Data Cleansing
Minimize data preparation time using various, transformation operators like filtering, imputation and more.
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 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.
Resources
white paper
Streaming analytics: Market research and trends
Explore the growth of the streaming analytics space, its leading players, and business drivers in this market update, published by Bloor Research.
blog Apr 18, 2022
Drag-and-drop Data Pipelining: The Next Disruptor in ML
Recent advances in ML and AI technologies are helping enterprises across industries quickly move from their use cases from the pilot stage to production and operationalization.
