11. Data Caching

Data caching, or storing previously used data in memory or on discs so that it may be accessed fast again, is a simple and effective approach to speed up ETL integration.

Example: A financial services firm caches stock market API responses during ETL, preventing redundant requests and speeding up the process by using the same data until new updates are required.

12. Maximize data quality

When it comes to ETL integration, the old adage “crap in, crap out” holds true. Ensure the data you enter into your ETL operations is as clean as possible if you want rapid, predictable outcomes. Automated data quality solutions can assist with this work by detecting missing and inconsistent data in your data sets.

Example: A retail company performs data quality checks, flagging records with invalid order_amount or missing shipping_address. Invalid records are rejected before loading into the warehouse.

13. Building Your Cleansing Machinery

Data inconsistencies should be addressed when loading data from several sources or a single source. It’s also a good idea to eliminate any serious data inaccuracies.

• Mismatched data should be repaired, and column sequence order must be preserved.
• Use normalized data or convert data to 3rd normal form for easier access.
• If necessary, enrich or improve data by combining data from Purchasing, Sales, and Marketing databases (for example, adding data to asset detail by combining data from Purchasing, Sales, and Marketing databases).
• Use declarative function variables to clean the data so that various data sources can reuse the same data purification process.

14. Use parallel processing

Automation not only saves your team time and effort, but it also allows them to do ETL integrations in parallel – that is, numerous integrations at the same time.

15. Minimize Data Input

Serial ETL operations should be avoided at all costs. Instead, you can reduce time-to-value by performing as many integrations as your architecture allows.

The less data you feed into the ETL process, the quicker and cleaner your outputs will be. That’s why you should remove any unnecessary data as early as possible in the ETL process.

16. Automating the Process

Automating your ETL integration procedures is nearly a given if you want them to be quick and efficient. However, since we live in a time when full automation is difficult to achieve, especially for teams working with legacy infrastructure, tools, and procedures, it’s good to remind ourselves of the importance of automation.

ETL integration automation entails relying solely on tools to clean data, transport it through the ETL pipeline, and check the outcomes. Effective ETL requirements help optimize data flow, ensuring seamless integration and reliable analytics.

These are some of the ETL Best Practices!

What are the Challenges when building ETL Architecture?

• Ignoring the importance of long-term maintenance.
• Underestimating the need for data transformation.
• Choosing not to engage in the customer development process.
• Creating a tight connection between the various pieces of your data pipeline.
• Creating your ETL process based on the size of your data.
• Not being able to recognize the warning indicators.

You can use an automated ETL Tool like Hevo to overcome and avoid these challenges. Hevo offers a no-code data pipeline that will take full control of your data integration, migration, and transformation process. To learn more about ETL challenges, check out our detailed blog.

Discover crucial ETL security measures with our comprehensive guide on ensuring safe and secure ETL operations.

Conclusion

In this blog post, you have seen a few of the ETL best practices that will make the process simpler and easier to perform. Following these best practices, you can quickly move data from multiple sources to your database or warehouse. The user can either choose to set up the ETL process manually via traditional techniques or rely on an automated tool. Hevo Data is one such tool that provides a simple solution for your target data transfer.

Sign up for a 14-day free trial and experience the feature-rich Hevo suite firsthand. You can also look at the unbeatable pricing that will help you choose the right plan for your business needs.

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