Databricks - Lakehouse & AI Mastery
Master Databricks across data engineering, machine learning, and generative AI: the lakehouse architecture, Delta Lake, Spark at scale, MLflow, and the platform that unifies data and AI.
The lakehouse
Databricks is built around the lakehouse, and the program starts there: the architecture that combines the flexibility of a data lake with the reliability of a warehouse, Delta Lake and its ACID transactions on the lake, the medallion architecture, Unity Catalog for governance, and the workspace model of clusters and jobs. Understanding the lakehouse is the key to everything Databricks does.
The lakehouse matters because it resolves a long-standing split between data lakes and warehouses, and Databricks is the platform most associated with it. Grasping how Delta Lake brings reliability to cheap, flexible lake storage is what lets you build data platforms that serve both analytics and AI from one place.
Grasping how Delta Lake brings reliability to cheap lake storage is what lets you build platforms that serve both analytics and AI from one place.
Data engineering and machine learning
The program covers data engineering on Databricks, Spark, Delta Live Tables, Structured Streaming, performance optimization, and orchestration with Workflows, and then the machine-learning workflow: feature engineering at scale, MLflow for tracking and models, model serving, and AutoML with the feature store. This span across data and ML is Databricks' defining strength.
Being able to move from a data pipeline to a trained, tracked, served model on one platform is exactly what makes Databricks powerful, and the program teaches that end-to-end flow. It is the capability that data-and-AI teams increasingly need, and it is a distinctive part of the senior data-platform skill set.
Moving from a pipeline to a trained, served model on one platform is Databricks' defining strength and an increasingly needed capability.
Generative AI and governance
The program reaches into generative AI on Databricks: its foundations, vector search and retrieval, building an AI application over your own data, and governing data and AI together. This reflects where the platform and the industry are heading, and it gives you hands-on experience with the retrieval-based AI patterns that are becoming standard.
Governance ties it together through Unity Catalog, which governs data and AI assets alike. The capstone assembles a lakehouse-and-AI platform, a medallion pipeline feeding an ML workflow and a generative-AI feature, all governed, which is a distinctive, forward-looking deliverable.
The generative-AI and governance material reflects where the platform and the industry are heading, giving the program a forward-looking edge.
See the method, not just the topic
A representative worked example from the program, so you can see the level of concreteness the curriculum works at.
# Delta Live Tables: declare the medallion layers as transformations.
# Databricks manages dependencies, data quality, and incremental runs.
import dlt
@dlt.table(comment="Raw ingest")
def bronze_sales():
return spark.readStream.format("cloudFiles") \
.option("cloudFiles.format", "json").load("/mnt/landing/sales")
@dlt.table
@dlt.expect_or_drop("valid_amount", "amount > 0") # data quality
def silver_sales():
return dlt.read_stream("bronze_sales").dropDuplicates(["sale_id"])
@dlt.table(comment="Business-level aggregates")
def gold_daily_revenue():
return dlt.read("silver_sales").groupBy("region", "sale_date") \
.agg(sum("amount").alias("revenue"))
# The expectation drops bad rows automatically; DLT figures out the
# bronze -> silver -> gold dependency graph and runs it incrementally.
# Delta Lake underneath gives ACID reliability on cheap lake storage.The full syllabus
Four modules of five chapters each, sequenced so the material builds cumulatively. Each chapter carries a note on what it teaches.
Module 1The lakehouse
- 01The lakehouse architectureThe lakehouse architecture. The lakehouse unifies lake and warehouse.
- 02Delta Lake and ACID on the lakeDelta Lake and ACID on the lake. Delta Lake brings reliability to lake storage.
- 03The medallion architectureThe medallion architecture. The medallion pattern structures the lakehouse.
- 04Unity Catalog and governanceGovernance with Unity Catalog. Unity Catalog governs data and AI together.
- 05Workspaces, clusters, and jobsWorkspaces, clusters, and jobs. The workspace is where the work happens.
Module 2Data engineering on Databricks
- 06Spark on DatabricksSpark on Databricks. Spark powers the processing underneath.
- 07Delta Live TablesDeclarative pipelines with Delta Live Tables. Delta Live Tables declare pipelines simply.
- 08Structured StreamingStructured Streaming. Streaming handles real-time data.
- 09Performance and optimizationPerformance and optimization. Optimization keeps big jobs efficient.
- 10Orchestration with WorkflowsOrchestration with Workflows. Workflows orchestrate the pipeline.
Module 3Machine learning
- 11The Databricks ML workflowThe Databricks ML workflow. The ML workflow lives on the same platform.
- 12Feature engineering at scaleFeature engineering at scale. Features are engineered at lakehouse scale.
- 13MLflow for tracking and modelsTracking and models with MLflow. MLflow tracks experiments and models.
- 14Model serving and deploymentModel serving and deployment. Serving puts models into production.
- 15AutoML and the feature storeAutoML and the feature store. AutoML and the feature store speed ML work.
