Description

Introduction:
Explore Databricks’ capabilities by combining advanced SQL and Python to analyze, transform, and visualize data effectively. This course focuses on the seamless integration of SQL and Python for optimized data workflows.

Prerequisites:

1. Basic SQL and Python Skills: Familiarity with writing SQL queries and basic Python programming.
2. Understanding of Cloud Platforms: Experience working with cloud-based tools or environments.
3. Databricks Access: A Databricks account or workspace setup with necessary permissions.
4. Data Analysis Fundamentals: Knowledge of data exploration, visualization, and preprocessing techniques.

TABLE OF CONTENT

1 : Introduction

1.1 Overview of Databricks
1.2 Importance of Advanced SQL with Python in Databricks
1.3 Prerequisites

2 : Getting Started with Databricks and Python

2.1 Setting up a Databricks Workspace
2.2 Configuring Python Environment
2.3 Connecting Databricks with Python

3 : Data Import and Exploration

3.1 Loading Data into Databricks
3.2 Exploratory Data Analysis with Python
3.3 Data Visualization in Databricks using Python Libraries

4 : Advanced SQL Concepts in Databricks

4.1 Review of Basic SQL in Databricks
4.2 Window Functions
4.3 Common Table Expressions (CTEs)
4.4 Advanced Joins and Subqueries
4.5 Nested Queries and Aggregations
4.6 Dynamic SQL Queries

5 : Python and SQL Integration

5.1 Leveraging Python UDFs in SQL
5.2 Running Python Code in Databricks SQL Cells
5.3 Integrating Python Libraries with SQL

6  : Optimizing SQL Performance in Databricks

6.1 Query Optimization Techniques
6.2 Indexing and Partitioning Strategies
6.3 Understanding Query Execution Plans

7 : Data Manipulation and Transformation

7.1 Using Python for Data Transformation
7.2 Data Cleaning and Preprocessing
7.3 Feature Engineering with SQL and Python(Ref: Python Programming for Data Enthusiasts)

8  : Advanced Topics in Databricks

8.1 Delta Lake and Versioned Data
8.2 Machine Learning Integration with SQL
8.3 Real-time Data Processing

9 : Best Practices and Tips

9.1 Code Organization and Documentation
9.2 Collaboration and Version Control
9.3 Performance Optimization Tips

Conclusion:
Master Databricks by leveraging both SQL and Python to handle complex data tasks efficiently. Gain practical insights into performance optimization, real-time data processing, and collaborative practices.

Reference for  Python

Reference for SQL