What Is Databricks?
Databricks is a cloud-based platform for data engineering, analytics, and AI, built around the data lakehouse architecture its founders helped create. It brings the pieces of a modern data stack, data processing, storage, governance, SQL analytics, and machine learning, into one environment, so teams can move from raw data to models and dashboards without stitching together separate tools. It is built on Apache Spark, the open-source processing engine its creators also originated, and it runs on all the major clouds.
The Lakehouse Foundation
Databricks is closely tied to the lakehouse, the architecture that combines the low cost and openness of a data lake with the reliability and governance of a data warehouse. Rather than keeping a separate lake for data science and a warehouse for reporting, Databricks aims to serve both from one governed copy of the data in open formats. This is the core idea behind the platform: one foundation that supports engineering, analytics, and AI rather than separate silos for each.
What draws teams to Databricks is usually not a single feature. It is that the engineering, the analytics, and the machine learning can sit on one governed copy of the data, which tends to reduce the copies and handoffs that quietly create most of the inconsistency we see.
Marla Nelson, CTO
What Databricks Includes
The platform spans several capabilities on a shared foundation:
- Data engineering and ETL, powered by Apache Spark.
- Delta Lake, the open table format that adds reliability to lake storage.
- Unity Catalog for governance, security, and lineage across data and AI assets.
- Databricks SQL for warehouse-style analytics and BI.
- Machine learning and AI tooling, including support for building and serving models.
Because these share one platform and one copy of the data, work can move between them without exporting and re-importing.
Databricks and Microsoft Fabric
Databricks and Microsoft Fabric are often weighed against each other, and both are credible foundations for enterprise analytics and AI. They take different starting points: Databricks grew from data engineering and machine learning and offers deep control over processing and ML, while Fabric grew from the Microsoft and Power BI world and offers a tightly integrated, managed experience for organizations already standardized on it. Neither is universally better. The right choice depends on the workload, the team’s skills, the existing investment in Azure and Power BI, and how much of the stack a company wants to manage itself. It is a fit decision, not a ranking, and the cost comparison in particular is workload-dependent rather than fixed in either platform’s favor.
Databricks and Snowflake
Databricks is also frequently compared to Snowflake. Both are major cloud data platforms, and their capabilities have converged over time. Broadly, Databricks started from data engineering and machine learning on open formats, while Snowflake started from cloud data warehousing and SQL analytics, and each has expanded toward the other. The decision again comes down to workload and fit rather than a clear winner.
Where Databricks Fits an ERP Reporting Stack
For companies bringing ERP data into analytics, Databricks can serve as the lakehouse foundation that consolidates and governs the data before it reaches Power BI or an AI model. As with any platform, the value is less the platform itself than what sits on it: a clean, governed model with the business logic built. QuickLaunch builds that governed foundation on the lakehouse for JD Edwards, Vista, NetSuite, and OneStream, so teams reach trustworthy reporting and AI-ready data in weeks rather than assembling the platform by hand.
Frequently Asked Questions
What is Databricks used for?
Data engineering, analytics, and machine learning on a single platform built around the lakehouse. Teams use it to process and govern large datasets, run SQL analytics, and build and serve AI models from one governed copy of the data.
What is the difference between Databricks and Snowflake?
Both are major cloud data platforms whose capabilities have converged. Databricks started from data engineering and machine learning on open formats; Snowflake started from cloud data warehousing and SQL. The right choice depends on the workload and fit rather than a clear winner.
What is the difference between Databricks and Microsoft Fabric?
Both are credible foundations for analytics and AI. Databricks offers deep control over processing and machine learning; Fabric offers a tightly integrated, managed experience for organizations standardized on Microsoft and Power BI. It is a fit decision, and the cost comparison is workload-dependent.
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