Module 2: Sorting and Filtering Query Results

In the module you will learn how to control what data is returned, the order in which it is returned. You will use the ORDER BY clause, with and without paging. You will learn about various kinds of filters that can be used in the WHERE clause to control which data rows are returned. You will also learn how to manage the results by removing duplicates with DISTINCT.

Lessons

• Sorting query results

• Filtering the data

Lab : Sort and filter data returned by SELECT queries

After completing this module, students will be able to:

• Use ORDER BY to sort results from a T-SQL SELECT statement

• Add a TOP clause to limit the ordered rows returned

• Page the sorted data with OFFSET-FET

• Write WHERE clauses to filter the rows returned

• Use DISTINCT to eliminate duplicate rows in the results

Module 3: Using Joins and Subqueries

In this module, you will explore T-SQL queries which access data from multiple tables with various kinds of JOIN operations and simple subqueries.

Lessons

• Using JOIN operations

• Using subqueries

Lab : Write queries with JOIN operations

Lab : Write SELECT statements using subqueries

After completing this module, students will be able to:

• Write queries accessing data from multiple tables using JOIN operations

• Understand the differences between type of JOIN operations: INNER JOIN, OUTER JOIN, CROSS JOIN

• Understand how to join a table to itself with a self-join

• Write subqueries within a SELECT statement

• Understand the difference between scalar and multi-valued subqueries

• Understand the difference between correlated and self-contained subqueries

Module 4: Using Built-in Functions

In the module you will explore the use of built-in functions for returning computed or special values in the SELECT list or in the WHERE clause. Functions include math functions, string functions and system functions. There are other types of functions that will be mentioned, but not discussed in detail. You will also learn how to combine rows of data into a single group, providing summary information for the group such as SUM, MIN or MAX.

Lessons

• Getting started with scalar functions

• Grouping aggregated results

Lab : Built-in functions

After completing this module, students will be able to:

• Write queries using scalar functions

• Write queries using aggregate functions

• Use GROUP BY to combine data into groups based on a common column value

• Understand how HAVING is used to filter groups of rows

Module 5: Modifying Data

In this module, you will learn the T-SQL statements for modifying table data including UPDATE, DELETE and MERGE as well as various options for INSERT including creating a new table with data from an existing table. You will also look at how to have the system automatically supply values for columns as the data is inserted.

Lessons

• Inserting data into tables

• Modifying and deleting data

Lab : Modify data

After completing this module, students will be able to:

• Insert data into an existing table

• Specify that a column should be automatically populating with an IDENTITY or a SEQUENCE value

• Modify data using the UPDATE statement

• Delete data using the DELETE statement

• Modify data using MERGE to synchronize two tables

Module 2: Run interactive queries using Azure Synapse Analytics serverless SQL pools

In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs).

Lessons

• Explore Azure Synapse serverless SQL pools capabilities

• Query data in the lake using Azure Synapse serverless SQL pools

• Create metadata objects in Azure Synapse serverless SQL pools

• Secure data and manage users in Azure Synapse serverless SQL pools

Lab : Run interactive queries using serverless SQL pools

• Query Parquet data with serverless SQL pools

• Create external tables for Parquet and CSV files

• Create views with serverless SQL pools

• Secure access to data in a data lake when using serverless SQL pools

• Configure data lake security using Role-Based Access Control (RBAC) and Access Control List

After completing this module, students will be able to:

• Understand Azure Synapse serverless SQL pools capabilities

• Query data in the lake using Azure Synapse serverless SQL pools

• Create metadata objects in Azure Synapse serverless SQL pools

• Secure data and manage users in Azure Synapse serverless SQL pools
  
  

Module 3: Data exploration and transformation in Azure Databricks

This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data.

Lessons

• Describe Azure Databricks

• Read and write data in Azure Databricks

• Work with DataFrames in Azure Databricks

• Work with DataFrames advanced methods in Azure Databricks

Lab : Data Exploration and Transformation in Azure Databricks

• Use DataFrames in Azure Databricks to explore and filter data

• Cache a DataFrame for faster subsequent queries

• Remove duplicate data

• Manipulate date/time values

• Remove and rename DataFrame columns

• Aggregate data stored in a DataFrame

After completing this module, students will be able to:

• Describe Azure Databricks

• Read and write data in Azure Databricks

• Work with DataFrames in Azure Databricks

• Work with DataFrames advanced methods in Azure Databricks

Module 4: Explore, transform, and load data into the Data Warehouse using Apache Spark

This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool.

Lessons

• Understand big data engineering with Apache Spark in Azure Synapse Analytics

• Ingest data with Apache Spark notebooks in Azure Synapse Analytics

• Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics

• Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Lab : Explore, transform, and load data into the Data Warehouse using Apache Spark

• Perform Data Exploration in Synapse Studio

• Ingest data with Spark notebooks in Azure Synapse Analytics

• Transform data with DataFrames in Spark pools in Azure Synapse Analytics

• Integrate SQL and Spark pools in Azure Synapse Analytics

After completing this module, students will be able to:

• Describe big data engineering with Apache Spark in Azure Synapse Analytics

• Ingest data with Apache Spark notebooks in Azure Synapse Analytics

• Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics

• Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Module 5: Ingest and load data into the data warehouse

This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion.

Lessons

• Use data loading best practices in Azure Synapse Analytics

• Petabyte-scale ingestion with Azure Data Factory

Lab : Ingest and load Data into the Data Warehouse

• Perform petabyte-scale ingestion with Azure Synapse Pipelines

• Import data with PolyBase and COPY using T-SQL

• Use data loading best practices in Azure Synapse Analytics

After completing this module, students will be able to:

• Use data loading best practices in Azure Synapse Analytics

• Petabyte-scale ingestion with Azure Data Factory

Module 6: Transform data with Azure Data Factory or Azure Synapse Pipelines

This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks.

Lessons

• Data integration with Azure Data Factory or Azure Synapse Pipelines

• Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Lab : Transform Data with Azure Data Factory or Azure Synapse Pipelines

• Execute code-free transformations at scale with Azure Synapse Pipelines

• Create data pipeline to import poorly formatted CSV files

• Create Mapping Data Flows

After completing this module, students will be able to:

• Perform data integration with Azure Data Factory

• Perform code-free transformation at scale with Azure Data Factory

Module 7: Orchestrate data movement and transformation in Azure Synapse Pipelines

In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines.

