Apache Arrow
What it is: Columnar in-memory data format and set of libraries. Standardizes how data is represented in memory across different tools. Foundation for fast analytics.
What It Does Best
Zero-copy data sharing. Pass data between Python, R, Spark, databases without serialization. 100x faster than pickling pandas DataFrames.
Blazing fast analytics. Columnar format optimized for modern CPUs. SIMD vectorization makes operations incredibly fast. Used by Polars, DuckDB, DataFusion.
Interoperability. Write once, use everywhere. Arrow Flight for network transfers. Works with Parquet, Feather, CSV. Language-agnostic.
Pricing
Free. Open source, Apache 2.0 license.
When to Use It
✅ Moving data between different systems/languages
✅ Need maximum performance for analytics
✅ Building data pipelines that span multiple tools
✅ Working with columnar formats (Parquet, ORC)
When NOT to Use It
❌ Simple pandas-only workflows (overhead unnecessary)
❌ Learning data science (start with pandas)
❌ Row-oriented data (traditional RDBMS better)
Bottom line: The infrastructure of modern data tools. You're probably using it without knowing. Powers Polars, DuckDB, Spark 3+. Not a tool you use directly, but benefits everything you do use. The future of analytics.