Azure Synapse: Best for unified data analytics across big data systems and data warehouses. Databricks: Best for use cases such as streaming, machine learning, and data science-based analytics.

For those needing robust ELT, data science, and machine learning features within a data lake/data warehouse framework, Databricks is the winner. Azure ML wins for those just wanting to add ML to ...

In this session, we’ll teach you how to build your own Azure Databricks ETL pipeline, starting with ingestion, moving through transformation, and loading your data into a SQL Data Warehouse.

Databricks has unveiled a new extract, transform, load (ETL) framework, dubbed Delta Live Tables, which is now generally available across the Microsoft Azure, AWS and Google Cloud platforms.

The Azure Databricks service is supposed to give them an easier experience of running big data jobs than rolling their own Spark deployment, and offers a deeper level of compatibility with ...

Because this is essentially a first-party Azure service, users will also get all of the usual Azure SLAs and be able to deliver support to their joint customers — a group of Databricks engineers ...

Striim’s unified platform further allows vector embeddings to be built within the data pipeline while delivering real-time data into Neon and into Databricks for building Agentic AI use cases.

Microsoft Azure customers interested in parsing large amounts of data to improve their businesses will soon be able to use Azure Databricks, developed in consultation with big data startup ...