Microsoft SQL Server 2019 - Big Data

Curriculum

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