Lessons

• Orchestrate data movement and transformation in Azure Data Factory

Lab : Orchestrate data movement and transformation in Azure Synapse Pipelines

• Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines

After completing this module, students will be able to:

• Orchestrate data movement and transformation in Azure Synapse Pipelines

Module 8: End-to-end security with Azure Synapse Analytics

In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools.

Lessons

• Secure a data warehouse in Azure Synapse Analytics

• Configure and manage secrets in Azure Key Vault

• Implement compliance controls for sensitive data

Lab : End-to-end security with Azure Synapse Analytics

• Secure Azure Synapse Analytics supporting infrastructure

• Secure the Azure Synapse Analytics workspace and managed services

• Secure Azure Synapse Analytics workspace data

After completing this module, students will be able to:

• Secure a data warehouse in Azure Synapse Analytics

• Configure and manage secrets in Azure Key Vault

• Implement compliance controls for sensitive data

Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless.

Lessons

• Design hybrid transactional and analytical processing using Azure Synapse Analytics

• Configure Azure Synapse Link with Azure Cosmos DB

• Query Azure Cosmos DB with Apache Spark pools

• Query Azure Cosmos DB with serverless SQL pools

Lab : Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

• Configure Azure Synapse Link with Azure Cosmos DB

• Query Azure Cosmos DB with Apache Spark for Synapse Analytics

• Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics

After completing this module, students will be able to:

• Design hybrid transactional and analytical processing using Azure Synapse Analytics

• Configure Azure Synapse Link with Azure Cosmos DB

• Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics

• Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics

Module 10: Real-time Stream Processing with Stream Analytics

In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput.

Lessons

• Enable reliable messaging for Big Data applications using Azure Event Hubs

• Work with data streams by using Azure Stream Analytics

• Ingest data streams with Azure Stream Analytics

Lab : Real-time Stream Processing with Stream Analytics

• Use Stream Analytics to process real-time data from Event Hubs

• Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics

• Scale the Azure Stream Analytics job to increase throughput through partitioning

• Repartition the stream input to optimize parallelization

After completing this module, students will be able to:

• Enable reliable messaging for Big Data applications using Azure Event Hubs

• Work with data streams by using Azure Stream Analytics

• Ingest data streams with Azure Stream Analytics

Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks

In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams.

Lessons

• Process streaming data with Azure Databricks structured streaming

Lab : Create a Stream Processing Solution with Event Hubs and Azure Databricks

• Explore key features and uses of Structured Streaming

• Stream data from a file and write it out to a distributed file system

• Use sliding windows to aggregate over chunks of data rather than all data

• Apply watermarking to remove stale data

• Connect to Event Hubs read and write streams

After completing this module, students will be able to:

• Process streaming data with Azure Databricks structured streaming

Module 2: Plan and Implement Data Platform Resources

This module introduces methods for deploying data platform resources in Azure. You will learn about options for both upgrading and migrating existing SQL databases to Azure. You will learn how to set up Azure resources to host SQL Server on a Virtual Machine, a Managed Instance, Azure SQL Database and either PostgreSQL or MySQL. You will learn how to determine which options are best based on specific requirements including the High Availability and Disaster Recovery (HADR) needs. They will learn to calculate resource requirements and create templates for their deployments.

Lessons

• Deploying SQL Server using IaaS

• Deploying SQL Server using PaaS

• Deploying Open Source Database Solutions on Azure

Lab : Deploying Azure SQL Database

• Deploy a VM using an ARM template

• Configure resources needed prior to creating a database

• Deploy an Azure SQL Database

• Register the Azure SQL Database instance in Azure Data Studio and validate connectivity

• Deploy PostgreSQL or MySQL using a client tool to validate connectivity

After completing this module, students will be able to:

• Deploy resoureces using manual methods

• Reconmmend an appropriate databvase offering based on requirements

• Configure database resources

• Evaluate and implement a strategy for moving a database to Azure
  
  

Module 3: Implement a Secure Environment

This module explores the practices of securing your SQL Server Database as well as an Azure SQL database. This includes a review of the various SQL Server-based options as well as the various Azure options for securing Azure SQL Database as well as the databases with reside within Azure SQL Database. . Students will lean why security is crucial when working with databases. . Students are also introduced to other database platforms available on Azure in addition to those based on SQL Server, in particular, Azure Database for MariaDB/MySQL/PostgreSQL

Lessons

• Configure Database Authentication

• Configure Database Authorization

• Implement Security for Data at Rest

• Implement Security for Data in Transit

• Implement Compliance Controls for Sensitive Data

Lab : Implement a Secure Environment

• Configure a server-based firewall rule using the Azure Portal

• Authorize Access to Azure SQL Database with Azure Active Directory

• Enable Advanced Data Security and Data Classification

• Manage access to database objects

After completing this module, students will be able to:

• Understand the differences between Windows, SQL Server and Azure Active Directory Authentication

• Describe and configure both data-at-rest encryption solutions as well as data-in-transit encryption

• Implement a data sensitivity solution
  
  

Module 4: Monitor and Optimize Operational Resources

This module will teach you about resource optimization for your databases created using either IaaS or PaaS services. The module also covers monitoring server and hardware resources. It will familiarize you with the various tools available for monitoring performance and establishing a baseline. You will learn how to interpret performance metrics for the most critical resources. You will also learn how to troubleshoot database performance using Azure SQL Database Intelligent Insights.

Lessons

• Baselines and Performance Monitoring

• Major Causes of Performance Issues

• Configuring Resources for Optimal Performance

• User Database Configuration

• Performance-related Maintenance Tasks

Lab : Monitor and Optimize Resources

• Isolate CPU Problems

• Use Query Store observe blocking problems

• Detect and correct fragmentation issues

After completing this module, students will be able to:

• Monitor activity and compare to a baseline

• Define maintenance tasks related to performance

• Identify major causes of performance problems

• Configure resources for optimal performance

• Configure a user database for optimal performance

Module 5: Optimize Query Performance

Query execution plans are potentially the most important aspect of database performance. Improving bad plans is certainly an area where a small amount of effort can bring huge improvements. While hardware issues can limit query performance, improving hardware usually yields performance improvements in the 10-20% range, at most. More commonly database administrators encounter queries that are not optimized, have stale or missing statistics, have missing indexes, or poor database design choices that lead to the database engine doing more work than is necessary to return results for a given query. Improving the plans can sometimes yield performance improvements in the 100-200% range or even more, meaning that after improving a plan with better indexes or statistics, a query could run twice or three times as fast! This module provides details on how to analyze individual query performance and determine where improvements can be made.

