PySpark

Module 1: Introduction to Azure Databricks and PySpark

• What is Databricks?
• Overview of Databricks as a unified analytics platform
• Comparison with other data processing tools

• PySpark Introduction:
• Understanding PySpark as a Python API for Apache Spark
• Benefits of using PySpark for data processing

• Setting Up Databricks and PySpark:
• Creating a Databricks workspace
• Creating a cluster with PySpark runtime
• Connecting to the Databricks workspace using PySpark

Module 2: PySpark Basics

• SparkContext and SparkSession:
• Creating SparkContext and SparkSession objects
• Understanding the relationship between them

• RDDs (Resilient Distributed Datasets):
• Creating RDDs from various sources (e.g., files, collections)
• Performing transformations (map, filter, flatMap, etc.)
• Performing actions (collect, count, reduce, etc.)

• DataFrames and DataSets:
• Creating DataFrames and DataSets from RDDs or other sources
• Using SQL-like operations on DataFrames
• Understanding the difference between DataFrames and DataSets

Module 3: Working with Data in PySpark

• Reading and Writing Data:
• Reading data from various formats (CSV, JSON, Parquet, etc.)
• Writing data to various formats

• Data Cleaning and Preparation:
• Handling missing values
• Dealing with outliers
• Feature engineering

• Data Exploration and Visualization:
• Using PySpark's built-in visualization functions
• Integrating with external visualization libraries (e.g., Matplotlib, Seaborn)

Module 4: Advanced PySpark Operations

• Window Functions:
• Using window functions for calculations within groups

• UDFs (User-Defined Functions):
• Creating and using UDFs for custom computations

• Broadcasts and Accumulators:
• Understanding broadcasts and accumulators for efficient data sharing and aggregation

• Machine Learning with PySpark:
• Using PySpark MLlib for machine learning tasks
• Building and training models
• Model evaluation and prediction

Module 5: Databricks-Specific Features

• • Delta Lake:
• o Integrating Delta Lake with PySpark
• o Using Delta Lake features (ACID compliance, schema evolution, time travel)

• Databricks SQL:
• Using SQL queries with PySpark
• Creating and managing SQL objects

• Databricks Repositories:
• Managing code and notebooks using Databricks Repositories

Module 6: Performance Optimization

• Performance Tuning Techniques:
• Understanding factors affecting performance
• Optimizing data loading and processing
• Using caching and persistence

• Cluster Configuration:
• Configuring clusters for optimal performance
• Choosing the right cluster type and resources

Module 7: Best Practices and Troubleshooting

• Best Practices for PySpark Development:
• Writing efficient PySpark code
• Following coding conventions

• Troubleshooting PySpark Applications:
• Identifying and resolving common issues
• Using Databricks' debugging tools

Module 8: Pyspark Projects

• Hands-on Projects:
• Creating and running pipelines
• Building data flows
• Integrating with various data sources and sinks
• Implementing ETL and ELT patterns
• Troubleshooting and optimizing Databricks workflows

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