Blog
How to Reduce Amazon Athena Costs by Up to 90%
Published on
Amazon Athena is easy to use, but query costs can grow quickly when data in S3
Converting CSV and JSON to Parquet on AWS: Options, Pros, and Cons
Published on
There are several ways to convert CSV and JSON to Parquet...
Parquet Schema Explained: Why It Matters for Analytics
Published on
Apache Parquet has become the standard file format for modern analytics platforms such as...
Avoid Building Data Infrastructure
Published on
Every growing software company eventually runs into the same problem. Your application starts generating more data....
Parquet Compression Explained
Published on
Apache Parquet is one of the most efficient file formats for analytics, but much of its performance comes from one important feature: compression.
Compress Archived Data and Reduce Storage Costs
Published on
Many companies keep years of logs, exports, backups, and historical datasets in Amazon S3. Those files are rarely modified but still consume storage and may eventually be queried with tools like Athena.
Parquet Versions Explained: Parquet v1 vs Parquet v2
Published on
Apache Parquet is one of the most widely used file formats for analytics. It stores data in a columnar layout, making queries faster and reducing storage costs.
What Are Amazon Athena, Apache Spark, AWS Glue, Trino, and Presto? A Simple Guide
Published on
Modern companies collect huge amounts of data from applications, websites, logs, payments, sensors, CRM systems, and internal tools. Storing this data is only the first step. To get value from it, companies need tools that can query, transform, clean, and analyze it. Athena, Spark, Glue, Trino, and Presto help companies analyze and process data at scale.