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Microsoft SQL 2019 – Big Data

41 Videos
7.6 Hours
75 Test Questions

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Certificate

Dedicated Tutors

Microsoft SQL 2019 – Big Data

Course Highlights

Closed Caption

Certificate

Dedicated Tutors

7.6 Hours
41 Videos

Microsoft SQL 2019 – Big Data

Course Description

7.6 Hours

41 Videos

Microsoft SQL 2019 – Big Data

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. We will show you 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 comprising Kubernetes, Spark, HDFS, and SQL Server on Linux. We will show you 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

Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments should take this course.

Course Syllabus

Module 1: What are Big Data Clusters?

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

Module 2: Big Data Cluster Architecture

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

Module 3: Deployment of Big Data Clusters

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

Module 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

Module 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

Module 6: Machine Learning on Big Data Clusters

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

Module 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 scheduled execution of a DB backup

Module 8: Maintenance of Big Data Clusters

  •  8.1 Introduction
  •  8.2 Monitoring
  •  8.3 Managing and Automation
  •  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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Frequently Asked Questions

Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.

Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.

All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.

The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.

Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
Our platform is typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.

Frequently Asked Questions

Instructional methods, course requirements, and learning technologies can vary significantly from one online program to the next, but the vast bulk of them use a learning management system (LMS) to deliver lectures and materials, monitor student progress, assess comprehension, and accept student work. LMS providers design these platforms to accommodate a multitude of instructor needs and preferences.

Online education may seem relatively new, but years of research suggests it can be just as effective as traditional coursework, and often more so. According to a U.S. Department of Education analysis of more than 1,000 learning studies, online students tend to outperform classroom-based students across most disciplines and demographics. Another major review published the same year found that online students had the advantage 70 percent of the time, a gap authors projected would only widen as programs and technologies evolve.

All new learning innovations are met with some degree of scrutiny, but skepticism subsides as methods become more mainstream. Such is the case for online learning. Studies indicate employers who are familiar with online degrees tend to view them more favorably, and more employers are acquainted with them than ever before. The majority of colleges now offer online degrees, including most public, not-for-profit, and Ivy League universities. Online learning is also increasingly prevalent in the workplace as more companies invest in web-based employee training and development programs.

The concern that online students cheat more than traditional students is perhaps misplaced. When researchers at Marshall University conducted a study to measure the prevalence of cheating in online and classroom-based courses, they concluded, “Somewhat surprisingly, the results showed higher rates of academic dishonesty in live courses.” The authors suggest the social familiarity of students in a classroom setting may lessen their sense of moral obligation.

Choosing the right course takes time and careful research no matter how one intends to study. Learning styles, goals, and programs always vary, but students considering online courses must consider technical skills, ability to self-motivate, and other factors specific to the medium. Online course demos and trials can also be helpful.
Our platform is typically designed to be as user-friendly as possible: intuitive controls, clear instructions, and tutorials guide students through new tasks. However, students still need basic computer skills to access and navigate these programs. These skills include: using a keyboard and a mouse; running computer programs; using the Internet; sending and receiving email; using word processing programs; and using forums and other collaborative tools. Most online programs publish such requirements on their websites. If not, an admissions adviser can help.

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Description

Microsoft SQL 2019 – Big Data

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. We will show you 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 comprising Kubernetes, Spark, HDFS, and SQL Server on Linux. We will show you 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

Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments should take this course.

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