PySpark Mastery Course Overview:

Embark on a transformative journey into the realm of data engineering through our comprehensive Databricks course. From mastering data fundamentals and Lakehouse architecture to advanced Spark SQL transformations and seamless Databricks integration, you’ll acquire hands-on expertise to excel in data manipulation and architecture. Dive into real-world labs, explore Azure services, and conquer Spark optimization techniques. This course empowers you with the skills needed to shape the future of data. For further insights on harnessing the power of PySpark in big data analytics, explore related resources.

After completing PySpark Mastery with Azure Databricks Training, students will be able to:

  • Master data storage and understand OLAP vs. OLTP distinctions.
  • Navigate Microsoft Cloud services for effective data engineering.
  • Explore Azure Blob Storage, Data Lake Gen2, and Cosmos DB.
  • Grasp Lakehouse fundamentals and Databricks architecture.
  • Perform Spark Read & Write operations and DataFrame exploration.
  • Harness the power of Spark SQL transformations and optimizations.
  • Effectively integrate Databricks with Azure Synapse Analytics.
  • Skilfully host Notebook execution in Azure Data Factory.

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Key Features of the PySpark Mastery with Azure Databricks Certification

  • Hands-on labs for practical skill development.

  • In-depth coverage of Databricks architecture and Spark SQL.

  • Expert-led guidance with industry insights.

  • Integration labs for seamless data workflows.

  • Real-world scenarios and case studies.

  • Certification of completion to validate your expertise.

Who can participate in the Training?

  • Aspiring data engineers seeking skill enhancement.
  • Data professionals transitioning to advanced roles.
  • Tech enthusiasts eager to master Databricks.

What are the prerequisites?

  • Basic knowledge of data concepts is recommended.
  • Familiarity with cloud services is advantageous.

Learning objective of course:

  • Master data storage, manipulation, and architecture.
  • Skillfully utilize Databricks for efficient data workflows.
  • Understand Spark SQL transformations and optimizations.
  • Effectively integrate Databricks with Azure services.
  • Develop expertise in hosting Notebooks in Azure Data Factory.

Why choose CloudThat as your training partner?

  • Industry-recognized expertise in data engineering training.
  • Practical labs for real-world application.
  • Comprehensive coverage of Databricks and Azure integration.
  • Proven track record of empowering data professionals.
  • Expert instructors with deep industry insights.

Modules covered in Databricks Data Engineering Course Download Course Outline

  • Types of data and how its stored
  • Difference between OLAP and OLTP
  • Microsoft Cloud services for Data engineering
  • Azure Blob Storage and Azure Data Lake Gen2
  • Azure Cosmos DB and its API’s
  • Lab: To explore Storage Account and Cosmos DB

  • Lakehouse Fundamentals
  • Azure Databricks Overview
  • Databricks Architecture
  • Basic spark architecture
  • Fundamental Concepts of databricks (workspace, notebooks, clusters)
  • Databricks File System(DBFS )
  • Lab Setup and Databricks Platform
  • Lab to work with dbutils
  • Lab to use Credential passthrough access to ADLS gen2.

  • What is Azure Databricks
  • Spark Read & Write
  • DataFrame & Exploration – infer schema, print schema and provide schema
  • Creating Service principal and mounting data to databricks
  • Difference between temp view and global tempview
  • Lab: Spark Read and Write using DataFrame
  • Lab : To explore data using infer schema, print schema and provide schema
  • Lab: Perform common transformation(count, creating tempview, global tempview, write, filter, display) using Spark DataFrame

  • Apache Spark Architecture
  • Driver Node, Executors, DAG, OnHeap memory
  • Transformation and Action
  • SparkSession
  • Dataframe
  • Lab to explore transformation and action and to work with groupBy().

  • SPARK SQL
  • Hive Metastore Understanding
  • Managed and Unmanaged Tables
  • Entire ETL and storing the final data a table
  • Explanation of partitionBy(), Data manipulation
  • Lab : to work on creating managed table
  • LAB: ETL using the sales data

  • Windowing Functions
  • Rank, dense Rank, Lead , Lag and Row number on windowing functions
  • Aggregate functions (mean, avg, max, min, count)
  • Catalyst Optimizer
  • Partitionby with aggregate functions
  • Lab: Create dataframe and work on windowing with rank functions
  • Lab: Using a dataframe apply aggregate functions with windowing

  • Spark Optimization
  • Cache and Persist
  • Repartition and Coalesce
  • Shuffling considerations and configuring
  • Delta tables – both managed and unmanaged
  • Streaming Data (readstream, write stream, checkpointing)
  • Working on JSON file
  • Delta Lake solution Architecture
  • Lab on Explode for JSON
  • Lab for Delta Table and Streaming data
  • Lab on Partitioning
  • Lab on Cache

  • Read and write data from and to Azure Synapse Analytics
  • Host the execution of Notebook in Azure Data Factory
  • Lab: Integrating with Azure Synapse Analytics/Dedicated SQL Pool

    • This course helps in clearing Databricks Data Engineering Associate certification exam.
    • Showcase your skills and advance your career.
    • Join a community of skilled data professionals.

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Course ID: 17745

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Basic data knowledge is recommended; familiarity with cloud services is a plus.

Acquire in-demand data engineering skills, setting you apart in the competitive job market.

Yes, hands-on labs provide real-world application of concepts taught.

Yes, you'll retain access to course materials for reference and continued learning.

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