The data lake vs data warehouse question is constantly evolving. A data warehouses holds structured data while data lakes hold unstructured data is quickly breaking down. Both have expanded their capabilities to support what the other does.

As data needs have changed, standards for data freshness and latency have come closer to real time. In industries like finance, ecommerce, and manufacturing, we’re going to see the rise of solutions that reduce the complexity of writing code to build real-time streaming pipelines.

Reducing the risks, costs, and headaches from traditional sharing methods, the feature allows organizations to share read-only database objects with other organizations without transferring the actual data.

A huge benefit is the separation of storage and compute. When an object is shared with a data consumer, it remains within the provider’s account, and the consumer incurs compute costs for querying the data, but no storage costs. 

While zero ETL promises to free us up to focus on tasks like data analysis and interpretation rather than data preparation, it’s unclear whether it ends up just a gimmick with limited use.

Data warehouses are becoming directly involved in not just storing data but also in processing and deriving insights through AI and ML models.

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