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Event-Processing Engine for Your AI
Build blazing fast stream processing pipelines in Python.
Serve real-time features, alerts, and AI-friendly indexes.
Transform unstructured data with Machine Learning.
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The comprehensive framework

Real-time Operational Analytics

Live Data
Data tablesLive events dataLive transaction recordsUser inputsLogistics & moving asset dataIoT dataSupply chain plansLive sales dataMore!
Value delivered in real time
AI-powered insights from LLMAnomalies detectedRecommendationsData harmonization and enrichmentAlertsActionable insightsForecastsInteractive scenario simulationsMore!
With Pathway

Deliver value through code,
not plumbing

Pathway gives you dramatic ease of interactive development, in streaming and batch.Made with for Python & ML/AI developers, Pathway is a data processing framework which enables rapid prototyping, working in notebooks, and containerized deployment for production at scale.
Connect live data sources.Connect multiple data sources - Kafka, API's, S3, local files and cloud folders, and databases. The Pathway engine performs incremental computing as input data changes, making sure your outputs are always up to date.
Process your data with high throughput, low latency, and guaranteed consistency.Pathway runs your Python data pipeline with the fastest Rust runtime on the market. It is based on Differential Dataflow and results of proprietary research. It also allows you to seamlessly integrate with Python Machine Learning libraries, use LLM's, and call into synchronous and asynchronous API's.
See our latest stream processing benchmarks.
Go beyond the ordinary.Tackle diverse tasks like time series analysis, anomaly detection with alerting, graph exploration, and more. All within a flexible and intuitive Python framework.
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Easy development

For your Live Data UseCase

index = KNNIndex(enriched_documents, d=embedding_dimension)
query_context = index.query(query, k=3).select(
    pw.this.query, documents_list=pw.this.result
prompt = query_context.select(
    prompt = build_prompt(pw.this.documents_list, pw.this.query)
model = OpenAIChatGPTModel(api_key=api_key)
responses = prompt.select(
    query_id = pw.this.id,
    result = model.apply(
    locator = model_locator,
            temperature = temperature,
        max_tokens = max_tokens,
Build an LLM App with Pathway
joined_table = (
    merged_timestamps.join_left(t1_timestamp, pw.left.timestamp == pw.right.timestamp)
        **pw.right[["lat", "lng", "alt"]].with_suffix("_1"),
    .join_left(t2_timestamp, pw.left.timestamp == pw.right.timestamp)
        **pw.right[["lat", "lng", "alt"]].with_suffix("_2"),
Combining two time series in Pathway
import pathway as pw
log_table = pw.connector(input_log_stream)
ts_table = log_table.reduce(ts = pw.reducers.max(pw.this.ts))
def sliding_window(log_table, ts_table, length):
	t_sliding_window = log_table.filter(pw.this.ts >= ts_table.ix_ref().ts - length)

return t_sliding_window

t_sliding_window = sliding_window(log_table, ts_table, 5*60)
t_alert = t_sliding_window.reduce(count=pw.reducers.count())
t_alert = t_alert.select(
alert=pw.this.count >= alert_threshold
Realtime Server Log Monitoring with Pathway

Stories from our Users

Pathway for Enterprise brings you
What you get with our Enterprise Edition
  • Horizontal Scalability
  • Machine Learning Toolboxes
  • Support with SLA
  • Secure by design
We are featured in Gartner
Featured in Gartner's Market Guide

Market Guide for Event Stream Processing

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Gartner's Representative Vendor

Market Guide for Data Analytics and Intelligence Platforms in Supply Chain

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