Pathway - Skip the JVM Bullshit for Data Processing

Most data teams maintain separate batch and streaming codebases because that's what Spark and Flink force you to do. This is expensive as hell - we learned that after months of maintaining two codebases where Spark batch jobs worked fine but Flink kept shitting the bed in production. Never figured out the root cause, just switched frameworks.

Pathway (~42k GitHub stars) was built by researchers who got tired of this dance. Same Python code, different execution modes - batch for historical data, streaming for live updates. No more translating logic between frameworks and wondering why performance characteristics change.

The Architecture Actually Makes Sense

Pathway is built on Differential Dataflow, which is fancy talk for "only recompute what changed." When new data arrives, it doesn't reprocess everything from scratch like Spark does. The Rust engine handles the messy bits - threading, memory management, distributed computation - while you write normal Python.

This matters because most data engineering teams waste months maintaining separate dev/test/prod environments. Your local Jupyter notebook uses Pandas, your CI tests run batch Spark, and production runs Kafka Streams. Three different mental models, three different failure modes. Pathway lets you test locally on CSV files, then deploy the same code to production Kafka - no translation layer bullshit.

The multi-worker deployment is based on that Microsoft Naiad paper everyone cites but nobody reads, which means it's built on actual computer science rather than hacked-together startup code. Each worker runs the same dataflow on different data shards, communicates via shared memory or sockets, and tracks progress efficiently.

The Stuff That Actually Matters

Pathway automatically manages late-arriving data and out-of-order events, which is something you'll appreciate when your Kafka producer decides to shit the bed during dinner. The free version gives you "at least once" processing (good enough for most use cases), while enterprise gets you "exactly once" if you're paranoid about duplicate processing.

Latest version (0.26.1) requires Python 3.10+ and runs on macOS/Linux. Windows users need Docker or WSL - learned this when our Windows intern spent a whole day trying to get it running natively.

The BSL 1.1 license is basically "free for everything except building a competing hosted service." Code auto-converts to Apache 2.0 after four years, so no vendor lock-in concerns. Way better than dealing with Confluent's license drama or Oracle's lawyers.

So how does this compare to what you're already using?

Pathway tries to solve the "one codebase for batch and streaming" problem, which sounds simple until you hit the edge cases. It works well for standard data transformations and shines on graph algorithms, but there are gaps you should know about.

Connectors: The Good and The "You'll Figure It Out"

Pathway has native connectors for the usual suspects: Kafka, PostgreSQL, S3, and some business tools like Google Drive and SharePoint. The SharePoint connector is behind a license key, which you'll discover when you try to use it.

They also claim Airbyte integration for "300+ data sources," but this means running Airbyte alongside Pathway. Not exactly seamless - you're maintaining two systems instead of one. The custom connector API is Python-based, so at least you won't be writing Java if something's missing.

Reality check: The connector ecosystem is decent but not comprehensive. Plan on writing custom integration code if you're pulling from obscure internal systems or legacy databases. The community is small, so don't expect extensive third-party connectors.

Transformations and Processing

Pathway handles joins, windows, and group-by operations without the usual performance penalty. The Rust engine does the heavy lifting while you write normal Python - no need to learn Rust.

You can use any Python library inside your Pathway pipelines - scikit-learn, numpy, pandas - whatever. Since it's just Python, your existing data science code mostly drops in without rewrites.

What actually works well: Joins, group-by operations, and window functions perform as advertised. SQL API exists but you'll probably stick with Python for complex logic. Async transformations let you call external APIs without blocking the whole pipeline.

AI and LLM Integration

The LLM integration actually works, which is more than I can say for most AI frameworks. Includes the usual suspects - document parsers, embeddings, vector search - but the real-time document syncing is where it shines.

Works with LlamaIndex and LangChain out of the box. The templates are actually useful starting points, not empty boilerplate - they include real RAG setups that handle document updates without rebuilding your entire index.

The vector search capabilities handle real-time document indexing, which beats static vector databases when your docs actually change. Works with OpenAI embeddings, Hugging Face models, and most other embedding providers you're already using.

Persistence and Fault Tolerance

The persistence actually works (shocking, I know) - I've had workers crash and restart without losing state. Took a couple tries to get the checkpoint config right, but once I figured it out, it was solid.

So what questions do you actually have about using this thing?

Installing Pathway looks easy in the docs. In reality, prepare for some dependency conflicts. Here's what actually happens when you try to install and deploy this thing.

Installation and System Requirements

Installation is pip install pathway but prepare for the inevitable dependency conflicts. The base install works most of the time, but if you need the LLM extensions, expect to debug some version mismatches. Works on macOS/Linux - Windows users get to mess around with Docker or WSL.

If you need the AI stuff, try pip install pathway[xpack-llm] - though you might hit some dependency hell with transformers and torch versions. The base package is around 200MB because it includes the Rust runtime.

Development Workflow

The "same code everywhere" promise mostly holds up, which is refreshing after years of Spark promising the same thing and failing. Your Python code works for local testing, batch processing, and streaming without the usual "oh shit, the streaming API is completely different" surprise you get with other frameworks.

It comes with a built-in monitoring dashboard that actually works (shocking for a data framework). Shows message throughput, latency, and error counts without forcing you to set up Prometheus or Grafana just to see what's broken.

Development reality: The monitoring is basic but functional - shows message rates, processing latency, and error counts. For serious production monitoring, you'll probably still want to integrate with your existing observability stack via OpenTelemetry or custom metrics. The dashboard is useful for "why is this slow" debugging but don't expect Grafana-level sophistication.

Ready-to-Use Templates and Examples

They've got ready-to-run templates for common stuff - ETL pipelines, event-driven systems, LLM applications with RAG. Available as Jupyter notebooks or Docker containers so you can mess around locally or copy-paste into production (we've all done it).

The examples repo has more code samples if you want to see how specific features work. Useful for figuring out how joins, windowing, and connectors actually work instead of guessing from the docs.

Deployment and Scaling Options

For local testing you can run scripts directly with Python or use pathway spawn if you want better threading control. Production deployment gets more interesting.

Production means Docker containers and Kubernetes if you want to do it right. They support Render, AWS ECS, Google Cloud Run, and Azure Container Instances deployments with varying degrees of "just works" vs "prepare to debug networking issues."

Real deployment reality: The Docker images are huge because they pack the Rust runtime. If you're used to 200MB Python containers, prepare for 2GB+ base images. Kubernetes deployments need stateful sets - don't try running this as stateless pods unless you enjoy losing data. Each worker needs persistent volumes for checkpointing, and the disk I/O requirements are higher than you might expect.

Enterprise features cost money but get you distributed computing, better persistence options, and exactly-once processing if your compliance team insists on it. For most use cases the free version works fine unless you're processing terabytes per day.

Need more resources? Here's the essential stuff for getting productive with Pathway.