Introduction to Python for Data Engineering
1 Python Environment Setup and Essentials
1 Data Types and Variables
1 Basic Operators and Expressions
1 Conditional Statements
1 Loops in Python
1 Functions in Python
1 Error Handling
1 File Handling
1 Data Structures – Lists and Tuples
1 Data Structures – Sets and Dictionaries
1 Introduction to Libraries: NumPy and Pandas
1 Data Manipulation with Pandas
1 Data Processing and Cleaning
1 Project: Mini ETL Pipeline

1. PySpark Basics & Environment
– Installation and Setup
– SparkSession & SparkContext
– Spark Web UI
– Spark Architecture
– Cluster modes (local, standalone, YARN)
– Configuration settings
2. RDD Operations
– Creating RDDs
– Transformations
• map, flatMap
• filter, distinct
• union, intersection
• reduceByKey
• groupByKey
• sortByKey
• join operations
– Actions
• collect, count
• first, take
• reduce, fold
• saveAsTextFile
– Persistence & Caching
– Partitioning
– Shared Variables (Broadcast & Accumulators)
3. DataFrame Operations
– Creating DataFrames
• from RDDs
• from CSV/JSON/Parquet
• from Hive tables
– Schema Definition & Management
– Column Operations
– Basic Operations
• select, filter
• groupBy, orderBy
• join types
• union, intersect
– Window Functions
– User-Defined Functions (UDFs)
– Handling NULL values
– Date/Timestamp Operations

4. SparkSQL
– SQL Query Execution
– Temporary Views
– Catalog Operations
– Query Optimization
– Caching Tables
– JDBC/ODBC Connectivity
5. Data Processing & Optimization
– Data Cleaning Techniques
– Performance Tuning
– Memory Management
– Broadcast Joins
– Repartitioning Strategies
– Coalesce vs Repartition
– Bucketing & Partitioning
6. Advanced Features
– Structured Streaming
• Stream Processing Concepts
• Input Sources (Kafka, Files)
• Output Sinks
• Watermarking
• Window Operations
– MLlib Integration
• Feature Engineering
• Model Training
• Model Evaluation
• Pipeline Creation
– GraphX Basics
– Spark REST API
7. End-to-End Projects
Project 1: Real-time Data Processing Pipeline
– Kafka Integration
– Stream Processing
– Real-time Analytics
– Dashboard Integration
Project 2: Data Lake ETL
– Raw to Procesed layer   – Delta Lake Implementation
– Data Quality Checks
– Incremental Loading

9: OLAP vs OLTP
9 : What is a Data Warehouse?
9 : Difference between Data Warehouse, Data Lake and Data
Mart
9 : Fact Tables
9 : Dimension Tables
9 : Slowly changing Dimensions
9 : Types of SCDs
9 : Star Schema Design
9 : bSnowflake Schema Design
9 : Data Warehousing Case Studies

10. 1: Introduction to cloud computing
10. 2: Types of Cloud Models
10. 3: Types of Cloud Service Models
10. 4: IAAS
10. 5: SAAS
10. 6: PAAS
10. 7: Creation of Microsoft Azure Account
10. 8: Microsoft Azure Portal Overview

11. 1: Introduction to Azure Synapse Analytics
11. 2: Work with data streams by using Azure Stream Analytics
11. 3: Design a multidimensional schema to optimize analytical workloads
11. 4: Code-free transformation at scale with Azure Data Factory
11. 5: Populate slowly changing dimensions in Azure Synapse Analytics
pipelines
11. 6: Design a Modern Data Warehouse using Azure Synapse Analytics
11. 7: Secure a data warehouse in Azure Synapse Analytics

Explore Azure Synapse serverless SQL pool capabilities
Query data in the lake using Azure Synapse serverless SQL pools
Create metadata objects in Azure Synapse serverless SQL pools
Secure data and manage users in Azure Synapse serverless SQL
pools

Understand big data engineering with Apache Spark in Azure Synapse
Analytics
13. Ingest data with Apache Spark notebooks in Azure Synapse Analytics
13. 3Transform data with DataFrames in Apache Spark Pools in Azure Synapse
Analytics
4Integrate SQL and Apache Spark pools in Azure Synapse 13.
Analytics
5Integrate SQL and Apache Spark pools in Azure Synapse 13.
Analytics

14. 1: Describe Azure Databricks
14. 2: Read and write data in Azure Databricks
14. 3: Work with DataFrames in Azure Databricks
14. 4: Work with DataFrames advanced methods in Azure Databricks

15.1: Use data loading best practices in Azure Synapse Analytics
15.2: Petabyte-scale ingestion with Azure Data Factory or Azure
Synapse Pipelines

16. 1: Data integration with Azure Data Factory or Azure Synapse
Pipelines
16. 2: Code-free transformation at scale with Azure Data Factory or Azure
Synapse Pipelines
16. 3: Orchestrate data movement and transformation in Azure Data
Factory or Azure Synapse Pipelines

17.1: Optimize data warehouse query performance in Azure Synapse
Analytics
17. 2: Understand data warehouse developer features of Azure Synapse
Analytics
17.3: Analyze and optimize data warehouse storage in Azure Synapse
Analytics