Data Engineer

Different components lead to complexity. But if you combine capabilities, then you can build and manage data pipelines to get data where it’s needed, when it’s needed.


Integrating diverse data types, complex functions, and ML creates manual processes that are too slow to meet your SLAs, while lack of visibility into data flow and lineage makes outcomes tough to trace.

Challenges include: 

  1. Manual deployment of data pipelines
  2. Inability to track data lineage across data pipelines
  3. Inability to track which version of pipeline and functions affected which data outcomes; lack of visibility into data flow across pipelines
  4. Difficulty integrating diverse data types, complex functions, and ML analytics
  5. Unsecured data access
  6. Difficulty deploying ML models into production against streaming data, as they can’t calculate features just-in-time
  7. Complex to implement and use; slow process


With the Kinetica Streaming Data Warehouse, you can combine streaming and historical data with location intelligence, graph, and ML in one platform, with standard tools like SQL and REST APIs, to build more complex pipelines without the hassle and time sink of integrating components--and troubleshooting them later.

Benefits include: 

  1. Simplified pipeline combines batch, streaming, graph, location, and ML
  2. Easier troubleshooting, from tracking ML inference lineage to auditing to versioning to pre-built models to role-based access control
  3. Single platform with standard tools

Ready to find out more?