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DP 203: Data Engineering on Microsoft Azure
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Lesson 1: Get Started With Data Engineering on Azure
1.1.1 Introduction to Data Engineering On Azure
1.1.2 What is Data Engineering
1.1.3 Important Data Engineering Concepts
1.1.4 Data Engineering In Microsoft Azure
1.1.5 Summary
1.2.1 Introduction to Azure Data Lake Storage Gen2
1.2.2 Understand Azure Data Lake Storage Gen2
1.2.3 Enable Azure Data Lake Storage Gen2 in Azure Storage
1.2.4 Compare Azure Data Lake Store to Azure Blob storage
1.2.5 Understand The Stages for Processing Big Data
1.2.6 Use Azure Data Lake Storage Gen2 in Data Analytics Workloads
1.2.7 Summary
1.3.1 Introduction to Azure Synapse Analytics
1.3.2 What is Azure Synapse Analytics
1.3.3 How Azure Synapse Analytics works
1.3.4 When to use Azure Synapse Analytics
1.3.5 Summary
Lesson 2: Build Data Analytics Solutions Using Azure Synapse Serverless SQL Pools
2.1.1 Introduction to Use Azure Synapse Serverless SQL Pool to Query Files in a Data Lake
2.1.2 Understand Azure Synapse Serverless SQL Pool Capabilities and Use Cases
2.1.3 Query Files Using a Serverless SQL Pool
2.1.4 Create External Database Objects
2.1.5 Summary
2.2.1 Introduction to Use Azure Synapse Serverless SQL Pools to Transform Data in a Data Lake
2.2.2 Transform Data Files With The CREATE EXTERNAL TABLE AS SELECT Statement
2.2.3 Encapsulate Data Transformations in a Stored Procedure
2.2.4 Include a Data Transformation Stored Procedure in a Pipeline
2.2.5 Summary
2.3.1 Introduction to Create a Lake Database in Azure Synapse Analytics
2.3.2 Understand Lake Database Concepts
2.3.3 Explore Database Templates
2.3.4 Create a Lake Database
2.3.5 Use a Lake Database
2.3.6 Summary
2.4.1 Introduction to Secure Data and Manage Users in Azure Synapse Serverless SQL Pools
2.4.2 Choose an Authentication Method in Azure Synapse Serverless SQL Pools
2.4.3 Manage Users in Azure Synapse Serverless SQL Pools
2.4.4 Manage User Permissions in Azure Synapse Serverless SQL Pools
2.4.5 Summary
Lesson 3: Perform Data Engineering With Azure Synapse Apache Spark Pools
3.1.1 Introduction to Analyze Data With Apache Spark in Azure Synapse Analytics
3.1.2 Get to Know Apache Spark
3.1.3 Use Spark in Azure Synapse Analytics
3.1.4 Analyze Data With Spark
3.1.5 Visualize Data With Spark
3.1.6 Summary
3.2.1 Introduction to Transform Data with Spark in Azure Synapse Analytics
3.2.2 Modify and Save Dataframes
3.2.3 Partition Data Files
3.2.4 Transform Data With SQL
3.2.5 Summary
3.3.1 Introduction Use Delta Lake in Azure Synapse Analytics
3.3.2 Understand Delta Lake
3.3.3 Create Delta Lake tables
3.3.4 Create Catalog Tables
3.3.5 Use Delta Lake with Streaming Data
3.3.6 Use Delta Lake in a SQL Pool
3.3.7 Summary
Lesson 4: Transfer and Transform Data With Azure Synapse Analytics Pipelines
4.1.1 Introduction to Build a Data Pipeline in Azure Synapse Analytics
4.1.2 Understand Pipelines in Azure Synapse Analytics
4.1.3 Create a Pipeline in Azure Synapse Studio
4.1.4 Define Data Flows
4.1.5 Run a Pipeline
4.1.6 Summary
