Azure Data Engineering Complete Tutorial

Welcome to Azure Data Engineering with Databricks — a comprehensive course designed for aspiring data engineers and developers eager to build expertise in Azure Data Engineering and Big Data technologies. This course offers a complete roadmap to mastering data engineering on Azure, featuring in-depth coverage of Azure Databricks, Azure Data Factory, Azure Synapse, Spark, PySpark, Delta Lake, and more.

Key Topics Covered:

1. Azure Databricks Fundamentals:

• Architecture and Components: Understand the Databricks architecture, components, and the benefits for data engineers and scientists.
• Workspace Management: Learn workspace creation, notebook management, and library handling.
• Data Management: Work with Databricks File System (DBFS), database and table management, metastore, and Delta tables.
• Computation Management: Dive into clusters, pools, runtimes, jobs, and workload monitoring.
• Advanced Topics: Develop workflows, handle notebook parallelism, integrate with Azure services, and monitor logs.

2. Spark Core Concepts and Advanced Programming:

• Introduction to Spark: Understand the purpose of Spark, its components, RDDs, and Spark standalone installation.
• RDD and DataFrames: Gain hands-on experience with RDD operations, DataFrame transformations, and optimizations.
• Application Programming and Libraries: Explore Spark Context, application programming, Spark libraries, and advanced tuning for performance optimization.

3. PySpark Essentials and Advanced Data Operations:

• Core PySpark: Learn about SparkSession, SparkContext, and SQLContext, along with Jupyter and Databricks notebooks for Python development.
• DataFrames and Big Data File Systems: Work with various data formats (CSV, JSON, Parquet, etc.), DataFrame transformations, and caching.
• Spark SQL for Big Data Processing: Understand SQL operations, table management, joins, complex queries, and SCD implementations in Spark SQL.

4. Delta Lake for Data Reliability and Compliance:

• Delta Lake Fundamentals: Explore Delta Lake architecture, table creation, partitions, schema enforcement, and versioning.
• Advanced Operations: Implement SCD Type 1 and Type 2 using Delta Lake, and learn about time travel, vacuuming, and merge operations.

5. Azure Data Engineering on Microsoft Azure:

• Azure Storage and Data Architecture: Learn about Azure Blob Storage, ADLS Gen1/Gen2, and hybrid storage models for data warehousing and big data architectures.
• Azure Data Factory (ADF): Understand the ADF architecture, pipeline creation, data transformation, and integration runtime.
• Azure Synapse Analytics (Dedicated SQL Pool): Master Synapse DW architecture, distributed tables, indexing, and data load processes for analytical workloads.

6. Comprehensive Spark SQL:

• Foundational SQL Operations: Create databases, tables, and partitions, and perform data manipulations with DML and DRL commands.
• Advanced SQL Queries: Use complex joins, window functions, pivots, and group by clauses, and manage SCD types in Spark SQL.

This course combines theory with real-world projects, including end-to-end implementations of PySpark projects in Azure Databricks and Azure Data Factory, ensuring hands-on skills development. By the end of this course, you'll have the confidence to tackle real-world data engineering challenges on Azure and Databricks with in-depth knowledge and practical experience.