When you’re fond of handling the never-ending streams of data, there’s a good chance you already have insights into the words ‘Extract, Transform, Load.’ In a nutshell, 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.
While ETL is three distinct steps, in a real sense, it is a much more nuanced process that requires a wide range of options. That explains why most engineers go through a lot when looking for the right solutions. Below are some common ETL mistakes you should try avoiding at all costs.
Underestimating Volume
Most companies that leverage an ETL pipeline need to be more accurate with volume. Remember, the amount of data you are processing only goes up, not down. Therefore, it is challenging to predict what lies ahead. The good news is there are many solutions you can employ to deal with the undeniably cumbersome issue of massive increases in data.
While choosing ETL tools, 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. It is only then that you stand a better chance of reaping maximum benefits.
Investing in Wrong Software or Hardware
Among the most common mistakes you can make while designing and building an ETL solution is investing in new tools and writing code without factoring in your business requirements/needs. To avoid this, it is in your best interest to perform a detailed analysis of every major stakeholder and their goals. It is then that you can start constructing your solution.
Remember, it is essential to choose the right ETL tools for each component of your stack and update components of your ETL process when your technologies and business needs change. After all, there is only one essence of investing in Snowflake ETL or any other ETL tool determining why you need it in the first place.
