Pathway: The Revolutionary Python ETL Framework That's Transforming Real-Time Data Processing with 51k+ GitHub Stars

In the rapidly evolving landscape of data processing and real-time analytics, a new player has emerged that's capturing the attention of developers worldwide. Pathway, a Python ETL framework with over 51,000 GitHub stars, is revolutionizing how we approach stream processing, real-time analytics, LLM pipelines, and RAG (Retrieval-Augmented Generation) systems.

Unlike traditional data processing frameworks that force you to choose between batch and streaming, Pathway offers a unified approach that handles both seamlessly. Built with a powerful Rust engine under the hood, it combines Python's ease of use with Rust's performance, making it an ideal choice for modern data-driven applications.

What Makes Pathway Revolutionary?

Pathway stands out in the crowded field of data processing frameworks for several compelling reasons:

🚀 Unified Batch and Streaming Processing

One of Pathway's most significant advantages is its ability to handle both batch and streaming data with the same codebase. This means you can develop locally with batch data, test with historical datasets, and deploy to production with streaming data without changing a single line of code.

⚡ Rust-Powered Performance

While you write your logic in Python, Pathway's execution engine is built in Rust using Differential Dataflow. This architecture enables:

• Multithreading and multiprocessing capabilities
• Distributed computations
• Incremental computation for optimal performance
• Memory-efficient processing

🔗 Extensive Connector Ecosystem

Pathway comes with built-in connectors for popular data sources including:

• Apache Kafka
• PostgreSQL
• Google Drive
• SharePoint
• 300+ additional sources via Airbyte integration

🤖 Native LLM and RAG Support

With dedicated LLM tooling, Pathway makes it incredibly easy to build live LLM and RAG pipelines. It includes:

• LLM wrappers for major services
• Real-time Vector Index
• Integrations with LangChain and LlamaIndex
• Document parsers and embedders

Getting Started with Pathway

Installation

Getting started with Pathway is straightforward. It requires Python 3.10 or above and can be installed via pip:

pip install -U pathway

Note: Pathway is available on macOS and Linux. Windows users should use a Virtual Machine or WSL.

Your First Pathway Application

Let's start with a simple example that demonstrates Pathway's core concepts. This example computes the sum of positive values in real-time:

import pathway as pw

# Define the schema of your data (Optional but recommended)
class InputSchema(pw.Schema):
    value: int

# Connect to your data using connectors
input_table = pw.io.csv.read(
    "./input/",
    schema=InputSchema
)

# Define your operations on the data
filtered_table = input_table.filter(input_table.value >= 0)
result_table = filtered_table.reduce(
    sum_value=pw.reducers.sum(filtered_table.value)
)

# Load your results to external systems
pw.io.jsonlines.write(result_table, "output.jsonl")

# Run the computation
pw.run()

This simple example showcases several key Pathway concepts:

• Schema Definition: Optional but helps with type safety
• Connectors: Easy data ingestion from various sources
• Transformations: Familiar operations like filter and reduce
• Output: Flexible data export options

Advanced Use Cases and Examples

Real-Time Analytics Pipeline

Here's a more complex example that demonstrates real-time analytics with windowing:

import pathway as pw
from datetime import timedelta

class SensorData(pw.Schema):
    timestamp: int
    sensor_id: str
    temperature: float
    humidity: float

# Read streaming sensor data
sensor_stream = pw.io.kafka.read(
    rdkafka_settings={
        "bootstrap.servers": "localhost:9092",
        "group.id": "sensor-analytics",
        "auto.offset.reset": "latest"
    },
    topic="sensor-data",
    schema=SensorData,
    format="json"
)

# Create sliding windows for analysis
windowed_data = sensor_stream.windowby(
    sensor_stream.timestamp,
    window=pw.temporal.sliding(timedelta(minutes=5)),
    behavior=pw.temporal.exactly_once_behavior()
)

# Calculate statistics per sensor per window
stats = windowed_data.reduce(
    sensor_id=pw.this.sensor_id,
    avg_temp=pw.reducers.avg(pw.this.temperature),
    max_temp=pw.reducers.max(pw.this.temperature),
    min_humidity=pw.reducers.min(pw.this.humidity),
    count=pw.reducers.count()
)

# Alert on anomalies
anomalies = stats.filter(
    (stats.avg_temp > 35.0) | (stats.min_humidity < 30.0)
)

# Output results
pw.io.kafka.write(stats, kafka_settings, "sensor-stats")
pw.io.kafka.write(anomalies, kafka_settings, "sensor-alerts")

pw.run()

Building a RAG Pipeline

Pathway excels at building RAG (Retrieval-Augmented Generation) systems. Here's an example of a live document processing pipeline:

import pathway as pw
from pathway.xpacks.llm import embedders, llms, parsers

# Monitor a folder for new documents
documents = pw.io.fs.read(
    "./documents",
    format="binary",
    mode="streaming"
)

