ETL Best Practices for Optimal Integration
The efficiency of ETL integration can make or break the rest of your data management workflow. Want to get the most from your ETL processes? Keep reading for high-performance ETL best practices.
8 ETL best practices
For optimum integration results, here’s eight of our best tips.
1. Minimize data input
The less data that you have going into ETL process, the faster and cleaner your results are likely to be. That’s why you want to strip out any unnecessary data as early in the ETL process as possible.
If you have redundant entries in a database, for example, clean those up before the ETL process starts, rather than spending time transforming that data only to cull it later.
2. Use incremental data updates
In addition to eliminating unnecessary data input from the ETL integration process, you can speed ETL integration by using incremental data updates. That means that when your data sets are updated, you add only the new data into your ETL pipeline, rather than replacing all of the existing data and starting again from scratch. Incremental data updates can be tricky to implement as part of an ETL integration solution, but the time it takes is worth it.
3. Maximize data quality
The old saying “crap in, crap out” applies to ETL integration. If you want fast, predictable ETL results, make sure that the data that you feed into your ETL processes is as clean as possible. Automated data quality tools can help with this task by finding things like missing and inconsistent data within your data sets.
Achieving the highest-quality data requires not just cleaning up data sets prior to ETL integration, but performing data quality maintenance on an ongoing, continuous basis.
4. Automate, automate, automate
It almost goes without saying that automating your ETL integration processes is key to making them fast and efficient. But since we live in an age when achieving full automation can be tough, especially for teams dealing with legacy infrastructure, tools, and processes, it’s worth reminding ourselves how important automation is.
In practice, ETL integration automation means minimizing the role of human operators and relying on tools alone to clean up data, move it through the ETL pipeline and verify the results.
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Watch this webinar to learn best practices for integrating legacy data sources, such as mainframe and IBM i, into modern data analytics platforms such as Cloudera, Databricks, and Snowflake.
5. Use parallel processing
Automation not only saves your staff a lot of headaches, but also makes it possible to do ETL integrations in parallel — or, in other words, doing multiple integrations at once.
No efficient ETL processes should be serial in nature. Instead, time-to-value is minimized by doing as many ETL integrations at the same time as your infrastructure allows.
6. Keep databases (and tables) small
The larger your databases and database tables, the longer ETL processes tend to take. You can often achieve an ETL integration performance boost by breaking large databases into smaller ones.
7. Cache data
Data caching, which means keeping recently used data in memory or on disks where it can be accessed again quickly, is a handy and easy-to-implement way to speed ETL integration.
8. Establish and track metrics
How effective are your ETL integration processes, and how are they improving over time? The only way to answer those questions is to establish, collect and analyze metrics that provide visibility into ETL processes.
Precisely’s ETL solutions can help. Learn best practices for integrating legacy data sources, such as mainframe and IBM i, into modern data analytics platforms such as Cloudera, Databricks, and Snowflake. Watch our webinar: Making the Case for Legacy Data in Modern Data Analytics Platforms