What do you do about data quality in big data projects? I can only work with clean and reliable data, otherwise my analyses end up as garbage in, garbage out. What methods do you use? Do you perform data validation, cleaning, continuous monitoring along the data pipeline, etc.? If you share, we can all learn together. 🧐
How is data quality ensured in big data projects?
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I can't perform any analysis without data cleaning, just like you. If I were to compare data to a kitchen, you'd need to filter and sort both the data and ingredients at every step. For instance, *OpenRefine* immediately comes to mind—it's like sorting out rotten tomatoes in a supermarket's vegetable section. You can find and fix inconsistencies, gaps, and duplicate records either automatically or manually. Then, using tools like *Talend* or *Apache Nifi*, I ensure a continuous flow from start to finish of the data pipeline while monitoring quality in real-time, much like quality control on a production line where every piece is inspected as it's processed. That way, if an error pops up anywhere along the pipeline, I can intervene instantly.