Databricks for Beginners: Data Analytics - Pro Track

Practical Databricks + Delta Lake hands‑on course: ETL with PySpark, medallion pipelines, visualization, streaming & basic ML

This Pro Track course is designed to teach practical, job-ready Databricks skills specifically tailored for data engineers and analytics professionals. Beginning with the essentials of workspace setup and data ingestion, you will progressively build repeatable Bronze to Silver to Gold data pipelines using Delta Lake technology. Throughout the course, you will perform robust data cleaning and complex transformations using PySpark, and apply advanced SQL patterns to support comprehensive analytics. The curriculum places a strong emphasis on defensive engineering practices, including safe type casting, handling duplicates effectively, enforcing schemas, and implementing thorough testing strategies to ensure that your data pipelines run reliably and consistently in production environments.

In addition, you will learn how to design and execute transformation workflows efficiently, schedule Databricks Jobs for automation, and optimize Spark performance by leveraging techniques such as partitioning, caching, and analyzing the Spark UI. The course also includes modules on visualization and reporting, demonstrating how to convert cleaned Spark outputs into persuasive and insightful charts using Databricks' built-in visualization tools as well as popular Python libraries like Matplotlib and Seaborn. Furthermore, the Pro Track covers fundamental concepts of structured streaming, including stateful operations, and introduces an introductory machine learning workflow utilizing MLlib and MLflow for experiment tracking and management.

Hands-on labs, real-world case studies based on datasets such as Netflix and IMDb, and downloadable notebooks provide ample opportunity to practice the entire end-to-end process. This includes ingesting raw data, cleaning and validating it, building Delta tables, creating data pipelines, visualizing insights, and deploying basic jobs. By the end of the course, you will be capable of implementing production-grade data pipelines, optimizing Spark jobs for performance, and presenting reliable, actionable analytics to stakeholders. Additionally, students who enroll during Early Access receive priority support through Q&A sessions and invitations to live interactive sessions for enhanced learning.

Explore related topics

1. Databricks Essentials

   • Workspace navigation in depth

   • Clusters vs SQL Warehouses

   • Notebooks: Python vs SQL vs Scala

2. Spark Fundamentals

   • RDDs vs DataFrames vs Datasets

   • Transformations and actions

   • Spark execution model (jobs, stages, tasks)

3. Data Engineering on Databricks

   • Ingesting data (files, databases, APIs)

   • Incremental loads & scheduling with Jobs

   • Delta Lake basics (ACID tables, time travel)

4. SQL Analytics on Databricks

   • Joins, window functions, aggregations

   • Building dashboards in Databricks SQL

   • Query optimization basics

5. Delta Lake & Lakehouse Concepts

   • Bronze / Silver / Gold architecture

   • Schema evolution & enforcement

   • CDC and streaming with Delta

6. Data Quality & Observability

   • Null/duplicate checks at scale

   • Expectations (e.g., with libraries like Great Expectations)

   • Monitoring data pipelines

7. ML & Advanced Analytics

   • Using MLflow in Databricks

   • Feature engineering in notebooks

   • Basic clustering/regression examples

8. Cost & Governance

   • Cluster sizing and cost control

   • Access control, table permissions

   • Audit/logging basics