Lessons

• Understanding SQL Server Query Plans

• Explore Performance-based Database Design

• Evaluate Performance Improvements

Lab : Query Performance Troubleshooting

• Identify issues with database design AdventureWorks2017

• Isolate problem areas in poorly performing queries in AdventureWorks2017

• Use Query Store to detect and handle regression in AdventureWorks2017

• Use query hints to impact performance in AdventureWorks2017

After completing this module, students will be able to:

• Analyze query plans and identify problem areas

• Evaluate potential query improvements

• Review table and index design

• Determine whether query or design changes have had a positive effect

Module 6: Automation of Tasks

A common goal for database administrators in many environments is to automate as many of their repetitive tasks. This can be as simple as using scripting to automate a backup process, and as complex as building a fully automated alerting system. This module provides details of automating tasks to simplify the DBA’s job. Methods include scheduling tasks for regular maintenance jobs, as well as multi-instance administration and configuration of notifications for task success or failure or non-completion.

Lessons

• Setting up Automatic Deployment

• Defining Scheduled Tasks

• Managing Azure PaaS resources Using Automated Methods

Lab : Automating Tasks

• Deploy an Azure template from a Quickstart template on GitHub

• Configure notifications based on performance metrics

• Deploy an Azure Automation Runbook (or elastic job) to rebuild indexes on an Azure SQL Database

After completing this module, students will be able to:

• Deploy resources using automated deployment scripts

• Create scheduled tasks

• Create notifications and alerts

• Configure automation for PaaS services

Module 7: Plan and Implement a High Availability and Disaster Recovery Environment

Data must be available when the business needs it. That means the solutions hosting the data must be designed with availability and recoverability in mind. Suppose you work for a company that sells widgets both in stores and online. Your main application uses a highly transactional database for orders. What would happen if the server or platform hosting the transactional database had a problem that made it unavailable or inaccessible for some reason? What impact would it have on the business? If the right solution is put in place, the database would come online in a reasonable timeframe with minimal effort, thus allowing business to continue with little-to-no impact. This module and its associated lab cover configuring, testing, and managing a solution for high availability and disaster recovery (HADR) in Azure, for both Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) deployments. This module will not only cover basic requirements, but also the various options available to achieve HADR.

Lessons

• High Availability and Disaster Recovery Strategies

• IaaS Platform and Database Tools for HADR

• PaaS Platform and Database Tools for HADR

• Database Backup and Recovery

Lab : Plan and Implement a High Availability and Disaster Recovery Environment

• Create an Always On Availability Group

• Enable Geo-Replication for Azure SQL Database

• Backup to URL and Restore from URL

After completing this module, students will be able to:

• The difference between recovery time and recovery point objectives

• The available HADR options for both IaaS and PaaS

• The considerations for planning and configuring HADR solutions including how backup and restore fi

• The factors that comprise a HADR strategy

• How to configure a high availability solution via a hands-on lab

Module 2: Explore fundamentals of relational data in Azure

Students will explore fundamental relational data concepts and relational database services in Azure.

Lessons

• Explore relational data offerings in Azure

• Explore Azure services for relational data

Lab : Provision Azure relational database services

After completing this module, students will be able to:

• Identify characteristics of relational data

• Define normalization

• Identify types of SQL statement

• Identify common relational database objects

• Identify options for Azure SQL services

• Identify options for open-source databases in Azure

• Provision a database service on Azure

Module 3: Explore fundamentals of non-relational data in Azure

Students will explore Azure storage for non-relational data and the fundamentals of Azure Cosmos DB.

Lessons

• Fundamentals of Azure Storage

• Fundamentals of Azure Cosmos DB

Lab : Explore Azure Storage

Lab : Explore Azure Cosmos DB

After completing this module, students will be able to:

• Describe features and capabilities of Azure blob storage

• Describe features and capabilities of Azure Data Lake Gen2

• Describe features and capabilities of Azure file storage

• Describe features and capabilities of Azure table storage

• Provision and use an Azure Storage account

• Describe key features and capabilities of Azure Cosmos DB

• Identify the APIs supported in Azure Cosmos DB

• Provision and use an Azure Cosmos DB instance

Module 4: Explore fundamentals of data analytics

Students will learn about modern data warehousing, real-time analytics, and data visualization.

Lessons

• Modern data warehousing

• Streaming and real-time analytics

• Data visualization

Lab : Analyze streaming data

Lab : Visualize data with Power BI

Lab : Explore Azure Synapse Analytics

After completing this module, students will be able to:

• Identify common elements of a modern data warehousing solution

• Describe key features for data ingestion pipelines

• Identify common types of analytical data store and related Azure services

• Provision Azure Synapse Analytics and use it to ingest, process, and query data

• Compare batch and stream processing

• Describe common elements of streaming data solutions

• Describe features and capabilities of Azure Stream Analytics

• Describe features and capabilities of Spark Structured Streaming on Azure

• Describe features and capabilities of Azure Synapse Data Explorer

• Describe a high-level process for creating reporting solutions with Microsoft Power BI

• Describe core principles of analytical data modeling

• Identify common types of data visualization and their uses

• Create an interactive report with Power BI Desktop

Module 2: Assigning Server and Database Roles

Using roles simplifies the management of user permissions. With roles, you can control authenticated users’ access to system resources based on each user’s job function—rather than assigning permissions user-by-user, you can grant permissions to a role, then make users members of roles. Microsoft SQL Server includes support for security roles defined at server level and at database level.

Lessons

• Working with server roles

• Working with Fixed Database Roles

• Assigning User-Defined Database Roles

Lab : Assigning server and database roles

• Assigning Server Roles

• Assigning Fixed Database Roles

• Assigning User-Defined Database Roles

• Verifying Security

After completing this module, you will be able to:

• Describe and use server roles to manage server-level security.

• Describe and use fixed database roles.

• Use custom database roles and application roles to manage database-level security.

Module 3: Authorizing Users to Access Resources

In the previous modules, you have seen how Microsoft SQL Server security is organized and how sets of permissions can be assigned at the server and database level by using fixed server roles, user-defined server roles, fixed database roles, and application roles. The final step in authorizing users to access SQL Server resources is the authorization of users and roles to access server and database objects.

In this module, you will see how these object permissions are managed. In addition to access permissions on database objects, SQL Server provides the ability to determine which users are allowed to execute code, such as stored procedures and functions. In many cases, these permissions and the permissions on the database objects are best configured at the schema level rather than at the level of the individual object. Schema-based permission grants can simplify your security architecture. You will explore the granting of permissions at the schema level in the final lesson of this module.

