Perform Data Engineering on Microsoft HD Insight (M20775)
The main purpose of the course is to give students the ability plan and implement big data workflows on HDInsight.
The primary audience for this course is data engineers, data architects, data scientists, and data developers who plan to implement big data engineering workflows on HDInsight.
- Programming experience using R, and familiarity with common R packages
- Knowledge of common statistical methods and data analysis best practices.
- Basic knowledge of the Microsoft Windows operating system and its core functionality.
- Working knowledge of relational databases.
- Deploy HDInsight Clusters.
- Authorizing Users to Access Resources.
- Loading Data into HDInsight.
- Troubleshooting HDInsight.
- Implement Batch Solutions.
- Design Batch ETL Solutions for Big Data with Spark
- Analyze Data with Spark SQL.
- Analyze Data with Hive and Phoenix.
- Describe Stream Analytics.
- Implement Spark Streaming Using the DStream API.
- Develop Big Data Real-Time Processing Solutions with Apache Storm.
- Build Solutions that use Kafka and HBase.
1: Getting Started with HDInsight
- What is Big Data?
- Introduction to Hadoop
- Working with MapReduce Function
- Introducing HDInsight
2: Deploying HDInsight Clusters
- Identifying HDInsight cluster types
- Managing HDInsight clusters by using the Azure portal
- Managing HDInsight Clusters by using Azure PowerShell
3: Authorizing Users to Access Resources
- Non-domain Joined clusters
- Configuring domain-joined HDInsight clusters
- Manage domain-joined HDInsight clusters
4: Loading data into HDInsight
- Storing data for HDInsight processing
- Using data loading tools
- Maximising value from stored data
- Compress and serialize uploaded data for decreased processing time.
5: Troubleshooting HDInsight
- Analyze HDInsight logs
- YARN logs
- Heap dumps
- Operations management suite
6: Implementing Batch Solutions
- Apache Hive storage
- HDInsight data queries using Hive and Pig
- Operationalize HDInsight
7: Design Batch ETL solutions for big data with Spark
- What is Spark?
- ETL with Spark
- Spark performance
8: Analyze Data with Spark SQL
- Implementing iterative and interactive queries
- Perform exploratory data analysis
9: Analyze Data with Hive and Phoenix
- Implement interactive queries for big data with interactive hive.
- Perform exploratory data analysis by using Hive
- Perform interactive processing by using Apache Phoenix
10: Stream Analytics
- Stream analytics
- Process streaming data from stream analytics
- Managing stream analytics jobs
11: Implementing Streaming Solutions with Kafka and HBase
- Building and Deploying a Kafka Cluster
- Publishing, Consuming, and Processing data using the Kafka Cluster
- Using HBase to store and Query Data
12: Develop big data real-time processing solutions with Apache Storm
- Persist long term data
- Stream data with Storm
- Create Storm topologies
- Configure Apache Storm
13: Create Spark Streaming Applications
- Working with Spark Streaming
- Creating Spark Structured Streaming Applications
- Persistence and Visualization