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.

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 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

Vision Training Systems Technology Institute Online dba
Creator of Technical and Creative Content

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