DP-203: Data Engineering on Microsoft Azure
In this course, the student will learn about the data engineering as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies.
«Magi Naumova is a brilliant instructor! The layout of the presentations and demos, along with her profound understanding of all of the technologies, made for a great engaging learning experience.»
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.
Audience
The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure.
The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
Prerequisites
Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
Specifically completing:
About the instructor - Magi Naumova
Margarita Naumova is a very well-known SQL Expert. Magi holds the highest possible SQL Server Technical Certification in the field – Microsoft Certified Master, making her one of the best SQL Server Experts Worldwide. Magi is also a Microsoft Data Platform MVP (Most Valuable Professional). She has more than 20 years of SQL Server and BI technologies consulting and training experience and is a trusted advisor for many large companies in SQL Server Platform Area.
Currently she works as a Managing Partner and Chief SQL Architect of Inspir-it AS, her own newly established Consulting Company in Norway. Margarita is a regular speaker at the largest IT events, SQLBits, SQL Saturday in Europe.
Read more about Magi at Microsoft MVP website
Learning objectives
After completing this course, students will be able to:
- 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
Course outline
Module 1: Explore compute and storage options for data engineering workloads
- Introduction to Azure Synapse Analytics
- Describe Azure Databricks
- Describe Azure Databricks Delta Lake architecture
- Introduction to Azure Data Lake storage
- Work with data streams by using Azure
- Stream Analytics
Module 2: Run interactive queries using serverless SQL pools
- Explore Azure Synapse serverless SQL pools capabilities
- Query data in the lake using Azure Synapse serverless SQL pools
- Create metadata objects in Azure Synapse serverless SQL pools
- Secure data and manage users in Azure Synapse serverless SQL pools
Module 3: Data Exploration and Transformation in Azure Databricks
- Describe Azure Databricks
- Read and write data in Azure Databricks
- Work with DataFrames in Azure Databricks
- Work with DataFrames advanced methods in Azure Databricks
Module 4: Explore, transform , and load data into the Data Warehouse using Apache Spark
- Understand big data engineering with Apache Spark in Azure Synapse Analytics
- Ingest data with Apache Spark notebooks in Azure Synapse Analytics
- Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics
- Integrate SQL and Apache Spark pools in Azure Synapse Analytics
Module 5: Ingest and load Data into the Data Warehouse
- Use loading best practices in Azure Synapse Analytics
- Petabyte-scale ingestion with Azure Data Factory
Module 6: Transform Data with Azure Data Factory or Azure Synapse Pipelines
- Data integration with Azure Data Factory
- Code-free transformation at scale with Azure Data Factory
Module 7: Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
- Orchestrating data movement and transformation in Azure Data Factory
- Run Azure Databricks Notebooks with Azure Data Factory
Module 8: End-to end security with Azure Synapse Analytics
- Secure a data warehouse in Azure Synapse Analytics
- Configure and manage secrets in Azure Key Vault
- Implement compliance controls for sensitive data
Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
- Plan hybrid transactional and analytical processing using Azure Synapse Analytics
- Implement Azure Synapse Link with Azure Cosmos DB
- Query Cosmos DB data with Spark
- Query Cosmos DB with Synapse SQL
Module 10: Real-time Stream Processing with Stream Analytics
- Enable reliable messaging for Big Data applications using Azure Event Hubs
- Work with data streams by using Azure Stream Analytics
- Transform data by using Azure Stream Analytics
Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks
- Process Streaming data with Azure Databricks structured streaming
Certification
This training will help you prepare for exam DP-203: Data Engineering on Microsoft Azure.
By passing this exam you will earn the Microsoft Certified: Azure Data Engineer Associate certification.
