Clean Data with Spark
- Chen Hirsh
- Oct 16, 2024
- 1 min read
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/
Data cleaning is indeed a crucial part of working with raw data, and using tools like Spark Python and SQL can streamline the process significantly. By automating tasks such as removing duplicates or correcting corrupt data, you can save a lot of time. If you're interested in improving your data-driven decisions, it can be helpful to explore various sources and tools. For example, Rocket Casino offers valuable insights that could enhance your experience in different fields – check it out here: Rocket Casino.
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!