Introduction to SQL Big Data & Analytics

Course Level: Beginner
Duration: 7 Hrs 6 Min
Total Videos: 41 On-demand Videos

Master the power of Microsoft SQL Server 2019 with our comprehensive course on Big Data Clusters. Perfect for both beginners and professionals, this course equips database administrators, data scientists, and IT professionals with the skills to manage, deploy, and leverage big data analytics for career advancement.

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Learning Objectives

01

Understand the fundamental concepts and applications of Big Data Clusters.

02

Gain knowledge on Big Data Cluster Architecture including Docker, Kubernetes, Hadoop, and Spark.

03

Learn how to successfully deploy Big Data Clusters and verify their deployment.

04

Acquire skills to load and query data in Big Data Clusters using HDFS, T-SQL, and more.

05

Learn how to work with Spark in Big Data Clusters, including submitting and running Spark jobs.

06

Understand the application of machine learning in Big Data Clusters using Python, R, and MLeap.

07

Develop the ability to create, deploy, and monitor Big Data Cluster applications using various tools.

08

Learn how to maintain Big Data Clusters, including monitoring, managing, and automating tasks.

Course Description

Welcome to the comprehensive “Microsoft SQL Server 2019 – Big Data Clusters” course. This course provides a deep dive into the world of big data, leveraging the power of Microsoft SQL Server 2019. You will learn about a wide range of topics including Linux, Docker, Kubernetes, Hadoop, Spark, and Machine Learning Services. The course is designed to equip you with the skills and knowledge necessary to deploy, monitor, and manage big data clusters.

Ideal for database administrators, data engineers, IT professionals, and beginners interested in learning about big data technologies, this course offers valuable practical experience. You will learn how to load, query, and transform data, and you will be guided through real-world scenarios involving data virtualization and Spark job deployment. Additionally, the course provides hands-on experience in creating machine learning models using Python, R, and MLeap.

By completing this course, you will gain crucial skills in handling big data using SQL Server 2019. Understanding the architecture and components of big data clusters, deploying and configuring Kubernetes and Docker, and working with Spark for data transformation and analysis are just a few of the many skills you will acquire. Additionally, the course could open up numerous career opportunities for you in industries that value professionals with big data skills, such as Big Data Engineer, Database Administrator, Data Analyst, Data Scientist, Machine Learning Engineer, and Business Intelligence Developer. Boost your career by enrolling in this course today.

Who Benefits From This Course

  • Data Analysts seeking to broaden their understanding of big data and analytics
  • Data Scientists who are interested in working with SQL and big data clusters
  • Database Administrators wanting to expand their skill set into big data and analytics
  • IT Professionals who want to deepen their understanding of big data architecture and maintenance
  • Software Developers interested in integrating big data analytics into their applications
  • Machine Learning Engineers who want to leverage big data clusters for their projects
  • Professionals in the field of Business Intelligence looking to gain insights from big data analysis

Frequently Asked Questions

What is Big Data and why is it important?
Big Data refers to large and complex datasets that traditional data processing software cannot manage. It's characterized by volume, velocity, and variety. The importance of Big Data lies in how an organization uses it to derive insights that can lead to better decisions and strategic moves. With the advent of Microsoft SQL Server 2019, managing and interpreting big data has become easier and more accessible.
What job roles can benefit from knowledge in Big Data and SQL Server 2019?
Several job roles can benefit from knowledge in Big Data and SQL Server 2019. These include:
  • Big Data Engineer
  • Database Administrator
  • Data Analyst
  • Data Scientist
  • Machine Learning Engineer
  • BI Developer
These professions span across a variety of industries, providing numerous paths for career advancement.
What are the features of Microsoft SQL Server 2019 that make it suitable for managing Big Data?
Microsoft SQL Server 2019 enhances the management of Big Data through its Big Data Clusters feature. It allows for the deployment of scalable clusters of SQL Server, Spark, and HDFS containers running on Kubernetes. This facilitates data processing over both relational and non-relational data, offering a unified data platform. It also provides advanced analytics and machine learning capabilities, making it a powerful tool for big data management.
What skills will I gain from learning Big Data Analytics using SQL Server 2019?
By learning Big Data analytics using SQL Server 2019, you will gain valuable skills and knowledge in handling big data. You’ll understand the architecture and components of big data clusters, learn to deploy and configure Kubernetes and Docker, and work with Spark for data transformation and analysis. Furthermore, you’ll get hands-on experience in implementing machine learning models using Python, R, and MLeap.
How is Big Data transforming the business landscape?
Big Data is radically transforming the business landscape. It enables businesses to make informed decisions by providing insights into market trends, customer behavior, and operational efficiency. Companies can use big data analytics to identify new revenue opportunities, improve customer service, predict future trends, and even outperform competition. As such, professionals who can manage and interpret big data are in high demand in today's job market.

Included In This Course

Module 1: What are Big Data Clusters?

  •    1.1 Introduction
  •    1.2 Linux, PolyBase, and Active Directory
  •    1.3 Scenarios

Module 2: Big Data Cluster Architecture

  •    2.1 Introduction
  •    2.2 Docker
  •    2.3 Kubernetes
  •    2.4 Hadoop and Spark
  •    2.5 Components
  •    2.6 Endpoints

Module 3: Deployment of Big Data Clusters

  •    3.1 Introduction
  •    3.2 Install Prerequisites
  •    3.3 Deploy Kubernetes
  •    3.4 Deploy BDC
  •    3.5 Monitor and Verify Deployment

Module 4: Loading and Querying Data in Big Data Clusters

  •    4.1 Introduction
  •    4.2 HDFS with Curl
  •    4.3 Loading Data with T-SQL
  •    4.4 Virtualizing Data
  •    4.5 Restoring a Database

Module 5: Working with Spark in Big Data Clusters

  •    5.1 Introduction
  •    5.2 What is Spark
  •    5.3 Submitting Spark Jobs
  •    5.4 Running Spark Jobs via Notebooks
  •    5.5 Transforming CSV
  •    5.6 Spark-SQL
  •    5.7 Spark to SQL ETL

Module 6: Machine Learning on Big Data Clusters

  •    6.1 Introduction
  •    6.2 Machine Learning Services
  •    6.3 Using MLeap
  •    6.4 Using Python
  •    6.5 Using R

Module 7: Create and Consume Big Data Cluster Apps

  •    7.1 Introduction
  •    7.2 Deploying, Running, Consuming, and Monitoring an App
  •    7.3 Python Example - Deploy with azdata and Monitoring
  •    7.4 R Example - Deploy with VS Code and Consume with Postman
  •    7.5 MLeap Example - Create a yaml file
  •    7.6 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

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