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    This course focuses on one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will be shown how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For instance, you can stream large volumes of data from Apache Spark in real-time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. This course provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You will be shown how to configure and deploy Big Data Clusters.  You will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis.

    • What a Big Data Cluster is
    • How to deploy BDC
    • How to analyze large volumes of data directly from SQL Server
    • How to analyze large volumes of data via Apache Spark
    • How to manage data stored in HDFS from SQL Server as if it were relational data
    • How to implement advanced analytics solutions through machine learning
    • How to expose different data sources as a single logical source using data virtualization

    This course is intended for data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments.

    Course Syllabus

    Modole 1: What are Big Data Clusters?

    1. Introduction
    2. Linux, PolyBase, and Active Directory
    3. Scenarios

    Modole 2: Big Data Cluster Architecture

    1. Introduction
    2. Docker
    3. Kubernetes
    4. Hadoop and Spark
    5. Components
    6. Endpoints

    Modole 3: Deployment of Big Data Clusters

    1. Introduction
    2. Install Prerequisites
    3. Deploy Kubernetes
    4. Deploy BDC
    5. Monitor and Verify Deployment

    Modole 4: Loading and Querying Data in Big Data Clusters

    1. Introduction
    2. HDFS with Curl
    3. Loading Data with T-SQL
    4. Virtualizing Data
    5. Restoring a Database

    Modole 5: Working with Spark in Big Data Clusters

    1. Introduction
    2. What is Spark
    3. Submitting Spark Jobs
    4. Running Spark Jobs via Notebooks
    5. Transforming CSV
    6. Spark-SQL
    7. Spark to SQL ETL

    Modole 6: Machine Learning on Big Data Clusters

    1. Introduction
    2. Machine Learning Services
    3. Using MLeap
    4. Using Python
    5. Using R

    Modole 7: Create and Consume Big Data Cluster Apps

    1. Introduction
    2. Deploying, Running, Consuming, and Monitoring an App
    3. Python Example – Deploy with azdata and Monitoring
    4. R Example – Deploy with VS Code and Consume with Postman
    5. MLeap Example – Create a yaml file
    6. SSIS Example – Implement schedoled execution of a DB backup

    Modole 8: Maintenance of Big Data Clusters

    1. 8.1 Introduction
    2. 8.2 Monitoring
    3. 8.3 Managing and Automation
    4. 8.4 Course Wrap Up

    Starting with SQL Server 2019 (15.x), SQL Server Big Data Clusters allow you to deploy scalable clusters of SQL Server, Spark, and HDFS containers running on Kubernetes. These components are running side by side to enable you to read, write, and process big data from Transact-SQL or Spark, allowing you to easily combine and analyze your high-value relational data with high-volume big data.

    The SQL Server 2019 relational database engine in a big data cluster leverages an elastically scalable storage layer that integrates SQL Server and HDFS to scale to petabytes of data storage. The Spark engine that is now part of SQL Server enables data engineers and data scientists to harness the power of open source data preparation and query programming libraries to process and analyze high-volume data in a scalable, distributed, in-memory compute layer.


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