Data and analytics engineering

Pipelines, time-series stores and the query layer that makes them useful.

Ingestion, storage and query are three different problems and most stacks get one of them wrong. We have built pipelines for IoT telemetry, payment analytics and fraud detection, including the natural-language layer on top when that is what the product needs.

What you get

  • ETL and streaming ingestion pipelines
  • Time-series modelling, indexing and retention
  • Analytics query layers and rollups
  • Natural-language query over your own data
  • Data pipelines feeding ML models

Related work

Tell us what you are trying to build

Describe the system and the constraint you have hit. You will get a technical reply, not a sales sequence.