DataOps platform for faster building and deploying data pipelines

Ingest data from any source into your cloud warehouse, transform using SQL, Python, or Spark and productionize a full end-to-end realtime pipeline and dashboard in minutes.

Sprinkle at a Glance

100 billion +

Rows Ingested/Day

20 k +


5000 +

Real time Pipelines

Designed for constant change

Sprinkle discovers source schema changes and automatically handles heterogeneous schema across different rows.

Just update or add new transformation, the pipeline adapts itself. Sprinkle automates dependency management, assembles the pipeline by discovering the data dependencies. No need to manually manage dependencies and scheduling rules.

No need to define complex failure handling rules. Sprinkle automatically retries failures, identifying and prioritizing the bottlenecks based on transitive data dependencies.

Policies to handle data delays - execute if all sources have refreshed or execute if at least one source is refreshed etc. Provides visibility to identify bottlenecks quickly.

Throttled execution based on configured capacity. Automatic prioritization of latency sensitive nodes in the pipeline.

How it works ?

How Sprinkle Empowers Different Teams

Product & Business Team

Deep Dive into business metrics on your own

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Data Science Team

Data prep pipeline using Jupyter Notebooks

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Data Analyst Team

Integrate, transform & deploy pipelines faster

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Trusted By

and many more