Data Engineering on Microsoft Azure (Live Online)

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 EngineerPreparation for exam: DP-203Skills gainedExplore compute and storage options for data engineering workloads in AzureDesign and Implement the serving layerUnderstand data engineering considerationsPrerequisites: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 FundamentalsCourse outline:Module 1: Explore compute and storage options for data engineering workloadsThis 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.LessonsIntroduction to Azure Synapse AnalyticsDescribe Azure DatabricksIntroduction to Azure Data Lake storageDescribe Delta Lake architectureWork with data streams by using Azure Stream AnalyticsLab : Explore compute and storage options for data engineering workloadsCombine streaming and batch processing with a single pipelineOrganize the data lake into levels of file transformationIndex data lake storage for query and workload accelerationModule 2: Design and implement the serving layerThis 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.LessonsDesign a multidimensional schema to optimize analytical workloadsCode-free transformation at scale with Azure Data FactoryPopulate slowly changing dimensions in Azure Synapse Analytics pipelinesLab : Designing and Implementing the Serving LayerDesign a star schema for analytical workloadsPopulate slowly changing dimensions with Azure Data Factory and mapping data flowsModule 3: Data engineering considerations for source filesThis 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.LessonsDesign a Modern Data Warehouse using Azure Synapse AnalyticsSecure a data warehouse in Azure Synapse AnalyticsLab : Data engineering considerationsManaging files in an Azure data lakeSecuring files stored in an Azure data lakeModule 4: Run interactive queries using Azure Synapse Analytics serverless SQL poolsIn 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).LessonsExplore Azure Synapse serverless SQL pools capabilitiesQuery data in the lake using Azure Synapse serverless SQL poolsCreate metadata objects in Azure Synapse serverless SQL poolsSecure data and manage users in Azure Synapse serverless SQL poolsLab : Run interactive queries using serverless SQL poolsQuery Parquet data with serverless SQL poolsCreate external tables for Parquet and CSV filesCreate views with serverless SQL poolsSecure access to data in a data lake when using serverless SQL poolsConfigure data lake security using Role-Based Access Control (RBAC) and Access Control ListModule 5: Explore, transform, and load data into the Data Warehouse using Apache SparkThis 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.LessonsUnderstand big data engineering with Apache Spark in Azure Synapse AnalyticsIngest data with Apache Spark notebooks in Azure Synapse AnalyticsTransform data with DataFrames in Apache Spark Pools in Azure Synapse AnalyticsIntegrate SQL and Apache Spark pools in Azure Synapse AnalyticsLab : Explore, transform, and load data into the Data Warehouse using Apache SparkPerform Data Exploration in Synapse StudioIngest data with Spark notebooks in Azure Synapse AnalyticsTransform data with DataFrames in Spark pools in Azure Synapse AnalyticsIntegrate SQL and Spark pools in Azure Synapse AnalyticsModule 6: Data exploration and transformation in Azure DatabricksThis 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.LessonsDescribe Azure DatabricksRead and write data in Azure DatabricksWork with DataFrames in Azure DatabricksWork with DataFrames advanced methods in Azure DatabricksLab : Data Exploration and Transformation in Azure DatabricksUse DataFrames in Azure Databricks to explore and filter dataCache a DataFrame for faster subsequent queriesRemove duplicate dataManipulate date/time valuesRemove and rename DataFrame columnsAggregate data stored in a DataFrameModule 7: Ingest and load data into the data warehouseThis 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.LessonsUse data loading best practices in Azure Synapse AnalyticsPetabyte-scale ingestion with Azure Data FactoryLab : Ingest and load Data into the Data WarehousePerform petabyte-scale ingestion with Azure Synapse PipelinesImport data with PolyBase and COPY using T-SQLUse data loading best practices in Azure Synapse AnalyticsModule 8: Transform data with Azure Data Factory or Azure Synapse PipelinesThis 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.LessonsData integration with Azure Data Factory or Azure Synapse PipelinesCode-free transformation at scale with Azure Data Factory or Azure Synapse PipelinesLab : Transform Data with Azure Data Factory or Azure