Lessons

• Authorizing User Access to Objects

• Authorizing Users to Execute Code

• Configuring Permissions at the Schema Level

Lab : Authorizing users to access resources

• Granting, Denying, and Revoking Permissions on Objects

• Granting EXECUTE Permissions on Code

• Granting Permissions at the Schema Level

After completing this module, you will be able to:

• Authorize user access to objects.

• Authorize users to execute code.

• Configure permissions at the schema level.

Module 4: Protecting Data with Encryption and Auditing

When configuring security for your Microsoft SQL Server systems, you should ensure that you meet any of your organization’s compliance requirements for data protection. Organizations often need to adhere to industry-specific compliance policies, which mandate auditing of all data access. To address this requirement, SQL Server provides a range of options for implementing auditing.

Another common compliance requirement is the encryption of data to protect against unauthorized access in the event that access to the database files is compromised. SQL Server supports this requirement by providing transparent data encryption (TDE). To reduce the risk of information leakage by users with administrative access to a database, columns containing sensitive data—such as credit card numbers or national identity numbers—can be encrypted using the Always Encrypted feature.

This module describes the available options for auditing in SQL Server, how to use and manage the SQL Server Audit feature, and how to implement encryption.

Lessons

• Options for auditing data access in SQL Server

• Implementing SQL Server Audit

• Managing SQL Server Audit

• Protecting Data with Encryption

Lab : Using Auditing and Encryption

• Working with SQL Server Audit

• Encrypt a Column as Always Encrypted

• Encrypt a Database using TDE

After completing this module, you will be able to:

• Describe the options for auditing data access.

• Implement SQL Server Audit.

• Manage SQL Server Audit.

• Describe and implement methods of encrypting data in SQL Server.

• Implement encryption

Module 5: Recovery Models and Backup Strategies

One of the most important aspects of a database administrator's role is ensuring that organizational data is reliably backed up so that, if a failure occurs, you can recover the data. Even though the computing industry has known about the need for reliable backup strategies for decades—and discussed this at great length—unfortunate stories regarding data loss are still commonplace. A further problem is that, even when the strategies in place work as they were designed, the outcomes still regularly fail to meet an organization’s operational requirements.

In this module, you will consider how to create a strategy that is aligned with organizational needs, based on the available backup models, and the role of the transaction logs in maintaining database consistency.

Lessons

• Understanding Backup Strategies

• SQL Server Transaction Logs

• Planning Backup Strategies

Lab : Understanding SQL Server recovery models

• Plan a Backup Strategy

• Configure Database Recovery Models

After completing this module, you will be able to:

• Describe various backup strategies.

• Describe how database transaction logs function.

• Plan SQL Server backup strategies.

Module 6: Backing Up SQL Server Databases

In the previous module, you learned how to plan a backup strategy for a SQL Server system. You can now learn how to perform SQL Server backups, including full and differential database backups, transaction log backups, and partial backups.

In this module, you will learn how to apply various backup strategies.

Lessons

• Backing Up Databases and Transaction Logs

• Managing Database Backups

• Advanced Database Options

Lab : Backing Up Databases

• Backing Up Databases

• Performing Database, Differential, and Transaction Log Backups

• Performing a Partial Backup

After completing this module, you will be able to:

• Perform backups of SQL Server databases and transaction logs.

• Manage database backups.

• Describe advanced backup options.

Module 7: Restoring SQL Server 2016 Databases

In the previous module, you learned how to create backups of Microsoft SQL Server 2016 databases. A backup strategy might involve many different types of backup, so it is essential that you can effectively restore them.

You will often be restoring a database in an urgent situation. You must, however, ensure that you have a clear plan of how to proceed and successfully recover the database to the required state. A good plan and understanding of the restore process can help avoid making the situation worse.

Some database restores are related to system failure. In these cases, you will want to return the system as close as possible to the state it was in before the failure. Some failures, though, are related to human error and you might wish to recover the system to a point before that error. The point-in-time recovery features of SQL Server 2016 can help you to achieve this.

Because they are typically much larger, user databases are more likely to be affected by system failures than system databases. However, system databases can be affected by failures, and special care should be taken when recovering them. In particular, you need to understand how to recover each system database because you cannot use the same process for all system databases.

In this module, you will see how to restore user and system databases and how to implement point-in-time recovery.

Lessons

• Understanding the Restore Process

• Restoring Databases

• Advanced Restore Scenarios

• Point-in-Time Recovery

Lab : Restoring SQL Server Databases

• Restoring a Database Backup

• Restring Database, Differential, and Transaction Log Backups

• Performing a Piecemeal Restore

After completing this module, you will be able to:

• Explain the restore process.

• Restore databases.

• Perform advanced restore operations.

• Perform a point-in-time recovery.

Module 8: Automating SQL Server Management

The tools provided by Microsoft SQL Server make administration easy when compared to some other database engines. However, even when tasks are easy to perform, it is common to have to repeat a task many times. Efficient database administrators learn to automate repetitive tasks. This can help to avoid situations where an administrator forgets to execute a task at the required time. Perhaps more importantly, the automation of tasks helps to ensure that they are performed consistently, each time they are executed.

This module describes how to use SQL Server Agent to automate jobs, how to configure security contexts for jobs, and how to implement multiserver jobs.

Lessons

• Automating SQL Server management

• Working with SQL Server Agent

• Managing SQL Server Agent Jobs

• Multi-server Management

Lab : Automating SQL Server Management

• Create a SQL Server Agent Job

• Test a Job

• Schedule a Job

• Configure Master and Target Servers

After completing this module, you will be able to:

• Describe methods for automating SQL Server Management.

• Configure jobs, job step types, and schedules.

• Manage SQL Server Agent jobs.

• Configure master and target servers.

Module 9: Configuring Security for SQL Server Agent

Other modules in this course have demonstrated the need to minimize the permissions that are granted to users, following the principle of “least privilege.” This means that users have only the permissions that they need to perform their tasks. The same logic applies to the granting of permissions to SQL Server Agent. Although it is easy to execute all jobs in the context of the SQL Server Agent service account, and to configure that account as an administrative account, a poor security environment would result from doing this. It is important to understand how to create a minimal privilege security environment for jobs that run in SQL Server Agent.