4.2.1 Introduction to Use Spark Notebooks in an Azure Synapse Pipeline
4.2.2 Understand Synapse Notebooks and Pipelines
4.2.3 Use a Synapse Notebook Activity in a Pipeline
4.2.4 Use Parameters in a Notebook
4.2.5 Summary
Lesson 5: Implement a Data Analytics Solution with Azure Synapse Analytics
5.1.1 Introduction to Introduction to Azure Synapse Analytics
5.1.2 What is Azure Synapse Analytics
5.1.3 How Azure Synapse Analytics works
5.1.4 When to Use Azure Synapse Analytics
5.1.5 Summary
5.2.1 Introduction to Use Azure Synapse Serverless SQL Pool to Query Files in a Data Lake
5.2.2 Understand Azure Synapse Serverless SQL Pool Capabilities and Use Cases
5.2.3 Query Files Using a Serverless SQL Pool
5.2.4 Create External Database Objects
5.2.5 Summary
5.3.1 Introduction to Analyze data with Apache Spark in Azure Synapse Analytics
5.3.2 Get to Know Apache Spark
5.3.3 Use Spark in Azure Synapse Analytics
5.3.4 Analyze Data with Spark
5.3.5 Visualize Data with Spark
5.3.6 Summary
5.4.1 Introduction to Use Delta Lake in Azure Synapse Analytics
5.4.2 Understand Delta Lake
5.4.3 Create Delta Lake tables
5.4.4 Create Catalog Tables
5.4.5 Use Delta Lake with Streaming Data
5.4.6 Use Delta Lake in a SQL Pool
5.4.7 Summary
5.5.1 Introduction to Analyze Data in a Relational Data Warehouse
5.5.2 Design a Data Warehouse Schema
5.5.3 Create Data Warehouse Tables
5.5.4 Load Data Warehouse Tables
5.5.5 Query a Data Warehouse
5.5.6 Summary
5.6.1 Introduction to Build a Data Pipeline in Azure Synapse Analytics
5.6.2 Understand Pipelines in Azure Synapse Analytics
5.6.3 Create a Pipeline in Azure Synapse Studio
5.6.4 Define Data Flows
5.6.5 Run a Pipeline
5.6.6 Summary
Lesson 6: Work with Data Warehouses using Azure Synapse Analytics
6.1.1 Introduction to Analyze data in a Relational Data Warehouse
6.1.2 Design a Data Warehouse Schema
6.1.3 Create Data Warehouse Tables
6.1.4 Load Data Warehouse Tables
6.1.5 Query a Data Warehouse
6.1.6 Summary
6.2.1 Introduction to Load Data Into a Relational Data Warehouse
6.2.2 Load Staging Tables
6.2.3 Load Dimension Tables
6.2.4 Load Time Dimension Tables
6.2.5 Load Slowly Changing Dimensions
6.2.6 Load Fact Tables
6.2.7 Perform Post Load Optimization
6.2.8 Summary
6.3.1 Introduction to Manage and Monitor Data Warehouse Activities in Azure Synapse Analytics
6.3.2 Scale Compute Resources in Azure Synapse Analytics
6.3.3 Pause Compute in Azure Synapse Analytics
6.3.4 Manage Workloads in Azure Synapse Analytics
6.3.5 Use Azure Advisor to Review Recommendations
6.3.6 Use Dynamic Management Views to Identify and Troubleshoot Query Performance
6.3.7 Summary
6.4.1 Introduction to Secure a Data Warehouse in Azure Synapse Analytics
6.4.2 Understand Network Security Options for Azure Synapse Analytics
6.4.3 Configure Conditional Access
6.4.4 Configure Authentication
6.4.5 Manage Authorization Through Column and Row Level Security
6.4.6 Manage Sensitive Data With Dynamic Data Masking
6.4.7 Implement Encryption in Azure Synapse Analytics
6.4.8 Summary
Lesson 7: Work with Hybrid Transactional and Analytical Processing Solutions Using Azure Synapse Analytics