Module 4Generative AI and capstone
- 16Foundations of generative AI on DatabricksFoundations of generative AI. Generative AI is built into the platform.
- 17Vector search and retrievalVector search and retrieval. Retrieval grounds AI in your own data.
- 18Building an AI applicationBuilding an AI application. You build a real AI application.
- 19Governing data and AI togetherGoverning data and AI together. One catalog governs data and AI alike.
- 20The lakehouse and AI capstoneBuilding the lakehouse and AI capstone. The capstone unites data, ML, and AI.
How the program is taught
The program is hands-on and project-driven: you work with the Databricks lakehouse across data engineering, ML, and AI through real labs rather than watching from a distance, and it builds toward a capstone you can keep and show. Every concept is applied, because platform skills are built by doing, not by reading about them.
It is structured so a motivated learner can start where they are and build steadily, with worked examples and code throughout. The through-line is always real, production-shaped work, so at every stage you are learning the platform the way practitioners actually use it.
Prerequisites and pace
SQL and the data-engineering foundation help, and the Spark program deepens the engine underneath. The program is otherwise self-contained. The pace builds from foundations to a capstone, and the most effective approach is to complete each lab rather than skim it, since the labs accumulate into the project.
For someone working toward the senior end of this track, consistency matters more than speed: steady progress through the material, and through the capstone, is what builds durable capability and a portfolio that demonstrates it.
What makes this program different
It spans data and AI on one platform, from Delta Lake pipelines to MLflow to generative AI, which is Databricks' defining strength and increasingly what teams need. That focus is what turns knowledge of a platform into the ability to build and operate real systems on it.
The other distinction is the orientation toward the whole journey. Every program in this track is designed to fit with the others and to build toward the senior technical-leadership destination, so this one is taught as a step on that path rather than an isolated course.
What you will be able to do
- Build a lakehouse on Delta Lake with the medallion pattern
- Engineer data pipelines with Spark and Delta Live Tables
- Run a machine-learning workflow with MLflow
- Deploy models and a generative-AI application
- Govern data and AI together with Unity Catalog
Who should take it
- Data engineers building lakehouses
- Machine-learning and AI engineers
- Analytics engineers on Spark
- Teams unifying data and AI
Common questions
Do I need to know Spark first? It helps but is not required; the program covers Spark on Databricks. The dedicated Spark program deepens the engine if you want that depth.
Is the AI material serious or a bolt-on? It is hands-on: you build a generative-AI feature with retrieval over your own data and govern it, reflecting where the platform and industry are heading.
Where it leads, toward Principal
In the near term, this program opens Data Engineer, ML Engineer, and Lakehouse Platform roles, and toward senior data-and-AI platform roles. The capstone is concrete evidence of that capability, which matters more than any list of topics studied.
In the longer term, it is one rung on the ladder toward Principal Engineer and Director of Cloud and Data Platforms. That destination is reached through breadth across the whole stack plus the architecture, operations, and leadership judgment the senior programs emphasize, and this program contributes a genuine, in-demand piece of that breadth.
How it fits the journey
This program is the second of the warehouse-and-lakehouse programs. It rests on the foundations before it and connects to the programs around it, so taking it in sequence builds cumulative command rather than isolated knowledge.
Because every program is also complete in itself, you can enter here if this is exactly the platform your goals require, and still get a whole, finished program. The sequence is a guide, not a gate, all the way up to the Principal and Director destination.
What you build and keep
Build a lakehouse-and-AI platform on Databricks: a medallion pipeline on Delta Lake with Delta Live Tables, governed by Unity Catalog, feeding a machine-learning workflow tracked in MLflow, and finishing with a generative-AI feature using retrieval over your own data.
Format: Self-paced with hands-on labs across data engineering, ML, and AI, to a unified capstone.
Run this program for your team
Every program can be delivered as a private, tailored cohort for your organization, aligned to your systems, policies, and career frameworks.
Scope a corporate cohortFrequently asked questions
What is the Databricks - Lakehouse & AI Mastery program?
Master Databricks across data engineering, machine learning, and generative AI: the lakehouse architecture, Delta Lake, Spark at scale, MLflow, and the platform that unifies data and AI.
Who is this program for?
It suits data engineers building lakehouses, along with others described on this page.
How is it delivered?
Self-paced with hands-on labs across data engineering, ML, and AI, to a unified capstone.
Is there a project or capstone?
Build a lakehouse-and-AI platform on Databricks: a medallion pipeline on Delta Lake with Delta Live Tables, governed by Unity Catalog, feeding a machine-learning workflow tracked in MLflow, and finishing with a generative-AI feature using retrieval over your own data.
How does this fit the wider journey?
Databricks rounds out the warehouses-and-lakehouses stage and reaches into ML and AI. Its Spark foundation connects to the Spark program, and its data-plus-AI breadth is increasingly central to senior and principal data-platform roles.
Can my organization run this as a private cohort?
Yes. Every program can be delivered as a tailored corporate cohort. Contact us to scope it.