In this course, the student will learn how to implement and manage data engineering workloads on Microsoft Azure, using Azure services such as Azure Synapse Analytics, Azure Data Lake Storage Gen2, Azure Stream Analytics, Azure Databricks, and others. The course focuses on common data engineering tasks such as orchestrating data transfer and transformation pipelines, working with data files in a data lake, creating and loading relational data warehouses, capturing and aggregating streams of real-time data, and tracking data assets and lineage.
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 includes data analysts and data scientists who work with analytical solutions built on Microsoft Azure.
Course Syllabus
Get started with data engineering on Azure
In most organizations, a data engineer is the primary role responsible for integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions. An Azure data engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints.
Build data analytics solutions using Azure Synapse serverless SQL pools
If you have large volumes of data stored as files in a data lake, you'll need a convenient way to explore and analyze the data they contain. Azure Synapse Analytics enables you to apply the SQL skills you use in a relational database to files in a data lake.
Perform data engineering with Azure Synapse Apache Spark Pools
Apache Spark is a highly scalable distributed processing solution for big data analytics and transformation. You can leverage its power in Azure Synapse Analytics by using Spark pools.
Transfer and transform data with Azure Synapse Analytics pipelines
Azure Synapse Analytics enables data integration through the use of pipelines, which you can use to automate and orchestrate data transfer and transformation activities.
Implement a Data Analytics Solution with Azure Synapse Analytics
Work with Data Warehouses using Azure Synapse Analytics
Relational data warehouses are at the heart of many business intelligence and enterprise analytics solutions. You can use Azure Synapse Analytics to implement highly scalable data warehouses in the cloud.
Work with Hybrid Transactional and Analytical Processing Solutions using Azure Synapse Analytics
Hybrid Transactional and Analytical Processing (HTAP) is a technique for near real time analytics without a complex ETL solution. In Azure Synapse Analytics, HTAP is supported through Azure Synapse Link.
Implement a Data Streaming Solution with Azure Stream Analytics
Stream processing enables you to capture and analyze data in real-time. Azure Stream Analytics is a cloud-based stream processing engine that you can use to build highly scalable real-time analytics solutions.
Implement a data lakehouse analytics solution with Azure Databricks
Learn how to harness the power of Apache Spark and powerful clusters running on the Azure Databricks platform to run large data engineering workloads in the cloud.
Certification
This training prepares you for:
*Certificiranje ni vključeno v ceno
Prerequisites
Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.
Specifically completing:
- AZ-900 - Azure Fundamentals
- DP-900 - Microsoft Azure Data Fundamentals
Data Engineering on Microsoft Azure