7.1.1 Introduction to Plan Hybrid Transactional and Analytical Processing Using Azure Synapse Analytics
7.1.2 Understand Hybrid Transactional and Analytical Processing Patterns
7.1.3 Describe Azure Synapse Link
7.1.4 Summary
7.2.1 Introduction to Implement Azure Synapse Link with Azure Cosmos DB
7.2.2 Enable Cosmos DB Account to Use Azure Synapse Link
7.2.3 Create an Analytical Store Enabled Container
7.2.4 Create a Linked Service for Cosmos DB
7.2.5 Query Cosmos DB Data with Spark
7.2.6 Query Cosmos DB with Synapse SQL
7.2.7 Summary
7.3.1 Introduction to Secure a Data Warehouse in Azure Synapse Analytics
7.3.2 What is Azure Synapse Link for SQL?
7.3.3 Configure Azure Synapse Link for Azure SQL Database
7.3.4 Configure Azure Synapse Link for SQL Server 2022
7.3.5 Summary
Lesson 8: Implement a Data Streaming Solution with Azure Stream Analytics
8.1.1 Introduction to Get started with Azure Stream Analytics
8.1.2 Understand Data Streams
8.1.3 Understand Event Processing
8.1.4 Understand Window Functions
8.1.5 Summary
8.2.1 Introduction to Ingest Streaming Data Using Azure Stream Analytics and Azure Synapse Analytics
8.2.2 Stream Ingestion Scenarios
8.2.3 Configure Inputs and Outputs
8.2.4 Define a Query to Select, Filter, and Aggregate Data
8.2.5 Run a Job to Ingest Data
8.2.6 Summary
8.3.1 Introduction to Visualize Real-Time Data with Azure Stream Analytics and Power BI
8.3.2 Use a Power BI Output in Azure Stream Analytics
8.3.3 Create a Query for Real-Time Visualization
8.3.4 Create Real-Time Data Visualizations in Power BI
8.3.5 Summary
Lesson 9: Implement a data Lakehouse analytics solution with Azure Databricks
9.1.1 Introduction to Explore Azure Databricks
9.1.2 Get Started with Azure Databricks
9.1.3 Identify Azure Databricks workloads
9.1.4 Understand Key Concepts
9.1.5 Data Governance Using Unity Catalog and Microsoft Purview
9.1.6 Summary
9.2.1 Introduction to Perform Data Analysis with Azure Databricks
9.2.2 Ingest Data with Azure Databricks
9.2.3 Data Exploration Tools in Azure Databricks
9.2.4 Data Analysis Using DataFrame APIs
9.2.5 Summary
9.3.1 Introduction to Use Apache Spark in Azure Databricks
9.3.2 Get to know Spark
9.3.3 Create a Spark Cluster
9.3.4 Use Spark in Notebooks
9.3.5 Use Spark to Work With Data Files
9.3.6 Visualize Data
9.3.7 Summary
9.4.1 Introduction to Manage Data with Delta Lake
9.4.2 Get started with Delta Lake
9.4.3 Manage ACID Transactions
9.4.4 Implement Schema Enforcement
9.4.5 Data Versioning and Time Travel in Delta Lake
9.4.6 Data Integrity with Delta Lake
9.4.7 Summary
9.5.1 Introduction to Build Data Pipelines with Delta Live Tables
9.5.2 Explore Delta Live Tables
9.5.3 Data Ingestion and Integration
9.5.4 Real-Time Processing
9.5.5 Summary
9.6.1 Introduction to Deploy workloads with Azure Databricks Workflows
9.6.2 What are Azure Databricks Workflows?
9.6.3 Understand Key Components of Azure Databricks Workflows
9.6.4 Explore the Benefits of Azure Databricks Workflows
9.6.5 Deploy workloads Using Azure Databricks Workflows
9.6.6 Summary
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DP 203: Data Engineering on Microsoft Azure
