Microsoft SQL Server 2019 – Big Data
Microsoft SQL Server 2019 – Big Data, available at $19.99, has an average rating of 3.5, with 41 lectures, 1 quizzes, based on 1 reviews, and has 22 subscribers.
You will learn about Learn about data virtualization and data lakes Learn how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. Learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux Learn how to configure and deploy Big Data Clusters. This course is ideal for individuals who are 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. It is particularly useful for 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.
Enroll now: Microsoft SQL Server 2019 – Big Data
Summary
Title: Microsoft SQL Server 2019 – Big Data
Price: $19.99
Average Rating: 3.5
Number of Lectures: 41
Number of Quizzes: 1
Number of Published Lectures: 41
Number of Published Quizzes: 1
Number of Curriculum Items: 42
Number of Published Curriculum Objects: 42
Number of Practice Tests: 1
Number of Published Practice Tests: 1
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn about data virtualization and data lakes
- Learn how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database.
- Learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux
- Learn how to configure and deploy Big Data Clusters.
Who Should Attend
- 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.
Target Audiences
- 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.
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.
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What a Big Data Cluster is
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How to deploy BDC
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How to analyze large volumes of data directly from SQL Server
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How to analyze large volumes of data via Apache Spark
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How to manage data stored in HDFS from SQL Server as if it were relational data
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How to implement advanced analytics solutions through machine learning
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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 Curriculum
Chapter 1: Module 1: What are Big Data Clusters?
Lecture 1: 1.1 Introduction
Lecture 2: 1.2 Linux, PolyBase, and Active Directory
Lecture 3: 1.3 Scenarios
Chapter 2: Module 2: Big Data Cluster Architecture
Lecture 1: 2.1 Introduction
Lecture 2: 2.2 Docker
Lecture 3: 2.3 Kubernetes
Lecture 4: 2.4 Hadoop and Spark
Lecture 5: 2.5 Components
Lecture 6: 2.6 Endpoints
Chapter 3: Module 3: Deployment of Big Data Clusters
Lecture 1: 3.1 Introduction
Lecture 2: 3.2 Install Prerequisites
Lecture 3: 3.3 Deploy Kubernetes
Lecture 4: 3.4 Deploy BDC
Lecture 5: 3.5 Monitor and Verify Deployment
Chapter 4: Module 4: Loading and Querying Data in Big Data Clusters
Lecture 1: 4.1 Introduction
Lecture 2: 4.2 HDFS with Curl
Lecture 3: 4.3 Loading Data with T-SQL
Lecture 4: 4.4 Virtualizing Data
Lecture 5: 4.5 Restoring a Database
Chapter 5: Module 5: Working with Spark in Big Data Clusters
Lecture 1: 5.1 Introduction
Lecture 2: 5.2 What is Spark
Lecture 3: 5.3 Submitting Spark Jobs
Lecture 4: 5.4 Running Spark Jobs via Notebooks
Lecture 5: 5.5 Transforming CSV
Lecture 6: 5.6 Spark-SQL
Lecture 7: 5.7 Spark to SQL ETL
Chapter 6: Module 6: Machine Learning on Big Data Clusters
Lecture 1: 6.1 Introduction
Lecture 2: 6.2 Machine Learning Services
Lecture 3: 6.3 Using MLeap
Lecture 4: 6.4 Using Python
Lecture 5: 6.5 Using R
Chapter 7: Module 7: Create and Consume Big Data Cluster Apps
Lecture 1: 7.1 Introduction
Lecture 2: 7.2 Deploying, Running, Consuming, and Monitoring an App
Lecture 3: 7.3 Python Example – Deploy with azdata and Monitoring
Lecture 4: 7.4 R Example – Deploy with VS Code and Consume with Postman
Lecture 5: 7.5 MLeap Example – Create a yaml file
Lecture 6: 7.6 SSIS Example – Implement scheduled execution of a DB backup
Chapter 8: Module 8: Maintenance of Big Data Clusters
Lecture 1: 8.1 Introduction
Lecture 2: 8.2 Monitoring
Lecture 3: 8.3 Managing and Automation
Lecture 4: 8.4 Course Wrap Up
Chapter 9: Microsoft SQL Server 2019 – Big Data Final Practice Test
Instructors
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Vision Training Systems Technology Institute Online dba
Creator of Technical and Creative Content
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- 3 stars: 1 votes
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