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Data Engineering on Microsoft Azure

Master the intricacies of data engineering on Microsoft Azure in this comprehensive course. Dive deep into data engineering patterns, batch, and real-time analytical solutions using Azure data platform technologies. From designing analytical serving layers to optimizing query performance, this course equips you with the skills to harness the full potential of Azure for your data projects.

  • Intermediate
  • Small class sizes
  • 18 and older
  • $2,295
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  • Online Classroom
  • 27 hours over 4 sessions

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Class Description

Description

What you'll learn in this azure training:

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. Participants 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. 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.

Attendees will also learn the various ways they can transform the data using the same technologies that is used to ingest data. Students 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. Participants will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.

Audience profile:

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.

Job role: Data Engineer

Preparation for examDP-203

Skills gained

  • Explore compute and storage options for data engineering workloads in Azure
  • Design and Implement the serving layer
  • Understand data engineering considerations

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 AND DP-900: Microsoft Azure Data Fundamentals

Course outline:

Module 1: Explore compute and storage options for data engineering workloads

This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration.

Lessons

  • 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
  • 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: Design and implement the serving layer

This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory.

Lessons

  • 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
  • Lab : Designing and Implementing the Serving Layer
  • Design a star schema for analytical workloads
  • Populate slowly changing dimensions with Azure Data Factory and mapping data flows

Module 3: Data engineering considerations for source files

This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake.

Lessons

  • Design a Modern Data Warehouse using Azure Synapse Analytics
  • Secure a data warehouse in Azure Synapse Analytics
  • Lab : Data engineering considerations
  • Managing files in an Azure data lake
  • Securing files stored in an Azure data lake

Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools

In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).

Lessons

  • 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
  • Lab : Run interactive queries using serverless SQL pools
  • Query Parquet data with serverless SQL pools
  • Create external tables for Parquet and CSV files
  • Create views with serverless SQL pools
  • Secure access to data in a data lake when using serverless SQL pools
  • Configure data lake security using Role-Based Access Control (RBAC) and Access Control List

Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark

This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.

Lessons

  • 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
  • Lab : Explore, transform, and load data into the Data Warehouse using Apache Spark
  • Perform Data Exploration in Synapse Studio
  • Ingest data with Spark notebooks in Azure Synapse Analytics
  • Transform data with DataFrames in Spark pools in Azure Synapse Analytics
  • Integrate SQL and Spark pools in Azure Synapse Analytics

Module 6: Data exploration and transformation in Azure Databricks

This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.

Lessons

  • Describe Azure Databricks
  • Read and write data in Azure Databricks
  • Work with DataFrames in Azure Databricks
  • Work with DataFrames advanced methods in Azure Databricks
  • Lab : Data Exploration and Transformation in Azure Databricks
  • Use DataFrames in Azure Databricks to explore and filter data
  • Cache a DataFrame for faster subsequent queries
  • Remove duplicate data
  • Manipulate date/time values
  • Remove and rename DataFrame columns
  • Aggregate data stored in a DataFrame

Module 7: Ingest and load data into the data warehouse

This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion.

Lessons

  • Use data loading best practices in Azure Synapse Analytics
  • Petabyte-scale ingestion with Azure Data Factory
  • Lab : Ingest and load Data into the Data Warehouse
  • Perform petabyte-scale ingestion with Azure Synapse Pipelines
  • Import data with PolyBase and COPY using T-SQL
  • Use data loading best practices in Azure Synapse Analytics

Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines

This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.

Lessons

  • Data integration with Azure Data Factory or Azure Synapse Pipelines
  • Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines
  • Lab : Transform Data with Azure Data Factory or Azure Synapse Pipelines
  • Execute code-free transformations at scale with Azure Synapse Pipelines
  • Create data pipeline to import poorly formatted CSV files
  • Create Mapping Data Flows

Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines

In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.

Lessons

  • Orchestrate data movement and transformation in Azure Data Factory
  • Lab : Orchestrate data movement and transformation in Azure Synapse Pipelines
  • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines

Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse

In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance.

Lessons

  • Optimize data warehouse query performance in Azure Synapse Analytics
  • Understand data warehouse developer features of Azure Synapse Analytics
  • Lab : Optimize Query Performance with Dedicated SQL Pools in Azure Synapse
  • Understand developer features of Azure Synapse Analytics
  • Optimize data warehouse query performance in Azure Synapse Analytics
  • Improve query performance

Module 11: Analyze and Optimize Data Warehouse Storage

In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations.

