1.1 Introduction
1.2 Linux, PolyBase, and Active Directory
1.3 Scenarios
Dive into the depths of SQL Server with this Microsoft SQL – SQL Big Data course and discover one of its most invaluable tools, SQL Big Data Clusters. Here, you will fully explore data virtualization and lakes in order to build a complete artificial intelligence (AI) and machine learning (ML) platform directly within the SQL Server database engine.
What you’ll
learn
Microsoft SQL Database Design
Introduction to Microsoft Power BI
Querying SQL Server With T-SQL – Master The SQL Syntax
Microsoft SQL Database Administration : Optimize Your SQL Server Skills
Microsoft Server – SQL Data Analysis
Microsoft SQL – SQL Big Data
SSAS : Microsoft SQL Server Analysis Services
The course begins by answering the fundamental question: what is big data analytics? You’ll learn the big data definition and big data meaning, and how it differs from traditional data analysis. This section will also introduce you to analytics big data, explaining how it can be used for effective decision-making.
1.1 Introduction
1.2 Linux, PolyBase, and Active Directory
1.3 Scenarios
2.1 Introduction
2.2 Docker
2.3 Kubernetes
2.4 Hadoop and Spark
2.5 Components
2.6 Endpoints
3.1 Introduction
3.2 Install Prerequisites
3.3 Deploy Kubernetes
3.4 Deploy BDC
3.5 Monitor and Verify Deployment
4.1 Introduction
4.2 HDFS with Curl
4.3 Loading Data with T-SQL
4.4 Virtualizing Data
4.5 Restoring a Database
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
6.1 Introduction
6.2 Machine Learning Services
6.3 Using MLeap
6.4 Using Python
6.5 Using R
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
8.1 Introduction
8.2 Monitoring
8.3 Managing and Automation
8.4 Course Wrap Up
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As software testers gain experience and develop their skills, they can take on more challenging roles and responsibilities. This can lead to promotions and career advancement opportunities. Most Manual testers progress to QA automation, Software development, DevOps, or Cloud Engineering.
The course primarily focuses on SQL Big Data Clusters, an impactful feature of SQL Server. It aims to teach students about data virtualization and data lakes, which are used to build a comprehensive AI and ML platform within the SQL Server database engine.
This course is perfect for data engineers, data scientists, data architects, and database administrators. It’s especially beneficial for those who want to apply data virtualization and big data analytics in their environments.
The course covers a variety of topics, including understanding what a Big Data Cluster is, how to deploy and manage it, and how to analyze large volumes of data directly from SQL Server or via Apache Spark. It also shows how to implement advanced analytics solutions through machine learning, and how to expose different data sources as a single logical source using data virtualization.
Your instructor will be James Ring-Howell, a Microsoft Certified Trainer and Developer with over 40 years of experience in the field. He has developed applications for a variety of industries and has been teaching technology courses for over 20 years.
The course is divided into 8 modules, each focusing on a specific aspect of Big Data Clusters. It starts with an introduction to Big Data Clusters and their architecture, then moves on to deployment, data loading and querying, working with Spark, machine learning, creating and consuming Big Data Cluster Apps, and finally maintenance of Big Data Clusters.
The course includes 7 training hours, presented across 41 videos and 8 topics. Additionally, there are 75 practice questions to help reinforce your understanding of the material.
