A Python Library every Data Engineer should know
As a data engineer in a large company, ensuring data quality is a key responsibility. Even if you perform your tasks diligently and rarely face major issues, there’s always a chance that end users may encounter data inconsistencies.
In today’s world of big data, not only is the data vast, but so are the processes managing it. With so many people involved in these processes, unexpected changes can occur at any time: columns may be renamed or removed, data types may shift due to new entries, or a numeric field might suddenly contain text. When this happens, the end user — like a dashboard viewer — quickly notices the issue and is unable to proceed with their work.
As data engineers, it’s our duty to minimize such occurrences. We need systems in place that notify us not only when something goes wrong but also provide insight into the root cause. This allows us to act swiftly, resolving the issue before end users are even aware of it.
This is where the Python package Great Expectations comes in. Throughout this blog, you’ll learn not only what Great Expectations is but also how to seamlessly integrate it into your daily workflow as a proactive data engineer. We’ll explore its advantages through real-world scenarios, using data from an e-commerce retailer as our example.