Lessons=

  • Analyze and optimize data warehouse storage in Azure Synapse Analytics
  • Lab : Analyze and Optimize Data Warehouse Storage
  • Check for skewed data and space usage
  • Understand column store storage details
  • Study the impact of materialized views
  • Explore rules for minimally logged operations

Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.

Lessons

  • 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
  • Lab : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
  • Configure Azure Synapse Link with Azure Cosmos DB
  • Query Azure Cosmos DB with Apache Spark for Synapse Analytics
  • Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics

Module 13: End-to-end security with Azure Synapse Analytics

In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.

Lessons

  • Secure a data warehouse in Azure Synapse Analytics
  • Configure and manage secrets in Azure Key Vault
  • Implement compliance controls for sensitive data
  • Lab : End-to-end security with Azure Synapse Analytics
  • Secure Azure Synapse Analytics supporting infrastructure
  • Secure the Azure Synapse Analytics workspace and managed services
  • Secure Azure Synapse Analytics workspace data

Module 14: Real-time Stream Processing with Stream Analytics

In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.

Lessons

  • 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
  • Lab : Real-time Stream Processing with Stream Analytics
  • Use Stream Analytics to process real-time data from Event Hubs
  • Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
  • Scale the Azure Stream Analytics job to increase throughput through partitioning
  • Repartition the stream input to optimize parallelization

Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks

In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.

Lessons

  • Process streaming data with Azure Databricks structured streaming
  • Lab : Create a Stream Processing Solution with Event Hubs and Azure Databricks
  • Explore key features and uses of Structured Streaming
  • Stream data from a file and write it out to a distributed file system
  • Use sliding windows to aggregate over chunks of data rather than all data
  • Apply watermarking to remove stale data
  • Connect to Event Hubs read and write streams

Module 16: Build reports using Power BI integration with Azure Synapase Analytics

In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI.

Lessons

  • Create reports with Power BI using its integration with Azure Synapse Analytics
  • Lab : Build reports using Power BI integration with Azure Synapase Analytics
  • Integrate an Azure Synapse workspace and Power BI
  • Optimize integration with Power BI
  • Improve query performance with materialized views and result-set caching
  • Visualize data with SQL serverless and create a Power BI report

Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI.

Lessons

  • Use the integrated machine learning process in Azure Synapse Analytics
  • Lab : Perform Integrated Machine Learning Processes in Azure Synapse Analytics
  • Create an Azure Machine Learning linked service
  • Trigger an Auto ML experiment using data from a Spark table
  • Enrich data using trained models
  • Serve prediction results using Power BI

Remote Learning

This course is available for "remote" learning and will be available to anyone with access to an internet device with a microphone (this includes most models of computers, tablets). Classes will take place with a "Live" instructor at the date/times listed below.

Upon registration, the instructor will send along additional information about how to log-on and participate in the class.

Refund Policy

Cancellation / Reschedule by Student

Because we must schedule rooms, ship course materials, assign instructors and provision computer resources well in advance, it is important students alert us ASAP with any cancel/reschedule requests.

Please note the following policies:

  • If requested 16 or more calendar days before the class
    • Cancellation requests made in this timeframe are eligible for refund less 5% cancellation fee.
    • Reschedule requests made in this timeframe will be accommodated if availability exists and will be processed at no additional charge.
  • If requested 15 calendar days or less before the class
    • Cancellation or Reschedule requests made by the student 15 calendar days or less before the class starts will be charged 100% the course fee and are not entitled to a refund.

Substitutions

  • If you are unable to attend your class, a substitute who meets the course prerequisites may attend in your place at no additional charge for the original class title, date and location. If you cannot find a substitute, you will be subject to the class cancellation policy.

Make-up Policy

Students are permitted one (1) Make-Up opportunity to attend an instructor-led class that was paid for but not previously attended for no additional charge.

  • Make-Up class registrations are subject to availability and are not entitled to refund.
  • Students may not reschedule a Make-up class registration.
  • Any no shows or cancellations forfeit their one Make-up opportunity.

Additionally, ONLC reserves the right to retire courses due to lack of demand. Retired classes are not available for Make-Up.

In the event that an instructor or the school needs to cancel at the last minute, students will receive a refund less a 5% non-refundable registration fee.

In any event where a customer wants to cancel their enrollment and is eligible for a full refund, a 5% processing fee will be deducted from the refund amount.

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