Course #:WA3060 Implementing Data Engineering on Azure Training Overview Data Engineer role requires various skills. The Azure Data Engineer Associate Certificate covers only a subset of these critical skills. Identified gaps in the Data Engineering job profiles include Azure Data Factory, HDInsight, databases such as MongoDB as well as real-time and streaming solutions to fulfill their responsibilities. This course is focused on addressing that gap. Audience Anyone who either has obtained the Azure Data Engineer Associate Certificate or is working towards it. Prerequisites DP-900. Having equivalent knowledge of DP-300 and DP-050 is very helpful. Duration Five Days Outline of Implementing Data Engineering on Azure Training Chapter 1. Understanding Data Engineering Overview Of Traditional Database Engineer And Bi Developer Roles When Relational Databases And Sql Isn’t Enough Transitioning From The Traditional Database Engineer Role To The Data Engineer Role The Modern Data Sources (Relational, Non-Relational, Real-Time & Streaming) Chapter 2. Azure Data Lake Overview What Is Azure Data Lake Importance Of Data Lake Gen1 Vs. Gen2. Hierarchical Namespace In Gen2 Chapter 3. Using U-Sql What Is The U-Sql Language Write, Run, And Manage Analytics Jobs Extend U-Sql Using Python Chapter 4. Monitoring And Optimizing U-Sql Jobs Schedule U-Sql Jobs Manage U-Sql Jobs Troubleshoot U-Sql Jobs Performance Optimization Chapter 5. Cosmos DB MOC 20777 content (there are 6 modules in the course) Chapter 6. Introduction To Big Data Formats Why Different Formats Emerged The Evolution Of Data Formats Use Cases For Different Formats Understanding Avro Understanding Parquet Understanding Optimized Row Columnar (ORC) Challenges Involved In Converting Formats Chapter 7. Azure Data Factory Overview What Is Azure Data Factory Understanding Automated Data Pipelines Understanding Data Sets Understanding Activities Chapter 8. Developing Azure Data Factory Pipelines Understanding Options For Developing Data Factory Pipelines Setup Source Setup Sink Setup Mappings Validate, Publish, And Test Developing Data Factory Pipelines Using Python Chapter 9. Managing Azure Data Factory Jobs Scheduling Data Factory Jobs Executing Data Factory Jobs Monitoring Data Factory Jobs Understanding Tumbling Windows Understanding Concurrency Understanding Dependency Understanding Troubleshooting Chapter 10. Getting Started on Microsoft HDInsight Introduction to Hadoop Working with MapReduce Function Introduction to HDInsight Understanding HDInsight Cluster Types Deploying HDInsight Clusters Chapter 11. Applying Data Engineering on Microsoft HDInsight Understanding various Data Loading Tools Loading Data into HDInsight Understanding Apache Hive Solutions HDInsight Data Queries using Hive and Pig Chapter 12. Putting It Together Combining Azure Data Factory With Azure HDinsight Combining Azure Data Factory With Azure Machine Learning Transforming And Processing Raw Data Into Predictions And Insights Chapter 13. Implementing Streaming Solutions with Kafka and HBase Introduction to Kafka and HBase Deploying a Kafka Cluster Publishing, Consuming, and Processing Data Storing Data to HBase Querying Data in HBase Chapter 14. Introduction to Streaming Data using Apache Spark Exploring Sources and Sinks Understanding Streaming Data Frames Understanding Window Operations on Frames Introduction to Streaming Joins Monitoring Streaming Queries Chapter 15. Implementing Streaming Solutions with Databricks Introduction to Structured Streaming on Azure Databricks Setting up Azure Databricks Configuring Source and Sink Building Streaming Pipeline on Azure Databricks Working with Timestamps and Windows Understanding Stateful Operations Handling Multiple Streams and Datasets Optimizing Streaming Pipeline for Production Use Chapter 16. Implementing Real-time Processing Solutions with Apache Storm How to Persist Long-term Data Streaming Data with Apache Storm Understanding Apache Storm Topologies Creating Apache Storm Topologies Configuring Apache Storm We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC. View Course Outline Share This Request On-Site or Customized Course Info REGISTER FOR A COURSEWARE SAMPLE x Sent First Name Last Name Email Request On-Site or Customized Course Info x Sent First Name Last Name Phone Number Company Name Email Question
