In this course, the student will learn about data engineering as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how to create a real-time analytical system to create real-time analytical solutions.
Microsoft Data Engineering on Microsoft Azure Training (DP-203) Delivery Methods
In-Person
Online
Microsoft Data Engineering on Microsoft Azure Training (DP-203) Course Benefits
Explore compute and storage options for data engineering workloads in Azure
Run interactive queries using serverless SQL pools
Perform data Exploration and Transformation in Azure Databricks
Explore, transform, and load data into the Data Warehouse using Apache Spark
Ingest and load Data into the Data Warehouse
Transform Data with Azure Data Factory or Azure Synapse Pipelines
Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
Perform end-to-end security with Azure Synapse Analytics
Perform real-time Stream Processing with Stream Analytics
Create a Stream Processing Solution with Event Hubs and Azure Databricks
Continue learning and face new challenges with after-course one-on-one instructor coaching
Microsoft DP-203 Training Outline
Module 1: Explore compute and storage options for data engineering workloads
In this module, you will learn how to use Azure Synapse Analytics to:
Describe Azure Databricks
Introduction to Azure Data Lake storage
Describe Delta Lake architecture
Work with data streams by using Azure Stream Analytics
Lab:
Explore compute and storage options for data engineering workloads
Combine streaming and batch processing with a single pipeline
Organize the data lake into levels of file transformation
Index data lake storage for query and workload acceleration
Module 2: Run interactive queries using Azure Synapse Analytics serverless SQL pools
Module 3: Data exploration and transformation in Azure Databricks
Module 4: Explore, transform, and load data into the Data Warehouse using Apache Spark
Module 5: Ingest and load data into the data warehouse
Module 6: Transform data with Azure Data Factory or Azure Synapse Pipelines
Module 7: Orchestrate data movement and transformation in Azure Synapse Pipelines
Module 8: End-to-end security with Azure Synapse Analytics
Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
Module 10: Real-time Stream Processing with Stream Analytics
Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks
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