Lessons

• Understanding SQL Server Agent Security

• Configuring Credentials

• Configuring Proxy Accounts

Lab : Configuring Security for SQL Server Agent

• Analyzing Problems in SQL Server Agent

• Configuring a Credential

• Configuring a Proxy Account

• Configuring and testing the Security Context of a Job

After completing this module, you will be able to:

• Explain SQL Server Agent security.

• Configure credentials.

• Configure proxy accounts.

Module 10: Monitoring SQL Server with Alerts and Notifications

One key aspect of managing Microsoft SQL Server in a proactive manner is to make sure you are aware of problems and events that occur in the server, as they happen. SQL Server logs a wealth of information about issues. You can configure it to advise you automatically when these issues occur, by using alerts and notifications. The most common way that SQL Server database administrators receive details of events of interest is by email message. This module covers the configuration of Database Mail, alerts, and notifications for a SQL Server instance, and the configuration of alerts for Microsoft Azure SQL Database.

Lessons

• Monitoring SQL Server Errors

• Configuring Database Mail

• Operators, Alerts, and Notifications

• Alerts in Azure SQL Database

Lab : Monitoring SQL Server with Alerts and Notifications

• Configuring Database Mail

• Configuring Operators

• Configuring Alerts and Notifications

• Testing Alerts and Notifications

After completing this module, you will be able to:

• Monitor SQL Server errors.

• Configure database mail.

• Configure operators, alerts, and notifications.

• Work with alerts in Azure SQL Database.

Module 11: Introduction to Managing SQL Server by using PowerShell

This module looks at how to use Windows PowerShell with Microsoft SQL Server. Businesses are constantly having to increase the efficiency and reliability of maintaining their IT infrastructure; with PowerShell, you can improve this efficiency and reliability by creating scripts to carry out tasks. PowerShell scripts can be tested and applied multiple times to multiple servers, saving your organization both time and money.

Lessons

• Getting Started with Windows PowerShell

• Configure SQL Server using PowerShell

• Administer and Maintain SQL Server with PowerShell

• Managing Azure SQL Databases using PowerShell

Lab : Using PowerShell to Manage SQL Server

• Getting Started with PowerShell

• Using PowerShell to Change SQL Server settings

After completing this module, you will be able to:

• Describe the benefits of PowerShell and its fundamental concepts.

• Configure SQL Server by using PowerShell.

• Administer and maintain SQL Server by using PowerShell.

• Manage an Azure SQL Database by using PowerShell.

Module 12: Tracing Access to SQL Server with Extended events

Monitoring performance metrics provides a great way to assess the overall performance of a database solution. However, there are occasions when you need to perform more detailed analysis of the activity occurring within a Microsoft SQL Server instance—to troubleshoot problems and identify ways to optimize workload performance.

SQL Server Extended Events is a flexible, lightweight event-handling system built into the Microsoft SQL Server Database Engine. This module focuses on the architectural concepts, troubleshooting strategies and usage scenarios of Extended Events.

Lessons

• Extended Events Core Concepts

• Working with Extended Events

Lab : Extended Events

• Using the System_Health Extended Events Session

• Tracking Page Splits Using Extended Events

After completing this module, you will be able to:

• Describe Extended Events core concepts.

• Create and query Extended Events sessions.

Module 13: Monitoring SQL Server

The Microsoft SQL Server Database Engine can run for long periods without the need for administrative attention. However, if you regularly monitor the activity that occurs on the database server, you can deal with potential issues before they arise. SQL Server provides a number of tools that you can use to monitor current activity and record details of previous activity. You need to become familiar with what each of the tools does and how to use them. It is easy to become overwhelmed by the volume of output that monitoring tools can provide, so you also need to learn techniques for analyzing their output.

Lessons

• Monitoring activity

• Capturing and Managing Performance Data

• Analyzing Collected Performance Data

• SQL Server Utility

Lab : Monitoring SQL Server

After completing this module, you will be able to:

• Monitor current activity.

• Capture and manage performance data.

• Analyze collected performance data.

• Configure SQL Server Utility.

Module 14: Troubleshooting SQL Server

Database administrators working with Microsoft SQL Server need to adopt the important role of troubleshooter when issues arise—particularly if users of business-critical applications that rely on SQL Server databases are being prevented from working. It is important to have a solid methodology for resolving issues in general, and to be familiar with the most common issues that can arise when working with SQL Server systems.

Lessons

• A Trouble Shooting Methodology for SQL Server

• Resolving Service Related Issues

• Resolving Connectivity and Log-in issues

Lab : Troubleshooting Common Issues

• Troubleshoot and Resolve a SQL Login Issue

• Troubleshoot and Resolve a Service Issue

• Troubleshoot and Resolve a Windows Login Issue

• Troubleshoot and Resolve a Job Execution Issue

• Troubleshoot and Resolve a Performance Issue

After completing this module, you will be able to:

• Describe a troubleshooting methodology for SQL Server.

• Resolve service-related issues.

• Resolve login and connectivity issues.

Module 15: Importing and Exporting Data

While a great deal of data residing in a Microsoft SQL Server system is entered directly by users who are running application programs, there is often a need to move data in other locations, to and from SQL Server.

SQL Server provides a set of tools you can use to transfer data in and out. Some of these tools, such as the bcp (Bulk Copy Program) utility and SQL Server Integration Services, are external to the database engine. Other tools, such as the BULK INSERT statement and the OPENROWSET function, are implemented in the database engine. With SQL Server, you can also create data-tier applications that package all the tables, views, and instance objects associated with a user database into a single unit of deployment.

In this module, you will explore these tools and techniques so that you can import and export data to and from SQL Server.

Lessons

• Transferring Data to and from SQL Server

• Importing and Exporting Table Data

• Using bcp and BULK INSERT to Import Data

• Deploying and Upgrading Data-Tier Application

Lab : Importing and Exporting Data

• Import and Excel Data Using the Import Wizard

• Import a Delimited Text File Using bcp

• Import a Delimited Text File using BULK INSERT

• Create and Test an SSIS Package to Extract Data

• Deploy a Data-Tier Application

After completing this module, you will be able to:

• Describe tools and techniques for transferring data.

• Import and export table data.

• Use bcp and BULK INSERT to import data.

• Use data-tier applications to import and export database applications.

Module 2: Designing and Implementing Tables

In a relational database management system (RDBMS), user and system data is stored in tables. Each table consists of a set of rows that describe entities and a set of columns that hold the attributes of an entity. For example, a Customer table might have columns such as CustomerName and CreditLimit, and a row for each customer. In Microsoft SQL Server data management software tables are contained within schemas that are very similar in concept to folders that contain files in the operating system. Designing tables is one of the most important tasks that a database developer undertakes, because incorrect table design leads to the inability to query the data efficiently.

