“We needed a partner who understood both the technical depth of Azure OpenAI and the governance requirements of an enterprise.”
Microsoft Official Curriculum
Role-Based Certification PrepTrack: DP-3011Official Source: Microsoft Learn
MicrosoftIntermediate
Implement a Data Analytics Solution with Azure Databricks
This course explores how to use Databricks and Apache Spark on Azure to take data projects from exploration to production. You’ll learn how to ingest, transform, and analyze large-scale datasets with Spark DataFrames, Spark SQL, and PySpark, while also building confidence in managing distributed data processing. Along the way, you’ll get hands-on with the Databricks workspace—navigating clusters and creating and optimizing Delta tables. You’ll also dive into data engineering practices, including designing ETL pipelines, handling schema evolution, and enforcing data quality. The course then moves into orchestration, showing you how to automate and manage workloads with Lakeflow Jobs and pipelines. To round things out, you’ll explore governance and security capabilities such as Unity Catalog and Purview integration, ensuring you can work with data in a secure, well-managed, and production-ready environment.
Duration
1 day
Level
Intermediate
Format
Virtual, On-site, or Hybrid
Language
English
Executive brief for stakeholdersDownload Program Brief (PDF)
Microsoft
DataIT
Implement a Data Analytics Solution with Azure Databricks
Azure
Data
On this page
Ideal for
Data AnalystDataCertification ReadinessTailored Team Delivery
Audience Profile
Built for these roles
Before taking this course, learners should already be comfortable with the fundamentals of Python and SQL. This includes being able to write simple Python scripts and work with common data structures, as well as writing SQL queries to filter, join, and aggregate data. A basic understanding of common file formats such as CSV, JSON, or Parquet will also help when working with datasets. In addition, familiarity with the Azure portal and core services like Azure Storage is important, along with a general awareness of data concepts such as batch versus streaming processing and structured versus unstructured data. While not mandatory, prior exposure to big data frameworks like Spark, and experience working with Jupyter notebooks, can make the transition to Databricks smoother.
Overview
Executive overview
Official Microsoft Learn-aligned instructor-led program for Implement a Data Analytics Solution with Azure Databricks.
Readiness
Prerequisites
- Relevant foundational experience in the target technology area.
- Comfort with hands-on labs in a cloud or GPU-accelerated environment.
Program Outcomes
Capabilities your teams will gain
Strengthen capability in data scenarios
Strengthen capability in azure scenarios
Strengthen capability in role-based scenarios
Curriculum
Curriculum roadmap
1
Data
2
Azure
3
Role-Based
1Module 1
Implement a Data Analytics Solution with Azure Databricks
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Module 1
Implement a Data Analytics Solution with Azure Databricks
Implement a Data Analytics Solution with Azure Databricks. (DP-3011)
- Explore Azure Databricks
- Perform data analysis with Azure Databricks
- Use Apache Spark in Azure Databricks
- Manage data with Delta Lake
- Build Lakeflow Declarative Pipelines
- Deploy workloads with Lakeflow Jobs
Delivery Models
Delivery models
Virtual ILTOnsiteHybridExecutive WorkshopBootcampWeekend
Engagement Fit
Engagement fit
Certification readinessImplementation-focused labsPrivate cohort deliveryIntermediate practitioner depth
Enterprise Customization
Enterprise customization
Tailor this program to your organization's priorities: Builds current Microsoft credential readiness for Implement a Data Analytics Solution with Azure Databricks using the official Microsoft Learn outline.
- •Align labs to your production environment and platform priorities
- •Add readiness reviews and instructor-led practice sessions
- •Extend into project-specific architecture or delivery coaching
Credentials
Certification & official source
- •DP-3011
Aligned to the official Microsoft Learn course and learning path for this program.
View Official Microsoft Learn PageResources
Program resources
Yes. Most enterprise clients prefer private delivery scoped to role mix, timezone, and rollout timeline. We align lab environments and scenarios to your tenant context where applicable.
Enterprise Proof
Trusted delivery outcomes
Banking & Finance
Representative Enterprise Banking Team
The focus was not just on tooling knowledge, but on helping teams work from a shared operating model as they adopted a more modern data platform.
- Clearer platform operating model across teams
- Improved confidence in modern data stack adoption
Healthcare
Representative Healthcare Product Team
The engagement helped product and engineering stakeholders move from interest in AI to clearer implementation choices, security expectations, and prototyping discipline.
- Stronger alignment between product and engineering teams
- Improved clarity on prototype-to-production requirements
Delivery Capability
Enterprise-grade instruction
MCT-led delivery
Programs led by Microsoft Certified Trainer practitioners
Enterprise program oversight
Founder-led specialist delivery with structured rollout planning
Global delivery
APAC · EMEA · Americas · Virtual & Onsite
Implementation-focused
Hands-on labs aligned to production scenarios