Data analytics & warehousing

Both platforms below are described from real, hands-on Azure delivery. The same category of tooling exists on AWS (EMR, Redshift) and Google Cloud (Dataproc, BigQuery) — the architectural reasoning for when a client actually needs this layer, versus when a simpler warehouse is enough, doesn't change by vendor.

Spark-based processing — 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.

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 — nothing is provisioned or sized ahead of time. Because it's built to handle large volumes of data efficiently, it 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 CloudCompass AI uses it

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.

Unified data warehouse — Synapse Analytics & Microsoft Fabric

Azure Synapse Analytics is a platform-as-a-service built from modular, independently controlled components: dedicated SQL pools for data warehousing, Spark pools for large-scale processing, and pipelines for orchestration — each billed separately. Microsoft Fabric folds that same set of capabilities into one interconnected, capacity-billed platform rather than a set of separately provisioned services. The piece that actually changes the architecture is OneLake: a single, unified data lake built into every Fabric tenant that every workload reads from and writes to, so a data engineering job, a warehouse query, and a BI report can all operate on the same copy of data instead of each maintaining its own.

Microsoft has been explicit that Fabric is where its own investment is going — but Synapse isn't being deprecated, and an existing implementation that's performing well doesn't need to move on that basis alone. A common and practical middle path is hybrid: new analytics work built on Fabric, while a working Synapse environment stays in place and gets connected into OneLake through a shortcut rather than being torn out and rebuilt.

How CloudCompass AI approaches this

For a client already running an existing warehouse successfully, we don't recommend migrating to a newer platform just because it exists — we look at whether a unified data lake actually solves a real problem the client has, like teams working from different copies of the same data across multiple tools. For a new analytics build, we default to Fabric, since that's where Microsoft's platform investment and new capability releases are landing.