Extract, transform, and load (ETL) processes are at the heart of business data integration and warehousing operations. ETL processes are vital to those who need to access strategic business intelligence (BI) data. However, the growing volume and diversity of data is making managing these ETL processes increasingly difficult, time-consuming, and costly. How can savvy IT leaders ensure their processes keep up with demands — and how can they benefit?
Benefits of an improved ETL process
As leaders begin to scale up their company data processing efforts, they often begin to see problems in ETL performance. Complaints begin coming in from staff members who rely on the processed data for their reports, decision-making, and daily operations. Most ETL operations run overnight, and staff members expect and need processed results when they arrive for work in the morning. Everything slows down when these staffers don’t have the information they need to perform their jobs. By implementing improvements to ETL processing, you can improve performance, reduce bottlenecks, as well as provide better support for end users immediately as your business continues to scale up.
Other benefits include
- the ability to store uniform, complete data in one place, simplifying management and reducing redundancies;
- access to historical data and comparison reporting;
- improved security;
- back-end processing that can handle data from acquired or merged companies; and more.
Those in production, sales, or customer service — any and all areas reliant on data analytics — can experience these improvements.
Improve your ETL process
When your ETL process isn’t keeping up with your growing data warehouse and analytical demands, it’s time to act. Here are some tips for optimizing your ETL operations.
- Correct bottlenecks. Determine which ETL operations use the most resources, then rewrite code for greater efficiency.
- Consolidate indexes. Database administrators often try to solve performance slowdowns by creating additional indexes, but this actually increases load times.
- Use set logic instead of cursors. Change a row-based cursor loop in your ETL code into a set-based SQL statement to make ETL processes run faster. Many ETL tools run load jobs in set-based ELT mode.
- Offload table joins to your database. This is more efficient than using an ETL tool to read and join the data as it frees up your ETL tool for processing.
- Divide large tables. Partition large tables into smaller ones. This speeds up ETL because multiple small tables with fewer rows are easier to process than enormous data sets made of many rows.
- Run parallel threads. Running parallel instead of serial threads when possible can optimize processing.
- Use only relevant data. Collect as much data as possible, but only use the most relevant data. This cuts down on processing time and allows leaders to scale as their businesses grow.
The best tip for ensuring your ETL processes don’t struggle with the data load as your business grows is to plan for scaling during the design phase. Use the above tips when planning your ETL operations and writing corresponding code. Through constant warehousing and data processing performance monitoring, IT decision-makers can ensure long-term success in their ETL implementations.