Course #:WA3060 Implementing Data Engineering on Azure Training Overview Data Engineer role requires various skills. The Azure Data Engineer Associate Certificate covers only a subset of these critical skills. Identified gaps in the Data Engineering job profiles include Azure Data Factory, HDInsight, databases such as MongoDB as well as real-time and streaming solutions to fulfill their responsibilities. This course is focused on addressing that gap. Audience Anyone who either has obtained the Azure Data Engineer Associate Certificate or is working towards it. Prerequisites DP-900. Having equivalent knowledge of DP-300 and DP-050 is very helpful. Duration Five Days Outline of Implementing Data Engineering on Azure Training Chapter 1. Understanding Data Engineering Overview Of Traditional Database Engineer And Bi Developer Roles When Relational Databases And Sql Isn’t Enough Transitioning From The Traditional Database Engineer Role To The Data Engineer Role The Modern Data Sources (Relational, Non-Relational, Real-Time & Streaming) Chapter 2. Azure Data Lake Overview What Is Azure Data Lake Importance Of Data Lake Gen1 Vs. Gen2. Hierarchical Namespace In Gen2 Chapter 3. Using U-Sql What Is The U-Sql Language Write, Run, And Manage Analytics Jobs Extend U-Sql Using Python Chapter 4. Monitoring And Optimizing U-Sql Jobs Schedule U-Sql Jobs Manage U-Sql Jobs Troubleshoot U-Sql Jobs Performance Optimization Chapter 5. Cosmos DB MOC 20777 content (there are 6 modules in the course) Chapter 6. Introduction To Big Data Formats Why Different Formats Emerged The Evolution Of Data Formats Use Cases For Different Formats Understanding Avro Understanding Parquet Understanding Optimized Row Columnar (ORC) Challenges Involved In Converting Formats Chapter 7. Azure Data Factory Overview What Is Azure Data Factory Understanding Automated Data Pipelines Understanding Data Sets Understanding Activities Chapter 8. Developing Azure Data Factory Pipelines Understanding Options For Developing Data Factory Pipelines Setup Source Setup Sink Setup Mappings Validate, Publish, And Test Developing Data Factory Pipelines Using Python Chapter 9. Managing Azure Data Factory Jobs Scheduling Data Factory Jobs Executing Data Factory Jobs Monitoring Data Factory Jobs Understanding Tumbling Windows Understanding Concurrency Understanding Dependency Understanding Troubleshooting Chapter 10. Getting Started on Microsoft HDInsight Introduction to Hadoop Working with MapReduce Function Introduction to HDInsight Understanding HDInsight Cluster Types Deploying HDInsight Clusters Chapter 11. Applying Data Engineering on Microsoft HDInsight Understanding various Data Loading Tools Loading Data into HDInsight Understanding Apache Hive Solutions HDInsight Data Queries using Hive and Pig Chapter 12. Putting It Together Combining Azure Data Factory With Azure HDinsight Combining Azure Data Factory With Azure Machine Learning Transforming And Processing Raw Data Into Predictions And Insights Chapter 13. Implementing Streaming Solutions with Kafka and HBase Introduction to Kafka and HBase Deploying a Kafka Cluster Publishing, Consuming, and Processing Data Storing Data to HBase Querying Data in HBase Chapter 14. Introduction to Streaming Data using Apache Spark Exploring Sources and Sinks Understanding Streaming Data Frames Understanding Window Operations on Frames Introduction to Streaming Joins Monitoring Streaming Queries Chapter 15. Implementing Streaming Solutions with Databricks Introduction to Structured Streaming on Azure Databricks Setting up Azure Databricks Configuring Source and Sink Building Streaming Pipeline on Azure Databricks Working with Timestamps and Windows Understanding Stateful Operations Handling Multiple Streams and Datasets Optimizing Streaming Pipeline for Production Use Chapter 16. Implementing Real-time Processing Solutions with Apache Storm How to Persist Long-term Data Streaming Data with Apache Storm Understanding Apache Storm Topologies Creating Apache Storm Topologies Configuring Apache Storm We regularly offer classes in these and other cities. Atlanta, Austin, Baltimore, Calgary, Chicago, Cleveland, Dallas, Denver, Detroit, Houston, Jacksonville, Miami, Montreal, New York City, Orlando, Ottawa, Philadelphia, Phoenix, Pittsburgh, Seattle, Toronto, Vancouver, Washington DC. View Course Outline Share This Request On-Site or Customized Course Info REGISTER FOR A COURSEWARE SAMPLE x Sent First Name Last Name Email Request On-Site or Customized Course Info x Sent First Name Last Name Phone Number Company Name Email Question
