Harness Big Data with SQL, Spark SQL, and Databricks

This course is designed to take you step by step from the fundamentals of Big Data to advanced analysis using modern SQL with Apache Spark and Databricks, one of the most widely used platforms in the industry. You will start by setting up your environment in Databricks Community Edition, getting to know its interface, catalogs, and SQL Warehouses so that from day one you can execute real queries on large datasets. Soon, you will understand why Excel is no longer sufficient when data grows, and you will learn key concepts such as horizontal scalability, distributed computing, and MapReduce in a clear manner with practical analogies.

Next, you will delve into the heart of modern analysis with the Data Lakehouse architecture and Delta Lake, where you will work with consistent data, secure transactions, and techniques such as Time Travel to audit historical information. Throughout the course, you will master Spark SQL in action: creating tables and views, aggregations, large-scale JOINs, subqueries, CTEs, date and text functions, and advanced tools like window functions for rankings, accumulations, and temporal comparisons. You will also learn to load data incrementally with INSERT and MERGE, as done in professional environments. Not only will you query data, but you will also understand how to optimize performance with concepts like predicate pushdown, partitions, shuffle, and Z-Order.

Finally, you will transform your analysis into interactive visualizations and dashboards within Databricks and conclude with a real business project where you will apply everything learned from start to finish. This course is your practical bridge from traditional SQL to the world of Big Data in the cloud.

What you will learn:

• Set up Databricks Community Edition and use SQL Warehouses for large-scale queries
• Understand the Data Lakehouse architecture and the advantages of Delta Lake (consistency, transactions, Time Travel)
• Master Spark SQL: creating tables, views, aggregations, JOINs, and subqueries
• Apply window functions for rankings, accumulations, and temporal comparisons
• Load data incrementally and perform MERGE for production scenarios
• Optimize performance with partitions, predicate pushdown, shuffle, and Z-Order
• Transform queries into interactive dashboards within Databricks
• Complete a real business project applying the entire workflow learned

Course Content:

• Sections: 8
• Lectures: 25
• Duration: 15 hours

Requirements:

• No prior experience in Big Data or Spark needed
• Basic SQL knowledge (SELECT, WHERE, GROUP BY, simple JOINs)
• Interest in large-scale data analysis

Who is it for?

• Data analysts already using SQL who want to work with large volumes of information
• Excel, Power BI, or traditional BI users feeling that their data no longer fits in local tools
• Individuals wanting to enter the world of Big Data without programming in complex languages
• Business professionals who want to understand how massive data is analyzed in the cloud.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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