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
blog Nov 24, 2022
ETL vs ELT: Which data integration practice is right for you
ETL and ELT are common data integration processes that are extensively used in data science and business intelligence. While leading…