In this course, the student will learn about the data engineering patterns and practices 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. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. 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. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. 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 the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.
Student Testimonials
Instructor did a great job, from experience this subject can be a bit dry to teach but he was able to keep it very engaging and made it much easier to focus.
Student
Excellent presentation skills, subject matter knowledge, and command of the environment.
Student
Instructor was outstanding. Knowledgeable, presented well, and class timing was perfect.
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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
Detailed Class Syllabus
Module 1: Explore compute and storage options for data engineering workloads
Introduction to Azure Synapse Analytics
Describe Azure Databricks
Introduction to Azure Data Lake storage
Describe Delta Lake architecture
Work with data streams by using Azure Stream Analytics
Module 2: Design and implement the serving layer
Design a multidimensional schema to optimize analytical workloads
Code-free transformation at scale with Azure Data Factory
Populate slowly changing dimensions in Azure Synapse Analytics pipelines
Module 3: Data engineering considerations for source files
Design a Modern Data Warehouse using Azure Synapse Analytics
Secure a data warehouse in Azure Synapse Analytics
Module 4: Run interactive queries using Azure Synapse Analytics 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 5: 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 6: 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 7: Ingest and load data into the data warehouse
Use data loading best practices in Azure Synapse Analytics
Petabyte-scale ingestion with Azure Data Factory
Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines
Data integration with Azure Data Factory or Azure Synapse Pipelines
Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines
Orchestrate data movement and transformation in Azure Data Factory
Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse
Optimize data warehouse query performance in Azure Synapse Analytics
Understand data warehouse developer features of Azure Synapse Analytics
Module 11: Analyze and Optimize Data Warehouse Storage
Analyze and optimize data warehouse storage in Azure Synapse Analytics
Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
Design hybrid transactional and analytical processing using Azure Synapse Analytics
Configure Azure Synapse Link with Azure Cosmos DB
Query Azure Cosmos DB with Apache Spark pools
Query Azure Cosmos DB with serverless SQL pools
Module 13: 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 14: 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
Ingest data streams with Azure Stream Analytics
Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks
Process streaming data with Azure Databricks structured streaming
Module 16: Build reports using Power BI integration with Azure Synpase Analytics
Create reports with Power BI using its integration with Azure Synapse Analytics
Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics
Use the integrated machine learning process in Azure Synapse Analytics