Databricks — Lakehouse Platform and Certifications
A three-day course on the Lakehouse architecture on the Databricks platform (Delta Lake, Unity Catalog) together with Databricks certification paths — including Databricks Certified GenAI Engineer, described by the industry as the most important certification for data engineers in 2026.
Lakehouse ends the data lake vs. data warehouse trade-off
For years, organizations had to choose between a flexible but unreliable data lake and a costly but transactional data warehouse. The Lakehouse architecture, built on Delta Lake, combines both worlds — data in an open format gains ACID properties and versioning without losing the flexibility that defines a data lake.
Unity Catalog as a trust layer
Without consistent governance, scattered data quickly becomes unusable. The course shows how Unity Catalog centralizes metadata, permissions, and data lineage management — from a single table to sharing data across organizations via Delta Sharing.
A certification that keeps up with the AI market
The Databricks Certified GenAI Engineer answers companies’ growing need to combine their own data with language models directly on the Lakehouse platform — the industry calls it the most important certification for data engineers in 2026. The final module prepares you for this certification and for the classic Data Engineer Associate path.
Benefits
- Design a Lakehouse architecture combining data lake flexibility with data warehouse reliability
- Implement Delta Lake as a transactional layer over data in an open format
- Configure Unity Catalog for governance, access control, and data lineage
- Prepare for the Databricks Data Engineer Associate and GenAI Engineer certifications
Who is this training for?
Prerequisites
- Knowledge of SQL and Apache Spark fundamentals
- Python experience is useful when working with Databricks notebooks
Training program
Lakehouse architecture — ending the data lake vs. data warehouse trade-off
- The evolution from data warehouse through data lake to the Lakehouse architecture
- Delta Lake as a transactional layer: ACID, time travel, schema enforcement
- Medallion architecture: bronze, silver, and gold layers in practice
- Comparing Databricks Lakehouse with alternative approaches (Snowflake, a classic data lake)
Unity Catalog — data governance and security
- Centralized metadata and permissions management in Unity Catalog
- Data lineage — tracking the origin and flow of data across pipelines
- Row-level and column-level access control
- Sharing data across teams and organizations (Delta Sharing)
Data engineering and production pipelines
- Databricks notebooks and Workflows for pipeline orchestration
- Spark performance optimization: partitioning, Z-ordering, caching
- Integrating data transformation tools (dbt on Databricks)
- Monitoring the cost and performance of Databricks clusters
Databricks certification paths
- Databricks Certified Data Engineer Associate — exam scope and structure
- Databricks Certified GenAI Engineer Associate — integrating LLMs with the Lakehouse
- Practice questions and analysis of common exam mistakes
- Career path: from Associate to the Professional levels
Delivery Methods
Online
- Convenience of participating from anywhere
- Interactive live sessions with trainer
- Materials available for 30 days
- No travel costs
On-site
- Direct contact with trainer and group
- Intensive hands-on workshops
- Networking with other participants
- Full focus on learning
Frequently asked questions
How is this different from Databricks — Unified Data Analytics in our catalog?
The existing two-day course is a hands-on introduction to working with data on Databricks, combining batch and streaming processing. This course goes deeper into architecture — it focuses on the Lakehouse as a paradigm (Delta Lake, medallion architecture), governance via Unity Catalog, and preparation for formal Databricks certifications, including the new GenAI Engineer path, which the previous course does not cover.
What is the Lakehouse architecture and why is it replacing the classic data lake?
Lakehouse combines the flexibility and low cost of a data lake with the transactional guarantees and reliability of a data warehouse — thanks to Delta Lake, data in an open format gains ACID properties, versioning (time travel), and schema enforcement, eliminating the need to maintain separate systems for analytics and BI.
What is the Databricks Certified GenAI Engineer, and why is it called the most important certification for 2026?
It is a new Databricks certification confirming the ability to build GenAI applications (RAG, agentic AI) directly on the Lakehouse architecture, combining company data with language models. The industry calls it key for data engineers in 2026 because it addresses the growing need to integrate enterprise data with AI.
Does the course prepare you for a specific Databricks exam?
Yes — the final module maps the material onto two exams: Databricks Certified Data Engineer Associate (Lakehouse and data engineering fundamentals) and Databricks Certified GenAI Engineer Associate (integrating LLMs with data on the Lakehouse), with a practice-question session for both paths.
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