Data Preparation and Processing

In-memory data processing to transform data as it arrives; perform data filtering, data blending and data enrichment at scale, to prepare for analytics and machine learning jobs

Data Cleansing

Minimize data preparation time using various, transformation operators like filtering, imputation and more.

Data Blending

Combine multiple data streams or batch sources into a single stream or table.

Data Blending

Combine multiple data streams or batch sources into a single stream or table.

Data Enrichment

Enhance data with external sources, reference tables and master data repositories, using various lookups, web-services and expressions.

Statistical and Temporal Analytics

In-built operators for complex event processing, aggregation, geo-spatial analytics, correlation and more.

Statistical and Temporal Analytics

In-built operators for complex event processing, aggregation, geo-spatial analytics, correlation and more.

Custom Processing

Use various native Apache Spark and Apache Storm-based operators, and languages including Java, Scala, SQL, Python, for hand-coding any custom logic.

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Resources

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StreamAnalytix Platform Overview

StreamAnalytixâ„¢ enables enterprises to analyse and respond to events in real-time at big data scale using stream processing and machine learning….

Impetus at DataWorks Summit 2018: How a leading bank leveraged Apache Spark and StreamAnalytix to rapidly rebuild their insider threat detection application

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blog Dec 13, 2018

How modern data science is transforming anomaly detection

Real-time anomaly detection has applications across industries. Detecting anomalous patterns in real-time is helping businesses derive actionable insights in multiple…

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