Azure Databricks & Spark For Data Engineers:Hands-on Project

Real World Project on Formula1 Racing using Databricks, PySpark, Spark SQL, Delta Lake, Unity Catalog,Azure Data Factory

What you'll learn
You will learn how to build a real world data project using Azure Databricks and Spark Core. This course has been taught using real world data.
You will acquire professional level data engineering skills in Azure Databricks, Delta Lake, Spark Core, Azure Data Lake Gen2 and Azure Data Factory (ADF)
You will learn how to create notebooks, dashboards, clusters, cluster pools and jobs in Azure Databricks
You will learn how to ingest and transform data using PySpark in Azure Databricks
You will learn how to transform and analyse data using Spark SQL in Azure Databricks
You will learn about Data Lake architecture and Lakehouse Architecture. Also, you will learn how to implement a Lakehouse architecture using Delta Lake.
You will learn how to create Azure Data Factory pipelines to execute Databricks notebooks
You will learn how to create Azure Data Factory triggers to schedule pipelines as well as monitor them.
You will gain the skills required around Azure Databricks and Data Factory to pass the Azure Data Engineer Associate certification exam DP203
You will learn how to connect to Azure Databricks from PowerBI to create reports
You will gain a comprehensive understanding about Unity Catalog and the data governance capabilities offered by Unity Catalog.
You will learn to implement a data governance solution using Unity Catalog enabled Databricks workspace.

Requirements
All the code and step-by-step instructions are provided, but the skills below will greatly benefit your journey
Basic Python programming experience will be required
Basic SQL knowledge will be required
Knowledge of cloud fundamentals will be beneficial, but not necessary
Azure subscription will be required, If you don't have one we will create a free account in the course

Description
Major updates to the course since the launchUpdate 3 - New sections 25, 26 and 27 added to include Unity Catalog. Unity Catalog is a recent addition to Databricks which offers unified data governance solution for a Data Lakehouse. These sections cover all aspects of Unity Catalog and the implementation using a project. Update 2 - New sections 6 and 7 added. Section 8 Updated. These changes are to reflect latest Databricks recommendations around accessing Azure Data Lake. Also, this provides a better solution to complete the course project for students using Azure Student Subscription or Corporate Subscriptions with limited access to Azure Active Directory. Update 1 - Sections 3, 4 & 5 updated to reflect recent UI changes to Azure Databricks. Also included lessons on additional functionality included by Databricks recently to Databricks clusters. . Welcome! I am looking forward to helping you with learning one of the in-demand data engineering tools in the cloud, Azure Databricks! This course has been taught with implementing a data engineering solution using Azure Databricks and Spark core for a real world project of analysing and reporting on Formula1 motor racing data.This is like no other course in Udemy for Azure Databricks. Once you have completed the course including all the assignments, I strongly believe that you will be in a position to start a real world data engineering project on your own and also proficient on Azure Databricks. I have also included lessons on Azure Data Lake Storage Gen2, Azure Data Factory as well as PowerBI. The primary focus of the course is Azure Databricks and Spark core, but it also covers the relevant concepts and connectivity to the other technologies mentioned. Please note that the course doesn't cover other aspects of Spark such as Spark streaming and Spark ML. Also the course has been taught using PySpark as well as Spark SQL; It doesn't cover Scala or Java. The course follows a logical progression of a real world project implementation with technical concepts being explained and the Databricks notebooks being built at the same time. Even though this course is not specifically designed to teach you the skills required for passing the Azure Data Engineer Associate Certification Exam DP203, it can greatly help you get most of the necessary skills required for the exam. Similarly, the course teaches the skills required to pass the Databricks Certified Data Engineer Associate Certification. I value your time as much as I do mine. So, I have designed this course to be fast-paced and to the point. Also, the course has been taught with simple English and no jargons. I start the course from basics and by the end of the course you will be proficient in the technologies used. Currently the course teaches you the followingAzure DatabricksBuilding a solution architecture for a data engineering solution using Azure Databricks, Azure Data Lake Gen2, Azure Data Factory and Power BICreating and using Azure Databricks service and the architecture of Databricks within AzureWorking with Databricks notebooks as well as using Databricks utilities, magic commands etcPassing parameters between notebooks as well as creating notebook workflowsCreating, configuring and monitoring Databricks clusters, cluster pools and jobsMounting Azure Storage in Databricks using secrets stored in Azure Key VaultWorking with Databricks Tables, Databricks File System (DBFS) etcUsing Delta Lake to implement a solution using Lakehouse architectureCreating dashboards to visualise the outputsConnecting to the Azure Databricks tables from PowerBISpark (Only PySpark and SQL)Spark architecture, Data Sources API and Dataframe APIPySpark - Ingestion of CSV, simple and complex JSON files into the data lake as parquet files/ tables. PySpark - Transformations such as Filter, Join, Simple Aggregations, GroupBy, Window functions etc.PySpark - Creating local and temporary viewsSpark SQL - Creating databases, tables and viewsSpark SQL - Transformations such as Filter, Join, Simple Aggregations, GroupBy, Window functions etc.Spark SQL - Creating local and temporary viewsImplementing full refresh and incremental load patterns using partitionsDelta LakeEmergence of Data Lakehouse architecture and the role of delta lake.Read, Write, Update, Delete and Merge to delta lake using both PySpark as well as SQL History, Time Travel and VacuumConverting Parquet files to Delta filesImplementing incremental load pattern using delta lakeUnity CatalogOverview of Data Governance and Unity CatalogCreate Unity Catalog Metastore and enable a Databricks workspace with Unity CatalogOverview of 3 level namespace and creating Unity Catalog objectsConfiguring and accessing external data lakes via Unity CatalogDevelopment of mini project using unity catalog and seeing the key data governance capabilities offered by Unity Catalog such as Data Discovery, Data Audit, Data Lineage and Data Access Control.Azure Data FactoryCreating pipelines to execute Databricks notebooksDesigning robust pipelines to deal with unexpected scenarios such as missing filesCreating dependencies between activities as well as pipelinesScheduling the pipelines using data factory triggers to execute at regular intervalsMonitor the triggers/ pipelines to check for errors/ outputs.

Who this course is for:
University students looking for a career in Data Engineering, IT developers working on other disciplines trying to move to Data Engineering, Data Engineers/ Data Warehouse Developers currently working on on-premises technologies, or other cloud platforms such as AWS or GCP who want to learn Azure Data Technologies, Data Architects looking to gain an understanding about Azure Data Engineering stack