This repository contains manual and automated deployment steps to set up the lab environments used by the Microsoft DP-203 training.
Module 1 Lab 1: Explore compute and storage options for data engineering workloads
Module 1 Lab 2: Explore compute and storage options for data engineering workloads
Module 1 Lab 2 setup - step 1 of 2
Module 1 Lab 2 setup - step 2 of 2
Module 2: Design and Implement the serving layer
Module 3: Data engineering considerations for source files
Module 3 has no lab
Module 4: Run interactive queries using serverless SQL pools
Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark
Module 5 uses the lab environment you set up for module 4.
Module 6: Data exploration and transformation in Azure Databricks
Module 7: Ingest and load data into the Data Warehouse
Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines
Module 9: Integrate data from notebooks with Azure Data Factory or Azure Synapse Pipelines
Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse
Module 11: Analyze and optimize Data Warehouse storage
Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
Module 13: End-to-end security with Azure Synapse Analytics
Modules 7, 8, 9, 10, 11, 12 and 13 use the lab environment you set up for module 4.
Module 14: Real-time stream processing with Stream Analytics
Module 15: Create a stream processing solution with Event Hubs and Azure Databricks
Module 16: Build reports using Power BI integration with Azure Synapse Analytics
Module 16 uses the lab environment you set up for module 4.
Module 17: Perform integrated Machine Learning processes in Azure Synapse Analytics
Module 17 Lab setup - step 1 of 2
Module 17 Lab setup - step 2 of 2