If you’re an engineer who constantly handles the never-ending streams of data, then the words ‘Extract, Transform, Load’ may haunt you whenever they come together. The process of extracting data from various sources, transforming it in line with the needs of your business, and loading it into a destination database is commonly referred to as ETL.
Even though ETL is by definition three distinct steps, in real sense it is a much more nuanced process that requires a wide range of options. No wonder most engineers tend to go through a lot when looking for the right solutions. In this post, we will share some of the common ETL mistakes you ought to avoid.
Choosing the Wrong Hardware or Software
One of the most common mistakes you can ever make when designing and building an ETL solution is investing in new tools and writing code without understanding your business requirements/needs. Actually, most business owners that do this end upregretting their decision in the long run.
To prevent this from happening, it is highly advisable that you carry out a detailed research of every major stakeholder, their goals after which you can start constructing your solution. It is essential for you to choose the right ETL tools for each component of your stack and update components of your ETL process whenever your technologies and business needs change.
Most companies that rely on an ETL pipeline at one point or another will always find themselves underestimating volume. Keep in mind the amount of data you’re processing only goes up, not down. For this reason, it is quite hard to predict what lies ahead, but there are so many solutions you can employ to deal with the undeniably cumbersome issue of massive increases in data.
When choosing ETL tools, be sure to leave plenty of bandwidth to scale up. If you’re fond of processing tons of data, there’s a very slight chance that the amount of data you’re processing will ever decrease.
By avoiding the above-mentioned tips, you’ll certainly get the most from your ETL solutions.