DATABRICKS BOOTCAMP

Transform your career with Big Data and AI skills

The Databricks Bootcamp offers comprehensive training in data analysis, data engineering, and data science using the Databricks platform.

Mon · Tue · Thu · Fri· 6:00 – 8:00 p.m.
100% live· Virtual
USD 1500· per person

32+hours of content

4weeks

24/7Community support

EXECUTIVE TRACK

DATABRICKS BOOTCAMP FOR EXECUTIVES

Lead digital transformation and turn data into business results. Develop strategic vision, make better decisions and amplify the impact of artificial intelligence in your organization.

Tue · Thu· 6:00 – 8:00 p.m.
100% live· Virtual
USD 1500· per person

40+Strategy hours

12+Executive workshops

100%Real case application

Scholarships · Limited spots

DATA & AI
BOOTCAMP

Become the talent companies are looking for. Specialized virtual course of 32 live hours, with or without prior experience. Apply for the scholarship and study for free — the public price is USD 1500 per person.

Mon · Fri· 6:00 – 8:00 p.m.
100% live· Virtual
USD 1500· free with scholarship

Attach your resume — we'll notify you within 48 hours if you're selected.

32+hours of content

16live sessions

100%online

01 / 03

Study Plan

Three specialized tracks for every stage of your data career.

Build your technical expertise in Databricks with 32 live hours (4 weeks) of hands-on practice, covering the entire Data Engineering and AI stack.

July 27, 202610 slots availableStart your technical journey!

Days

Monday, Tuesday, Thursday, Friday

Time

6:00 pm — 8:00 pm

Colombia · UTC-5

2 hrs/session · 16 sessions · 32 total hrs

Module 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.

Module 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%.

Module 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.

Module 4