After an appropriate design has been created, it is important to know how to correctly implement the design.

Lessons

• Designing Tables

• Data Types

• Working with Schemas

• Creating and Altering Tables

Lab : Designing and Implementing Tables

• Designing Tables

• Creating Schemas

• Creating Tables

After completing this module, you will be able to:

• Design tables using normalization, primary and foreign keys.

• Work with identity columns.

• Understand built-in and user data types.

• Use schemas in your database designs to organize data, and manage object security.

• Work with computed columns and temporary tables.

Module 3: Advanced Table Designs

The physical design of a database can have a significant impact on the ability of the database to meet the storage and performance requirements set out by the stakeholders. Designing a physical database implementation includes planning the file groups, how to use partitioning to manage large tables, and using compression to improve storage and performance. Temporal tables are a new feature in SQL Server and offer a straightforward solution to collecting changes to your data.

Lessons

• Partitioning Data

• Compressing Data

• Temporal Tables

Lab : Using Advanced Table Designs

• Partitioning Data

• Compressing Data

After completing this module, you will be able to:

• Describe the considerations for using partitioned tables in a SQL Server database.

• Plan for using data compression in a SQL Server database.

• Use temporal tables to store and query changes to your data.

Module 4: Ensuring Data Integrity through Constraints

The quality of data in your database largely determines the usefulness and effectiveness of applications that rely on it—the success or failure of an organization or a business venture could depend on it. Ensuring data integrity is a critical step in maintaining high-quality data.

You should enforce data integrity at all levels of an application from first entry or collection through storage. Microsoft SQL Server data management software provides a range of features to simplify the job.

Lessons

• Enforcing Data Integrity

• Implementing Data Domain Integrity

• Implementing Entity and Referential Integrity

Lab : Using Data Integrity Through Constraints

• Add Constraints

• Test the Constraints

After completing this module, you will be able to:

• Describe the options for enforcing data integrity, and the levels at which they should be applied.

• Implement domain integrity through options such as check, unique, and default constraints.

• Implement referential integrity through primary and foreign key constraints.

Module 5: Introduction to Indexes

An index is a collection of pages associated with a table. Indexes are used to improve the performance of queries or enforce uniqueness. Before learning to implement indexes, it is helpful to understand how they work, how effective different data types are when used within indexes, and how indexes can be constructed from multiple columns. This module discusses table structures that do not have indexes, and the different index types available in Microsoft SQL Server.

Lessons

• Core Indexing Concepts

• Data Types and Indexes

• Heaps, Clustered, and Nonclustered Indexes

• Single Column and Composite Indexes

Lab : Implementing Indexes

• Creating a Heap

• Creating a Clustered Index

• Creating a Covered Index

After completing this module, you will be able to:

• Explain core indexing concepts.

• Evaluate which index to use for different data types.

• Describe the difference between single and composite column indexes.

Module 6: Designing Optimized Index Strategies

Indexes play an important role in enabling SQL Server to retrieve data from a database quickly and efficiently. This module discusses advanced index topics including covering indexes, the INCLUDE clause, query hints, padding and fill factor, statistics, using DMOs, the Database Tuning Advisor, and Query Store.

Lessons

• Index Strategies

• Managing Indexes

• Execution Plans

• The Database Engine Tuning Advisor

• Query Store

Lab : Optimizing Indexes

• Using Query Store

• Heaps and Clustered Indexes

• Creating a Covered Index

After completing this module, you will be able to:

• What a covering index is, and when to use one.

• The issues involved in managing indexes.

• Actual and estimated execution plans.

• How to use Database Tuning Advisor to improve the performance of queries.

• How to use Query Store to improve query performance.

Module 7: Columnstore Indexes

Introduced in Microsoft SQL Server 2012, columnstore indexes are used in large data warehouse solutions by many organizations. This module highlights the benefits of using these indexes on large datasets; the improvements made to columnstore indexes in SQL Server 2016; and the considerations needed to use columnstore indexes effectively in your solutions.

Lessons

• Introduction to Columnstore Indexes

• Creating Columnstore Indexes

• Working with Columnstore Indexes

Lab : Using Columnstore Indexes

• Creating a Columnstore Index

• Create a Memory Optimized Columnstore Table

After completing this module, you will be able to:

• Describe columnstore indexes and identify suitable scenarios for their use.

• Create clustered and nonclustered columnstore indexes.

• Describe considerations for using columnstore indexes.

Module 8: Designing and Implementing Views

This module describes the design and implementation of views. A view is a special type of query—one that is stored and can be used in other queries—just like a table. With a view, only the query definition is stored on disk; not the result set. The only exception to this is indexed views, when the result set is also stored on disk, just like a table.

Views simplify the design of a database by providing a layer of abstraction, and hiding the complexity of table joins. Views are also a way of securing your data by giving users permissions to use a view, without giving them permissions to the underlying objects. This means data can be kept private, and can only be viewed by appropriate users.

Lessons

• Introduction to Views

• Creating and Managing Views

• Performance Considerations for Views

Lab : Designing and Implementing Views

• Creating Standard Views

• Creating an Updateable view

After completing this module, you will be able to:

• Understand the role of views in database design.

• Create and manage views.

• Understand the performance considerations with views.

Module 9: Designing and Implementing Stored Procedures

This module describes the design and implementation of stored procedures.

Lessons

• Introduction to Stored Procedures

• Working with Stored Procedures

• Implementing Parameterized Stored Procedures

• Controlling Execution Context

Lab : Designing and Implementing Stored Procedures

• Create Stored procedures

• Create Parameterized Stored procedures

• Changes Stored Procedure Execution Context

After completing this module, you will be able to:

• Understand what stored procedures are, and what benefits they have.

• Design, create, and alter stored procedures.

• Control the execution context of stored procedures.

• Implement stored procedures that use parameters.

Module 10: Designing and Implementing User-Defined Functions

Functions are routines that you use to encapsulate frequently performed logic. Rather than having to repeat the function logic in many places, code can call the function. This makes code more maintainable, and easier to debug.

In this module, you will learn to design and implement user-defined functions (UDFs) that enforce business rules or data consistency. You will also learn how to modify and maintain existing functions.

