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The Problem: Skyrocketing AWS Analytics Costs When managing analytics for our loan management system, we initially turned to the standard AWS stack: Amazon Redshift for data warehousing and AWS Glue for ETL pipelines. The result? A shocking $800 bill...

Introduction Parquet has quickly become one of the most popular file formats for storing large-scale analytics data. Parquet is now a top choice due to its efficiency, compression, and seamless integration with big data frameworks. My experience cont...

Every data team knows the drill: a PM needs to “just take a quick look” at some Parquet data. That usually means asking an engineer to write SQL or spin up a tool to pull a few rows. It’s a small ask, but one that happens often enough to slow everyon...

One of the most important decisions in your Apache Spark pipeline is how you store your data. The data format you choose can dramatically affect performance, storage costs, and query speed. Let’s explore the most common file formats supported by Apac...

Data is growing, and fast. Whether you're querying petabytes in a data lake or running analytics in a cloud warehouse, the format you store your data in can make or break performance. Parquet is your saviour. If you've ever used tools like Apache S...
