Goodbye, data lake: Why continuous analytics yield higher ROI

Description

Faced with the need to handle increasing volumes of data, alternative datasets (“alt data”), and AI, many enterprises are working to design or redesign their big data architectures. One traditional approach is to store everything in a data lake, process it via ETL, and analyze it in batch or interactive modes. However, this decade-old approach often fails to generate sufficient ROI.

Yaron Haviv argues for a different approach, in which information is ingested, enriched, and analyzed in context as it arrives, including via machine learning or deep learning, and is then immediately made available to users or to drive automated actions. He explains why it’s possible to take full advantage of modern hardware, microservices, and serverless functions to achieve much higher performance while still benefitting from CI/CD, autoscaling, and fast software rollouts. The resulting “continuous analytics” solutions yield faster answers for the business while remaining simpler and less expensive for IT.