Description

TABLE OF CONTENT

UNIT 1 : Introduction

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

UNIT 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

UNIT 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

UNIT 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

UNIT 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

UNIT 6  : Optimizing SQL Performance in Databricks

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

UNIT 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

UNIT 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

UNIT 9 : Best Practices and Tips

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