Lessons

• Overview of Functions

• Designing and Implementing Scalar Functions

• Designing and Implementing Table-Valued Functions

• Considerations for Implementing Functions

• Alternatives to Functions

Lab : Designing and Implementing User-Defined Functions

• Format Phone numbers

• Modify an Existing Function

After completing this module, you will be able to:

• Describe different types of functions.

• Design and implement scalar functions.

• Design and implement table-valued functions (TVFs).

• Describe considerations for implementing functions.

• Describe alternatives to functions.

Module 11: Responding to Data Manipulation via Triggers

Data Manipulation Language (DML) triggers are powerful tools that you can use to enforce domain, entity, referential data integrity and business logic. The enforcement of integrity helps you to build reliable applications. In this module, you will learn what DML triggers are, how they enforce data integrity, the different types of trigger that are available to you, and how to define them in your database.

Lessons

• Designing DML Triggers

• Implementing DML Triggers

• Advanced Trigger Concepts

Lab : Responding to Data Manipulation by Using Triggers

• Create and Test the Audit Trigger

• Improve the Audit Trigger

After completing this module, you will be able to:

• Design DML triggers

• Implement DML triggers

• Explain advanced DML trigger concepts, such as nesting and recursion.

Module 12: Using In-Memory Tables

Microsoft SQL Server 2014 data management software introduced in-memory online transaction processing (OLTP) functionality features to improve the performance of OLTP workloads. SQL Server adds several enhancements, such as the ability to alter a memory-optimized table without recreating it. Memory-optimized tables are primarily stored in memory, which provides the improved performance by reducing hard disk access.

Natively compiled stored procedures further improve performance over traditional interpreted Transact-SQL.

Lessons

• Memory-Optimized Tables

• Natively Compiled Stored Procedures

Lab : Using In-Memory Database Capabilities

• Using Memory-Optimized Tables

• Using Natively Compiled Stored procedures

After completing this module, you will be able to:

• Use memory-optimized tables to improve performance for latch-bound workloads.

• Use natively compiled stored procedures.

Module 13: Implementing Managed Code in SQL Server

As a SQL Server professional, you are likely to be asked to create databases that meet business needs. Most requirements can be met using Transact-SQL. However, occasionally you may need additional capabilities that can only be met by using common language runtime (CLR) code.

As functionality is added to SQL Server with each new release, the necessity to use managed code decreases. However, there are times when you might need to create aggregates, stored procedures, triggers, user-defined functions, or user-defined types. You can use any .NET Framework language to develop these objects.

In this module, you will learn how to use CLR managed code to create user-defined database objects for SQL Server.

Lessons

• Introduction to CLR Integration in SQL Server

• Implementing and Publishing CLR Assemblies

Lab : Implementing Managed Code in SQL Server

• Assessing Proposed CLR Code

• Creating a Scalar-Valued CLR Function

• Creating a Table Valued CLR Function

After completing this module, you will be able to:

• Explain the importance of CLR integration in SQL Server.

• Implement and publish CLR assemblies using SQL Server Data Tools (SSDT).

Module 14: Storing and Querying XML Data in SQL Server

XML provides rules for encoding documents in a machine-readable form. It has become a widely adopted standard for representing data structures, rather than sending unstructured documents. Servers that are running Microsoft SQL Server data management software often need to use XML to interchange data with other systems; many SQL Server tools provide an XML-based interface. SQL Server offers extensive handling of XML, both for storage and querying. This module introduces XML, shows how to store XML data within SQL Server, and shows how to query the XML data. The ability to query XML data directly avoids the need to extract data into a relational format before executing Structured Query Language (SQL) queries. To effectively process XML, you need to be able to query XML data in several ways: returning existing relational data as XML, and querying data that is already XML.

Lessons

• Introduction to XML and XML Schemas

• Storing XML Data and Schemas in SQL Server

• Implementing the XML Data Type

• Using the Transact-SQL FOR XML Statement

• Getting Started with XQuery

• Shredding XML

Lab : Storing and Querying XML Data in SQL Server

• Determining when to use XML

• Testing XML Data Storage in Variables

• Using XML Schemas

• Using FOR XML Queries

• Creating a Stored Procedure to Return XML

After completing this module, you will be able to:

• Describe XML and XML schemas.

• Store XML data and associated XML schemas in SQL Server.

• Implement XML indexes within SQL Server.

• Use the Transact-SQL FOR XML statement.

• Work with basic XQuery queries.

Module 15: Storing and Querying Spatial Data in SQL Server

This module describes spatial data and how this data can be implemented within SQL Server.

Lessons

• Introduction to Spatial Data

• Working with SQL Server Spatial Data Types

• Using Spatial Data in Applications

Lab : Working with SQL Server Spatial Data

• Become Familiar with the Geometry Data Type

• Add Spatial Data to an Existing Table

• Find Nearby Locations

After completing this module, you will be able to:

• Describe how spatial data can be stored in SQL Server

• Use basic methods of the GEOMETRY and GEOGRAPHY data types

• Query databases containing spatial data

Module 16: Storing and Querying BLOBs and Text Documents in SQL Server

Traditionally, databases have been used to store information in the form of simple values—such as integers, dates, and strings—that contrast with more complex data formats, such as documents, spreadsheets, image files, and video files. As the systems that databases support have become more complex, administrators have found it necessary to integrate this more complex file data with the structured data in database tables. For example, in a product database, it can be helpful to associate a product record with the service manual or instructional videos for that product. SQL Server provides several ways to integrate these files—that are often known as Binary Large Objects (BLOBs)—and enable their content to be indexed and included in search results. In this module, you will learn how to design and optimize a database that includes BLOBs.

Lessons

• Considerations for BLOB Data

• Working with FILESTREAM

• Using Full-Text Search

Lab : Storing and Querying BLOBs and Text Documents in SQL Server

• Enabling and Using FILESTREAM Columns

• Enabling and Using File Tables

• Using a Full-Text Index

After completing this module, you will be able to:

• Describe the considerations for designing databases that incorporate BLOB data.

• Describe the benefits and design considerations for using FILESTREAM to store BLOB data on a Windows file system.

• Describe the benefits of using full-text indexing and Semantic Search, and explain how to use these features to search SQL Server data, including unstructured data

Module 17: SQL Server Concurrency

This module explains how to name, declare, assign values to, and use variables. It also describes how to store data in an array. Concurrency control is a critical feature of multiuser database systems; it allows data to remain consistent when many users are modifying data at the same time. This module covers the implementation of concurrency in Microsoft SQL Server. You will learn about how SQL Server implements concurrency controls, and the different ways you can configure and work with concurrency settings.

