Databricks

Databricks brings all your data into one place for analytics, reporting and AI. It replaces the patchwork of tools most data teams end up juggling. Rockhop helps you set it up and move your data across, whether you're coming off legacy systems or starting fresh.

What is Databricks?

Databricks is a “lakehouse”. That's a data lake (cheap storage for all your raw data) and a data warehouse (clean, structured data ready for reporting) in one platform. So your engineers, analysts and data scientists all work from the same data, instead of copying it between tools.

One Place for All Your Data

Structured or unstructured, it lives together. No more shuffling data between a lake and a warehouse just to get an answer.

Machine Learning Built In

Build, train and run models in the same platform, with MLflow and notebooks your data scientists already know.

Real-Time Analytics

Process streaming data with Apache Spark and Delta Live Tables, so decisions use current numbers rather than yesterday's.

Governance & Security

Unity Catalog controls who can see what, with fine-grained access, data lineage and compliance-ready controls.

AI on Your Own Data

Mosaic AI lets you build and deploy AI models on your governed data. Genie lets people ask questions of that data in plain English.

The Business Impact of Databricks

25%

higher data-scientist productivity

20%

higher data-engineer productivity

40%

less computing time required

65%

faster ETL processing in an enterprise implementation

Already on Microsoft? Databricks Fits

Databricks connects to Azure, Microsoft Fabric and Power BI, so it slots into the stack you already run. Your data strategy stays joined up, not split across tools that don't talk to each other.

Where To Start

Pick the workshop that matches what you're trying to do. Each one solves a real problem and leaves you with something you can use.

Microsoft Fabric Accelerator

See what AI on your own data can do in 2-4 weeks. Rockhop sets up one data source, a live AI agent and a Power BI report.

A working environment built on real data from your own systems

A live AI agent your team can test on your own data, not a demo

A clear answer on what your data needs to support AI at scale

A defined next step, with the evidence to plan and fund it

Explore the Fabric Accelerator

AI Data Readiness Assessment

Fast-Track AI Readiness with a Trusted Data Foundation. This engagement helps organizations get ready to scale AI and Copilot initiatives.

A plain-English readiness score for your data environment

A clear view of the gaps in quality, governance, and architecture

A prioritized action plan, ordered by what to fix first

Clarity on which AI use cases are viable for you right now

Assess Your Data Readiness

AI-Ready Data Foundation

Evaluate, modernize, and govern your data environment to support scalable AI, Copilot, analytics, and machine learning initiatives.

Assess data quality, governance, and accessibility to identify what's AI-ready and what needs improvement.

Establish a Microsoft Fabric environment with the right structure, permissions, and governance controls.

Integrate Fabric and Databricks to support advanced analytics, machine learning, and AI workloads.

Create a roadmap of high-value AI use cases, from Copilot to predictive analytics and beyond.

Build Your AI-Ready Data Foundation

Explore Databricks in Practice

What Our Microsoft Designations Mean for You

Microsoft designations are audited, not bought. Here's what Rockhop's accreditations mean for our clients.

Migrating to Microsoft Power BI Video Guide - Part 1: Business Drivers

Migrating to Microsoft Power BI

Considering migrating to Microsoft Power BI from tools like Click or Tableau? This is a 7-part series exploring the migration journey. Gaurav focuses on key business drivers for migrating to Power BI, both financial and operational.

Building Dashboards the Right Way

Join Julia in this eye opening discussion of data visualization best practices when building dashboards. She discusses how data turns into insights, and how insights further tell stories that help decision makers.

Get more insights

Databricks FAQs

Describe your Databricks services

We handle full setup, configuration, ingestion and implementation. Including workspace provisioning, data integration, security configuration (Unity Catalog, RBAC) and performance tuning - plus knowledge transfer to your team.

How long is a Databricks implementation?

Simple projects can be as short as 5–6 weeks depending on data sources, complexity and governance requirements.

Can you integrate Databricks with our existing data tools?

Yes, we connect to major cloud platforms (e.g. Azure/Fabric), BI tools (e.g. Power BI) and ingestion tools (e.g. Fivetran); as well as legacy on-prem databases through secure connectors.

Can you set up Unity Catalog and data governance?

Absolutely. We configure Unity Catalog for data lineage, access control and masking to meet your compliance and privacy standards.

What level of support do you provide after go-live?

We offer ongoing managed support for performance, cost optimization, new feature enablement and train training.

How do you price Databricks implementation projects?

We scope by complexity of data, data volume, integrations, transformations and governance - with fixed-fee or T&M engagements.

Does Databricks utilize AI?

Yes. Databricks uses AI throughout the Lakehouse to streamline engineering, analytics and automation.

Mosaic AI – provides the foundation for machine learning and generative AI, enabling teams to build, fine-tune and deploy models directly on governed Lakehouse data.
Genie – built on Mosaic AI, is Databricks’ conversational assistant that lets users explore data, run queries and automate tasks using natural language.
Unity Catalog – ensures all AI and data assets are governed with consistent permissions, lineage tracking and data security across the Lakehouse environment.