Data Cleansing
Minimize data preparation time using various, transformation operators like filtering, imputation and more.
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
Minimize data preparation time using various, transformation operators like filtering, imputation and more.
Combine multiple data streams or batch sources into a single stream or table.
Combine multiple data streams or batch sources into a single stream or table.
Enhance data with external sources, reference tables and master data repositories, using various lookups, web-services and expressions.
In-built operators for complex event processing, aggregation, geo-spatial analytics, correlation and more.
In-built operators for complex event processing, aggregation, geo-spatial analytics, correlation and more.
Use various native Apache Spark and Apache Storm-based operators, and languages including Java, Scala, SQL, Python, for hand-coding any custom logic.
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…