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Just Released: AI Pipelines for Your Enterprise Documents - Try It Out Today!
   
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FAST AND EASY

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.
Get Started Let's talk



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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.
Check out our developer hub
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(
        pw.this.prompt,
    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)
    .select(
        *pw.left,
        **pw.right[["lat", "lng", "alt"]].with_suffix("_1"),
    )
    .join_left(t2_timestamp, pw.left.timestamp == pw.right.timestamp)
    .select(
        *pw.left,
        **pw.right[["lat", "lng", "alt"]].with_suffix("_2"),
    )
)
preview_table(joined_table)

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

Build an LLM App with Pathway
Check It Out

Combining two time series in Pathway
Check It Out

Realtime Server Log Monitoring with Pathway
Check It Out
Testimonials


STORIES FROM OUR USERS

Want to create an #LLM app that can fetch real-time data? Without the need for
vector databases or a complex stack? You should try Pathway.

Streamlit (Snowflake) Team

Pathway’s ability to process real-time data is truly impressive. Thanks to
Pathway we implemented our data pipelines for applied observability in
production. If you’re looking for a top-notch data processing solutions, be sure
to check out Pathway!

Priyam KumarQuestionPro

Kafka and Flink are based on outdated tech which results in underperformance.
Their paradigm simply is not suited to AI/ML use cases. Pathway is fast and
built for the AI era.

Leading Streaming authorEnterprise Kafka Engineer at major retail chain

Going from streaming to batch and vice-versa is cumbersome for Flink or Spark.
You usually end up designing and build from scratch a hybrid infra supporting
both streaming & real-time. Pathway is built to resolve this pain.

Member of Apache Pulsar core teamApache

I’m really into Pathway! It has a way lower learning curve than Flink

Flink developerStaff Data Engineer at a leading mobility scaleup

Pathway corrects the answers of LLM’s in real time! Incrementally as well!

Hubert DulayAuthor of “Streaming Databases”

Here comes an update on the input data source, and we get alerted that the
language model has changed its answer to my question. Just set a web hook,
create a reactive application in Pathway, and never need to care about
synchronizing data again ❤️

Pau Labarto BajoCreator of Real World ML
Pathway for Enterprise brings you
Logistics

Postal Services, 3PL, Container Shipping

Read More Document Search

Live data pipelines for AI apps, LLM-powered analytics

Read More AI-enabled vehicles & eSports

Telemetry tracking, Augmented reality, Realtime monitoring

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What you get with our Enterprise Edition
 * Horizontal Scalability
 * Machine Learning Toolboxes
 * Support with SLA
 * Secure by design

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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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