Cleaning data is a very common task for data professionals. The data we read from source systems are sometimes corrupt, duplicated, or need some other kind of transformation to adjust to our needs.
In this post, I demonstrate a few common data-cleaning tasks with spark Python and SQL.
See the full post on my blog - https://chenhirsh.com/cleaning-data-with-spark/
Cleaning data with Spark is essential for smooth workflows, but sometimes dealing with online platforms can be just as tricky. I recently made a donation through ActBlue, but the transaction didn’t go through as expected. My payment was stuck in limbo, so I had to contact ActBlue customer service https://actblue.pissedconsumer.com/customer-service.html . They got back to me quickly and helped me resolve the issue. It’s reassuring when customer service is efficient, but it would be better if the system ran without hiccups in the first place!