Stream Data Processing, Complex Event Processing - StreamAnalytix

Data 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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blog Dec 07, 2020

Essentials for an enterprise data science solution

Data science solutions enable enterprises to explore their data, develop machine learning (ML) models, and operationalize them to drive business outcomes.

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