Synapse PipelinesExecute code-free transformations at scale with Azure Synapse PipelinesCreate data pipeline to import poorly formatted CSV filesCreate Mapping Data FlowsModule 9: Orchestrate data movement and transformation in Azure Synapse PipelinesIn this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.LessonsOrchestrate data movement and transformation in Azure Data FactoryLab : Orchestrate data movement and transformation in Azure Synapse PipelinesIntegrate Data from Notebooks with Azure Data Factory or Azure Synapse PipelinesModule 10: Optimize query performance with dedicated SQL pools in Azure SynapseIn 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.LessonsOptimize data warehouse query performance in Azure Synapse AnalyticsUnderstand data warehouse developer features of Azure Synapse AnalyticsLab : Optimize Query Performance with Dedicated SQL Pools in Azure SynapseUnderstand developer features of Azure Synapse AnalyticsOptimize data warehouse query performance in Azure Synapse AnalyticsImprove query performanceModule 11: Analyze and Optimize Data Warehouse StorageIn 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 AnalyticsLab : Analyze and Optimize Data Warehouse StorageCheck for skewed data and space usageUnderstand column store storage detailsStudy the impact of materialized viewsExplore rules for minimally logged operationsModule 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse LinkIn 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.LessonsDesign hybrid transactional and analytical processing using Azure Synapse AnalyticsConfigure Azure Synapse Link with Azure Cosmos DBQuery Azure Cosmos DB with Apache Spark poolsQuery Azure Cosmos DB with serverless SQL poolsLab : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse LinkConfigure Azure Synapse Link with Azure Cosmos DBQuery Azure Cosmos DB with Apache Spark for Synapse AnalyticsQuery Azure Cosmos DB with serverless SQL pool for Azure Synapse AnalyticsModule 13: End-to-end security with Azure Synapse AnalyticsIn 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.LessonsSecure a data warehouse in Azure Synapse AnalyticsConfigure and manage secrets in Azure Key VaultImplement compliance controls for sensitive dataLab : End-to-end security with Azure Synapse AnalyticsSecure Azure Synapse Analytics supporting infrastructureSecure the Azure Synapse Analytics workspace and managed servicesSecure Azure Synapse Analytics workspace dataModule 14: Real-time Stream Processing with Stream AnalyticsIn 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.LessonsEnable reliable messaging for Big Data applications using Azure Event HubsWork with data streams by using Azure Stream AnalyticsIngest data streams with Azure Stream AnalyticsLab : Real-time Stream Processing with Stream AnalyticsUse Stream Analytics to process real-time data from Event HubsUse Stream Analytics windowing functions to build aggregates and output to Synapse AnalyticsScale the Azure Stream Analytics job to increase throughput through partitioningRepartition the stream input to optimize parallelizationModule 15: Create a Stream Processing Solution with Event Hubs and Azure DatabricksIn 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.LessonsProcess streaming data with Azure Databricks structured streamingLab : Create a Stream Processing Solution with Event Hubs and Azure DatabricksExplore key features and uses of Structured StreamingStream data from a file and write it out to a distributed file systemUse sliding windows to aggregate over chunks of data rather than all dataApply watermarking to remove stale dataConnect to Event Hubs read and write streamsModule 16: Build reports using Power BI integration with Azure Synapase AnalyticsIn 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.LessonsCreate reports with Power BI using its integration with Azure Synapse AnalyticsLab : Build reports using Power BI integration with Azure Synapase AnalyticsIntegrate an Azure Synapse workspace and Power BIOptimize integration with Power BIImprove query performance with materialized views and result-set cachingVisualize data with SQL serverless and create a Power BI reportModule 17: Perform Integrated Machine Learning Processes in Azure Synapse AnalyticsThis 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.LessonsUse the integrated machine learning process in Azure Synapse AnalyticsLab : Perform Integrated Machine Learning Processes in Azure Synapse AnalyticsCreate an Azure Machine Learning linked serviceTrigger an Auto ML experiment using data from a Spark tableEnrich data using trained modelsServe prediction results using Power BI