Databricks Bootcamps

Technical Bootcamp

Master the Data Engineering and AI stack on Databricks

Four intensive, 100% hands-on weeks (32 live hours) taking you from Lakehouse fundamentals to production-grade data engineering projects. 1:1 mentorship, real industry cases and full prep for the Databricks Data Engineer Associate certification.

  • Next cohort: July 27, 2026
  • Mondays, Tuesdays, Thursdays and Fridays
  • 6:00 p.m. – 8:00 p.m. (GMT-5)
  • Live virtual + recordings
Apply now Ask a question
32 hrs Duration
70% Hands-on
USD 1500 Investment

What you'll achieve

Skills and outcomes you take away when you complete the program.

Design production-ready pipelines

Build PySpark pipelines on Delta Lake following the medallion architecture.

Orchestrate and monitor

Schedule workflows with Jobs and Databricks Workflows, control quality and SLAs.

Get certified

You leave ready for the Databricks Data Engineer Associate exam.

Prerequisites

What you need to make the most of it.

  • Basic Python or SQL programming
  • General database concepts
  • Computer with 8 GB RAM and stable connection

Study plan

Modules and topics covered throughout the program.

Next cohort: July 27, 2026 Live virtual + recordings Master the Data Engineering and AI stack on Databricks

Days

Mondays, Tuesdays, Thursdays and Fridays

Hours

6:00 p.m. – 8:00 p.m. (GMT-5)

4 weeks (32 live hours) · 16 live sessions + 2 hands-on challenges
Module 01Week 1

Modern Data & AI Fundamentals + Ingestion at Scale

Objective: Establish the foundations of the Databricks ecosystem and Lakehouse architecture, and master batch and streaming ingestion techniques with Auto Loader and Spark Structured Streaming.

  • Lakehouse vs Data Warehouse architecture
  • Databricks clusters and runtime
  • Notebooks, repos and version control
  • Introduction to Delta Lake
  • Auto Loader and Structured Streaming
  • Data sources: S3, ADLS, GCS
  • Schema evolution and enforcement
  • Checkpointing and error handling

Hands-on Lab: Set up a Databricks workspace and build a continuous ingestion pipeline from S3 to a Delta table.

Outcome: Solid understanding of the Databricks environment and the ability to design robust ingestion pipelines for data in motion.

Module 02Week 2

Delta Lake & Governance + Advanced Analytics & Performance

Objective: Apply advanced Delta Lake capabilities and Unity Catalog for data governance, and optimize SQL queries and Spark processing for maximum performance.

  • ACID transactions and time travel
  • Vacuum, optimize and Z-ordering
  • Unity Catalog: catalogs, schemas and permissions
  • Data lineage and auditing
  • Photon engine and adaptive query execution
  • Partitioning, bucketing and bloom filters
  • Spark UI and performance diagnostics
  • Medallion architecture patterns

Hands-on Lab: Implement a Unity Catalog catalog with column-level access control and optimize an existing pipeline reducing execution time by 50%.

Outcome: Ability to implement enterprise data governance and build high-performance analytical solutions at scale.

Module 03Week 3

Machine Learning + AI & LLMs in Databricks

Objective: Develop and train ML models using Feature Store and MLflow, and integrate large language models into data pipelines with Databricks AI.

  • MLflow tracking, registry and serving
  • Databricks Feature Store
  • AutoML and experimentation
  • Hyperparameter tuning with Hyperopt
  • Foundation Models and Model Serving
  • RAG (Retrieval Augmented Generation)
  • Databricks Vector Search
  • Prompt engineering and fine-tuning

Hands-on Lab: Train and register a classification model with MLflow and build a RAG system that answers questions about documents using Vector Search.

Outcome: Competence to develop complete ML pipelines and the ability to implement generative AI solutions in production.

Module 04Week 4

MLOps & Production + Final Project & Storytelling

Objective: Deploy and monitor ML models in production with modern MLOps practices, and integrate all learned concepts into an end-to-end project.

  • Model registry and versioning
  • CI/CD for ML pipelines with Databricks Asset Bundles
  • Model monitoring and data drift
  • Feast and feature serving
  • Real business problem definition
  • Complete solution architecture
  • Executive presentation of results
  • Preparation for Databricks certification

Hands-on Lab: Set up a CI/CD pipeline that deploys a model to production and present a complete Data Engineering or ML project to industry mentors.

Outcome: Ability to maintain ML models in production with confidence, plus an interview-ready portfolio and solid foundation for Databricks certification.

Pricing

Limited to 25 students per cohort.

Technical Bootcamp

4 weeks (32 live hours) · 16 live sessions + 2 hands-on challenges

$1500 USD -33% OFF
$1000 USD / per person
  • Databricks workspace access during the bootcamp
  • Downloadable material, challenges and real datasets
  • Practice voucher for the official certification
  • 1:1 mentorship and project feedback
  • Private alumni community

Next cohort: Next cohort: July 27, 2026

Limited to 25 students per cohort.

Apply now

Company billing and 3-month financing available.

Private instructor for companies

Per-seat rates based on group size. The larger the volume, the bigger the discount.

Private instructor

  • 20-50 seats Base Price $365
  • 50–99 seats 10% off $329
  • 100–999 seats 20% off $292
  • 1,000+ seats 30% off $256

Per-seat price for dedicated corporate cohorts. Request a custom proposal for your organization.

Request a corporate proposal

Frequently asked questions

Do I need prior Databricks experience?

No. We start from scratch — you only need Python or SQL basics.

What if I miss a class?

Every session is recorded and available for 90 days.

Do I get a certificate?

Yes: a Talento Para TI completion certificate and prep for the official Databricks DE Associate exam.

Ready to take the next step?

Reserve your spot in the upcoming cohort or talk to an advisor.

Apply now Ask a question

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