Unified Batch & Streaming

Ascend Use Case

Unified Batch & Streaming

Ascend orchestrates batch and streaming data with a single architecture that results in the continuous enrichment of datasets at the point of usage. This unified approach eliminates the need for separate infrastructures and tools, resulting in significant savings in cost and time.

Reduction in Code

Simply specify your data sources, transforms, and destinations. Ascend handles the rest.

Automation

for common problems with streaming architectures (i.e. automatically handles late data)

Cut Maintenance

by eliminating parallel architectures. Spend more time building, less time plumbing pipelines.

How it Works

  • Connect directly to any stream (Kafka, Kinesis, etc.) and Ascend will automatically collect and persist the data
  • Specify streaming data transformation logic the same as you would for batch with SQL, Python, Java, or Scala
  • Ascend automatically processes and aggregates new data in time window optimized micro-batches
  • Unify with your historical and batch data using time window optimized joins, and Ascend ensures your pipeline runs efficiently
  • Write the resulting data directly to a data warehouse, blob store, and more

What to Expect