DP-203: Data Engineering on Microsoft Azure

About this course:

In this course DP-203: Data Engineering on Microsoft Azure, the student will be integrating, transforming, and consolidating data from various structured and
unstructured data systems into a structure that is suitable for building analytics solutions.

The student will also understand the data through exploration, and they build and maintain secure and complaint data processing pipelines by using different tools
and techniques. Student will also learn data pipelines and data stores are high-performing, efficient, organized, and reliable.

Course Outline:

Module 01 : Explore compute and storage options for data engineering workloads

  • L01 – Introduction to Azure Synapse Analytics
  • L02 – Describe Azure Databricks
  • L03 – Introduction to Azure Data Lake storage
  • L04 – Describe Delta Lake architecture
  • L05 – Work with data streams by using Azure Stream Analytics

Module 02 : Run interactive queries using Azure Synapse Analytics serverless SQL pools

  • L01: Explore Azure Synapse serverless SQL pools capabilities
  • L02: Query data in the lake using Azure Synapse serverless SQL pools
  • L03: Create metadata objects in Azure Synapse serverless SQL pools
  • L04: Secure data and manage users in Azure Synapse serverless SQL pools

Module 03 : Data Exploration and Transformation in Azure Databricks

  • L01 – Understand Azure Databricks
  • L02 – Read and write data in Azure Databricks
  • L03 – Work with DataFrames in Azure Databricks
  • L04 – Work with DataFrames advanced methods in Azure Databricks

Module 04 : Explore, transform, and load data into the Data Warehouse using Apache Spark

  • L01 – Understand big data engineering with Apache Spark in Azure Synapse Analytics
  • L02 – Ingest data with Apache Spark notebooks in Azure Synapse Analytics
  • L03 – Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
  • L04 – Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Module 05 : Ingest and load data into the data warehouse

  • L01: Use data loading best practices in Azure Synapse Analytics
  • L02: Petabyte-scale ingestion with Azure Data Factory

Module 06 : Transform data with Azure Data Factory or Azure Synapse Pipelines

  • L01 – Data integration with Azure Data Factory or Azure Synapse Pipelines
  • L02 – Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Module 07 : Integrate data from Notebooks with Azure Data Factory or Azure Synapse Pipeline

  • L01 – Integrate data from Notebooks with Azure Data Factory or Azure Synapse Pipelines

Module 08 : End-to-end security with Azure Synapse Analytics

  • L01: Secure a data warehouse in Azure Synapse Analytics
  • L02: Configure and manage secrets in Azure Key Vault
  • L03: Implement compliance controls for sensitive data

Module 09 : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

  • L01 – Design hybrid transactional and analytical processing using Azure Synapse Analytics
  • L02 – Configure Azure Synapse Link with Azure Cosmos DB
  • L03 – Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics
  • L04 – Query Azure Cosmos DB with SQL Serverless for Azure Synapse Analytics

Module 10 : Real-time Stream Processing with Stream Analytics

  • L01 – Enable reliable messaging for Big Data applications using Azure Event Hubs
  • L02 – Work with data streams by using Azure Stream Analytics
  • L03 – Transform data by using Azure Stream Analytics

Module 11 : Create a Stream Processing Solution with Event Hubs and Azure Databricks

  • L01 – Understand the key features and uses of Structured Streaming
  • L02 – Stream data from a file and write it out to a distributed file system and connect to Event Hubs to read and write streams
  • L03 – Use sliding windows to aggregate over chunks of data rather than all data
  • L04 – Apply watermarking

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