This comprehensive course is built from the ground up. We go far beyond standard prompt templates to cover the engineering rigor required for dependable AI systems. You will master everything from context window trimming and compaction strategies to cutting-edge features like Lakebase serverless Postgres for persistent agent memory, Model Context Protocol (MCP) tool integration, and absolute context governance via Unity Catalog.

This course bridges the gap between general AI theory and platform-specific execution on Databricks. This curriculum focuses intensely on the modern Databricks AI ecosystem. You will dive deep into building Retrieval-Augmented Generation (RAG) applications, multi-stage agentic workflows, scalable vector indexing, and automated LLM evaluation.

Rather than just teaching abstract machine learning theory, this course zeroes in on Databricks platform-specific mastery. You will learn how to automate workflows, track iterative experiments at scale, and handle big data modeling without pulling your hair out over distributed infrastructure.

Moving beyond basic PySpark and SQL syntax, this course dives into practical production-level engineering: CI/CD setups, reading the Spark UI to fix data skew, implementing row/column-level security, and orchestrating advanced workflows. By the time you finish, you will possess both the theoretical knowledge to ace the 45-question proctored exam and the hands-on mastery required of a high-performing Databricks Data Engineer.

This comprehensive course is specifically designed to bridge the gap between basic SQL knowledge and Databricks platform-specific mastery. Throughout this course, you will move beyond standard SQL syntax to master advanced platform features like AI/BI Dashboards, natural-language Genie spaces, Query Profiler, and Unity Catalog governance. By the time you finish, you'll be prepared to act as a leading Databricks analyst in any environment.

A hands-on course designed to take you from a Databricks novice to a confident navigator of the industry's leading unified data platform.

Built on top of Apache Spark, Databricks blends the best of data warehouses and data lakes into a single "Lakehouse" architecture. In this course, we will demystify the platform, break down the jargon, and get you writing code, building pipelines, and collaborating in real time.

Rather than testing platform-specific UI configurations, this course focuses directly on the open-source Apache Spark engine and DataFrame API. This course is fully revamped, expanding the curriculum to test a modern, balanced layout of Spark features—shifting away from pure syntax memorization to emphasize architecture fundamentals, troubleshooting, Structured Streaming, Spark Connect, and the Pandas API on Spark.

Moving far beyond basic ETL execution, this course will prepare you to manage dependencies programmatically, write automated unit tests for distributed pipelines, build robust data purging frameworks for compliance, and systematically eliminate complex runtime bottlenecks using the Spark UI and Query Profiler. By the end of this course, you will possess both the deep theoretical insight needed to clear the proctored exam and the senior-level engineering skills required to architect enterprise systems.

This intensive, hands-on course bridges the gap between data science and DevOps. You will learn how to design, deploy, and scale intelligent operations (AIOps) by leveraging the massive processing power of Databricks and the seamless automation of GitHub Actions.

While SQL is incredibly powerful, Python is the undisputed language of automation, scalability, and advanced analytics on the Databricks platform. This course is explicitly designed to take you from a Python beginner to a confident programmer capable of building production-grade workflows in Databricks. We don't just teach you abstract coding syntax in a vacuum. Instead, we guide you through a deliberate three-tier learning path: Core Python Foundations, Applied Data Analysis with Pandas, and Native Databricks Orchestration using PySpark.

This course teaches you to eliminate training-inference skew, deploy low-latency endpoints safely, and implement automated, touchless retraining pipelines. We bypass entry-level syntax to focus heavily on complex production setups: custom MLflow pyfunc models, registry webhooks, Databricks Asset Bundles (DABs), and automated statistical drift monitoring. By the end of this course, you will possess the deep technical insight required to clear the proctored exam and the engineering confidence to architect enterprise AI solutions.