Databricks

Azure Databricks isn't a database in the traditional sense — it's a processing and analytics platform built on Apache Spark that reads and writes data stored elsewhere (in a data lake, a warehouse, or blob storage) and gives data engineers, data scientists, and analysts a shared environment to work in. Its storage layer, Delta Lake, adds ACID transactions and unified metadata on top of raw files, so batch and streaming data can be handled through the same tables instead of separate pipelines. A query engine called Photon, written in C++, handles the actual SQL and DataFrame processing underneath.

Governance is centralized through Unity Catalog, which controls who can access what across the whole platform rather than leaving permissions scattered across individual notebooks or clusters. Compute itself is serverless and autoscaling — you don't provision or size a cluster ahead of time, and usage is billed in Databricks Units on a per-second basis, with discounted pricing available for organizations willing to commit to one- or three-year terms.

Because it's built to handle large volumes of data efficiently, Databricks tends to be the platform of choice for heavy ETL pipelines, real-time streaming analytics, predictive modeling and machine learning work, and — increasingly — the model training and fine-tuning work behind generative AI projects.

How AzureForge AI uses Databricks

We bring in Databricks specifically for the large-scale ETL rebuilds that come up when a client's data volume has outgrown what their existing pipelines were designed for — most often when we're replacing legacy SQL Server Integration Services (SSIS) and Reporting Services (SSRS) packages that were built years ago and can no longer keep up with the current data volume or the reporting demands placed on them. It gives us a single platform to rebuild that processing logic on, instead of patching the old packages further.