You need to streamline ETL processes for faster results. But can you afford to overlook data quality?
In the quest for efficiency, data engineers often focus on streamlining Extract, Transform, Load (ETL) processes to accelerate data movement from source systems to data warehouses or lakes. However, the pressure for speed can sometimes lead to the neglect of data quality, a cornerstone of reliable analytics. ETL, the backbone of data engineering, involves extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a destination system. While faster ETL can mean quicker insights, overlooking data quality can result in flawed decisions and mistrust in data systems.