What are common use cases for Data Flow Platform
Example use cases for Data Flow Platform
Stream Data Ingestion
Ingest data in real-time as they arrive. Good for real-time data-driven decision processing for improving customer experience, minimizing fraud, and optimizing operations and resource utilization.
Bulk Data Ingestion
Ingest blocks of data that have already been stored over a period of time. It is often used when dealing with huge amounts of data and/or when data sources are legacy systems that cannot deliver data in streams.
Bulk Ingestion is suitable when:
Data freshness is not a mission-critical issue
You are working with large datasets and are running a complex algorithm that requires access to the entire batch – e.g., sorting the entire dataset.
You get access to the data in batches rather than in streams
When you are joining tables in relational databases
Replicate data from one data repository to another data repository. For example, replicate MySQL data to Postgres in real-time using Change Data Capture.
Data Synchronization between multiple data centers/clouds
Synchronize datasets from one data repository to another or between multiple data centers or Clouds.
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