Don’t dump data. Build better pipelines.

Extract, Transform and Load — this process dumps valuable data in a lake. Dump, pull, transform makes data difficult to manage, duplicative and unreliable. Modernize this process by handling data as it is created (in a stream), processing it in-memory, and keeping only clean, orderly data. Future users will thank you.

You need to know when data changes at the source so you can take action — whether to modify a database, automate new responses or change the way you process it. Using ELT to sync data stores doesn’t cut it. But with stream processing, you’ll immediately and reliably recognize data changes and be ready to act on new variables.

Much of the data stored in warehouses is hidden deep in the cloud — so logging into your bank for a transaction history will take time to query, retrieve and serve. Why not do it better? Recognize customers and use ML to predict what that customer is likely to query. Then selectively pre-load data to serve it immediately, reducing latency.

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Build better business intelligence dashboards using the streaming infrastructure. Aggregate data from any source, layer on business logic, then easily query to generate high-value reports. Now you can report on-demand or on any schedule, so reporting is always current in real time. This automation regardless of when or how often data arrives.

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Empower your data scientists to self-serve streaming data. Let them explore, build, test, deploy and monitor their ML models without (much) support from you. Just create a sandboxed workspace, connect it to raw data streams, and off they go. You can also hook their real-time model results to production to connect them to your data consumers.

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