Data Integration for Accelerating DataOps - StreamAnalytix ETL Platform

Enable your data-flow journey with self-service data processing, machine learning, and DataOps

StreamAnalytix – a self-service ETL platform enables end-to-end data ingestion, enrichment, machine learning, action triggers, and visualization.

 

Learn how you can visually design and manage Spark-based workflows using StreamAnalytix on popular cloud platforms like AWS, Azure, and Databricks.

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Build your data pipelines in minutes

Cloud and on-premise

Ensure automated translation with an innovative engine

Significant productivity gains

Business-as-usual on existing systems while validating new workflows

Low code

Prioritize transformation candidates with an in-built assessment

Machine learning and analytics

Reduced costs with significantly higher ROI

Why StreamAnalytix is the best tool for handling your ETL workloads?

Faster business insights

Rapidly develop and analyze high-performance ETL workloads leveraging a no/low-code, intuitive UI

Next-gen data-prep and profiling

Get a deeper understanding of your data while you work on it

Simplified data flows

Set up data flows in minutes; customize, enrich, and transform data on-the-go, even before it hits your data store

Future-proof your business

Stay ahead of the curve with a modern ETL platform based on the latest open source technologies

Transparent data processing

Get complete visibility of data pipelines with a thoughtfully designed visual canvas

Data quality and enrichment

Enrich your data using proven statistical and machine learning methodologies

DataOps

Process data over the stream before it hits the target with in-memory data processing

  • Data cleansing

    Cleanse your data using the pre-built operators and functions like filtering, imputation, and more to save time

  • Data blending

    Combine data from multiple sources, be it streaming or batch data into a single pipeline

  • Data enrichment

    Enhance data with external sources, reference tables, or master data repositories

  • Data processors

    Design and save a process to replicate and use for other pipelines

  • Advanced data analytics and machine learning

    Use in-built operators and libraries for machine learning, complex event processing, aggregation, geo-spatial analysis, and more

Powerful in-built ETL applications

Automated ingestion application

Use automated in-built ingestion application to load data from any source to your destination in few clicks

Automated ETL migration

The auto-migration utility makes it easy to migrate all your legacy ETL workloads by preserving the structure, logic, and execution rules in easy step-wise process

Change Data Capture

Capture changes from any database or non-database source, transform and enrich the data in motion, and stream changes to a data warehouse

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