Introductory Guide

What is Databricks? A Comprehensive Platform Overview

Published on July 6, 2026 | Written by Mr. Shifa ur rehman jamali

As modern businesses process terabytes of data, maintaining separate databases for business intelligence (BI) and machine learning (ML) has become a major roadblock. Enter the Lakehouse.

1. The Core Lakehouse Concept

Historically, corporations separated their database infrastructure into two distinct halves: a data lake (cheap, scalable storage for raw files) and a data warehouse (highly indexed, SQL-queryable structure for BI).

This model created duplicates, synchronization issues, and heavy maintenance overhead. The Lakehouse architecture combines these two halves by layering transactional guarantees (ACID features) directly onto files stored in raw object containers.

2. Spark Engine, DLT, and Unity Catalog

At the center of this platform is Apache Spark, a distributed computing engine that allows data transformations to run in parallel across massive server arrays.

• Delta Lake: An open storage framework providing versioning, ACID transactions, and file organization features.
• Unity Catalog: A centralized platform for auditing table access, column-level security, and machine learning models.
• Lakeflow Declarative Pipelines (formerly Delta Live Tables / DLT): A declarative framework for automating batch and streaming data transformations.

3. Decoupling Compute and Storage for Cost Efficiency

A key differentiator of the Lakehouse platform is the absolute isolation of storage and compute. Files are saved in cheap public storage containers (like AWS S3 or Azure ADLS). Computational clusters are booted up and scaled dynamically to query those files, shutting down immediately when done.