Pathway: A Python ETL Framework Built for Real-Time Data

Why Should You Care?

If you're working with streaming data, real-time analytics, or LLM pipelines, you know how messy it can get. Batch processing is often too slow, and stitching together Kafka, Flink, and custom Python scripts feels like reinventing the wheel.

Enter Pathway—a Python ETL framework designed for stream processing, real-time analytics, and even RAG (Retrieval-Augmented Generation) workflows. With over 28K GitHub stars, it’s clearly resonating with developers who need a simpler way to handle live data.

What It Does

Pathway is a high-performance Python framework for:

• Stream processing: Ingest and transform data from Kafka, Postgres, APIs, etc.
• Real-time analytics: Compute aggregations, joins, and windowed operations on the fly.
• LLM pipelines: Build workflows for embeddings, semantic search, and RAG without batch delays.
• ETL/ELT: Clean, enrich, and move data between systems in real time.

Unlike traditional batch-based ETL tools (e.g., Airflow), Pathway is built for low-latency scenarios where data freshness matters.

Why It’s Cool

1. Python-Native, but Fast

   • Write transformations in Python, but Pathway compiles them to Rust under the hood for performance.
   • No need to juggle JVM-based tools (looking at you, Flink).

2. Unified Stream + Batch

   • Treat streams and tables interchangeably (like Materialize or RisingWave).
   • Recent commits show active work on stream-table conversions—key for stateful workflows.

3. LLM & RAG Optimized

   • Prebuilt connectors for embedding models, vector DBs, and document chunking.
   • Example: Real-time semantic search over live chat logs or customer support tickets.

4. Active Development

   • Recent updates include Postgres snapshot connectors (#8979) and Rust upgrades.

How to Try It

1. Install:

   pip install pathway
   

2. Run the real-time LLM example (from their docs):

   import pathway as pw
   
   # Stream data from a file (or Kafka/Postgres/etc.)
   data = pw.io.csv.read("input.csv")
   
   # Tweak data in real time
   processed = data.select(
      *pw.this.columns,
      new_column=pw.apply(lambda x: x.upper(), pw.this.text_column)
   )
   
   # Output to another system
   pw.io.csv.write(processed, "output.csv")
   
   # Run the pipeline
   pw.run()
   

   More examples: Pathway Demos.

Final Thoughts

Pathway feels like a pragmatic middle ground between heavyweight stream processors (Flink) and duct-taped Python scripts. If you’re building:

• Real-time dashboards,
• Live feature pipelines for ML,
• or low-latency RAG systems,

it’s worth a look. The Python API keeps things familiar, while the Rust backend handles the heavy lifting.

Downsides? It’s still evolving—some connectors are newer than others, and the docs could use more real-world recipes. But for Python-centric teams needing speed without the JVM tax, Pathway is a solid contender.

Check it out: GitHub | Website