# Parse documents (supports PDF, DOCX, TXT, etc.)
parsed_docs = documents.select(
    content=parsers.ParseUnstructured(pw.this.data)
)

# Split into chunks
chunked_docs = parsed_docs.select(
    chunks=parsers.chunk_texts(pw.this.content)
).flatten(pw.this.chunks)

# Generate embeddings
embedder = embedders.OpenAIEmbedder()
embedded_docs = chunked_docs.select(
    chunk=pw.this.chunks,
    embedding=embedder(pw.this.chunks)
)

# Create vector index
index = embedded_docs.build_index(
    embedded_docs.embedding,
    n_dimensions=1536
)

# Query function
def query_documents(query: str, k: int = 5):
    query_embedding = embedder(query)
    results = index.get_nearest_items(
        query_embedding, 
        k=k, 
        metadata_filter=None
    )
    return results

# Set up LLM for generation
llm = llms.OpenAIChat(model="gpt-4")

# Create RAG pipeline
def rag_pipeline(question: str):
    # Retrieve relevant documents
    relevant_docs = query_documents(question)
    
    # Create context from retrieved documents
    context = "\n".join([doc.chunk for doc in relevant_docs])
    
    # Generate response
    prompt = f"""Based on the following context, answer the question:
    
    Context: {context}
    
    Question: {question}
    
    Answer:"""
    
    response = llm(prompt)
    return response

pw.run()

Key Features and Capabilities

Stateful and Stateless Transformations

Pathway supports both stateful operations (joins, windowing, sorting) and stateless transformations. Many operations are implemented directly in Rust for optimal performance, while you can also use any Python function or library.

Persistence and Fault Tolerance

Pathway provides built-in persistence to save computation state, allowing you to restart pipelines after updates or crashes. The free version offers "at least once" consistency, while the enterprise version provides "exactly once" consistency.

Time Management and Consistency

Pathway automatically handles time management, ensuring all computations remain consistent. It manages late and out-of-order data points by updating results when new data arrives.

Monitoring and Observability

Pathway includes a built-in monitoring dashboard that tracks:

• Message counts per connector
• System latency
• Log messages
• Pipeline health metrics

Deployment Options

Local Development

For local development, simply run your Pathway script like any Python application:

# Basic execution
python main.py

# With Pathway's spawn command
pathway spawn python main.py

# Multi-threaded execution
pathway spawn --threads 3 python main.py

Docker Deployment

Pathway provides official Docker images for easy containerization:

# Using Pathway's official image
FROM pathwaycom/pathway:latest

WORKDIR /app
COPY requirements.txt ./
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

CMD ["python", "./your-script.py"]

Kubernetes and Cloud

Pathway containers are ideal for Kubernetes deployment. The enterprise version offers distributed computing capabilities and external persistence setup for production-scale deployments.

Performance and Benchmarks

Pathway is designed to outperform established technologies like Apache Flink, Spark, and Kafka Streams. Its Rust-based engine enables:

• Superior throughput for streaming workloads
• Lower latency for real-time processing
• Efficient memory usage
• Support for complex algorithms not readily available in other streaming frameworks

The framework particularly excels at temporal joins, iterative graph algorithms, and machine learning routines in streaming mode.

Integration Ecosystem

Pathway has established partnerships and integrations with leading data and AI companies:

• LangChain: Native integration for agent engineering
• LlamaIndex: Support for context-aware AI agents
• MinIO: High-performance object storage integration
• Redpanda: Streaming data platform compatibility
• Databento: Market data processing capabilities

Real-World Use Cases

Financial Services

• Real-time fraud detection
• Algorithmic trading systems
• Risk management pipelines
• Market data analysis

IoT and Manufacturing

• Sensor data processing
• Predictive maintenance
• Quality control monitoring
• Supply chain optimization

AI and Machine Learning

• Real-time RAG systems
• Live model inference
• Feature engineering pipelines
• A/B testing frameworks

Getting Started: Next Steps

Ready to dive deeper into Pathway? Here are your next steps:

1. Explore Templates: Visit Pathway's template gallery for ready-to-run examples
2. Read Documentation: Comprehensive guides available at pathway.com/developers
3. Join the Community: Connect with other developers on Discord
4. Try Examples: Start with the GitHub examples repository

Conclusion

Pathway represents a significant leap forward in data processing frameworks. By unifying batch and streaming processing, providing native LLM support, and delivering Rust-level performance with Python simplicity, it addresses many of the pain points developers face when building modern data applications.

Whether you're building real-time analytics dashboards, implementing RAG systems, or processing IoT sensor data, Pathway provides the tools and performance you need to succeed. With over 51,000 GitHub stars and growing adoption across industries, Pathway is quickly becoming the go-to choice for developers who need both power and simplicity in their data processing pipelines.

The framework's commitment to developer experience, combined with its enterprise-grade performance and scalability, makes it an excellent choice for both startups and large enterprises looking to modernize their data infrastructure.

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