Lessons

• Concurrency and Transactions

• Locking Internals

Lab : SQL Server Concurrency

• Implement Snapshot Isolation

• Implement Partition Level Locking

After completing this module, you will be able to:

• Describe concurrency and transactions in SQL Server.

• Describe SQL Server locking.

Module 18: Performance and Monitoring

This module explains how to name, declare, assign values to, and use variables. It also describes how to store data in an array. This module looks at how to measure and monitor the performance of your SQL Server databases. The first two lessons look at SQL Server Extended Events, a flexible, lightweight event-handling system built into the Microsoft SQL Server Database Engine. These lessons focus on the architectural concepts, troubleshooting strategies and usage scenarios.

Lessons

• Extended Events

• Working with extended Events

• Live Query Statistics

• Optimize Database File Configuration

• Metrics

Lab : Monitoring, Tracing, and Baselining

• Collecting and Analyzing Data Using Extended Events

• Implementing Baseline Methodology

After completing this module, you will be able to:

• Understand Extended Events and how to use them.

• Work with Extended Events.

• Understand Live Query Statistics.

• Optimize the file configuration of your databases.

• Use DMVs and Performance Monitor to create baselines and gather performance metrics

Module 2: Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons

• Considerations for data warehouse infrastructure.

• Planning data warehouse hardware.

Lab : Planning Data Warehouse Infrastructure

• Planning data warehouse hardware

After completing this module, you will be able to:

• Describe the main hardware considerations for building a data warehouse

• Explain how to use reference architectures and data warehouse appliances to create a data warehouse

Module 3: Designing and Implementing a Data Warehouse

This module describes how you go about designing and implementing a schema for a data warehouse.

Lessons

• Data warehouse design overview

• Designing dimension tables

• Designing fact tables

• Physical Design for a Data Warehouse

Lab : Implementing a Data Warehouse Schema

• Implementing a star schema

• Implementing a snowflake schema

• Implementing a time dimension table

After completing this module, you will be able to:

• Implement a logical design for a data warehouse

• Implement a physical design for a data warehouse

Module 4: Columnstore Indexes

This module introduces Columnstore Indexes.

Lessons

• Introduction to Columnstore Indexes

• Creating Columnstore Indexes

• Working with Columnstore Indexes

Lab : Using Columnstore Indexes

• Create a Columnstore index on the FactProductInventory table

• Create a Columnstore index on the FactInternetSales table

• Create a memory optimized Columnstore table

After completing this module, you will be able to:

• Create Columnstore indexes

• Work with Columnstore Indexes

Module 5: Implementing an Azure SQL Data Warehouse

This module describes Azure SQL Data Warehouses and how to implement them.

Lessons

• Advantages of Azure SQL Data Warehouse

• Implementing an Azure SQL Data Warehouse

• Developing an Azure SQL Data Warehouse

• Migrating to an Azure SQ Data Warehouse

• Copying data with the Azure data factory

Lab : Implementing an Azure SQL Data Warehouse

• Create an Azure SQL data warehouse database

• Migrate to an Azure SQL Data warehouse database

• Copy data with the Azure data factory

After completing this module, you will be able to:

• Describe the advantages of Azure SQL Data Warehouse

• Implement an Azure SQL Data Warehouse

• Describe the considerations for developing an Azure SQL Data

• WarehousePlan for migrating to Azure SQL Data Warehouse

Module 6: Creating an ETL Solution

At the end of this module you will be able to implement data flow in a SSIS package.

Lessons

• Introduction to ETL with SSIS

• Exploring Source Data

• Implementing Data Flow

Lab : Implementing Data Flow in an SSIS Package

• Exploring source data

• Transferring data by using a data row task

• Using transformation components in a data row

After completing this module, you will be able to:

• Describe ETL with SSIS

• Explore Source Data

• Implement a Data Flow

Module 7: Implementing Control Flow in an SSIS Package

This module describes implementing control flow in an SSIS package.

Lessons

• Introduction to Control Flow

• Creating Dynamic Packages

• Using Containers

• Managing consistency.

Lab : Implementing Control Flow in an SSIS Package

• Using tasks and precedence in a control flow

• Using variables and parameters

• Using containers

Lab : Using Transactions and Checkpoints

• Using transactions

• Using checkpoints

After completing this module, you will be able to:

• Describe control flow

• Create dynamic packages

• Use containers

Module 8: Debugging and Troubleshooting SSIS Packages

This module describes how to debug and troubleshoot SSIS packages.

Lessons

• Debugging an SSIS Package

• Logging SSIS Package Events

• Handling Errors in an SSIS Package

Lab : Debugging and Troubleshooting an SSIS Package

• Debugging an SSIS package

• Logging SSIS package execution

• Implementing an event handler

• Handling errors in data flow

After completing this module, you will be able to:

• Debug an SSIS package

• Log SSIS package events

• Handle errors in an SSIS package

Module 9: Implementing a Data Extraction Solution

This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.

Lessons

• Introduction to Incremental ETL

• Extracting Modified Data

• Loading modified data

• Temporal Tables

Lab : Extracting Modified Data

• Using a datetime column to incrementally extract data

• Using change data capture

• Using the CDC control task

• Using change tracking

Lab : Loading a data warehouse

• Loading data from CDC output tables

• Using a lookup transformation to insert or update dimension data

• Implementing a slowly changing dimension

• Using the merge statement

After completing this module, you will be able to:

• Describe incremental ETL

• Extract modified data

• Load modified data

• Describe temporal tables

Module 10: Enforcing Data Quality

This module describes how to implement data cleansing by using Microsoft Data Quality services.

Lessons

• Introduction to Data Quality

• Using Data Quality Services to Cleanse Data

• Using Data Quality Services to Match Data

Lab : Cleansing Data

• Creating a DQS knowledge base

• Using a DQS project to cleanse data

• Using DQS in an SSIS package

Lab : De-duplicating Data

• Creating a matching policy

• Using a DS project to match data

After completing this module, you will be able to:

• Describe data quality services

• Cleanse data using data quality services

• Match data using data quality services

• De-duplicate data using data quality services

Module 11: Using Master Data Services

This module describes how to implement master data services to enforce data integrity at source.

Lessons

• Introduction to Master Data Services

• Implementing a Master Data Services Model

• Hierarchies and collections

• Creating a Master Data Hub

Lab : Implementing Master Data Services

• Creating a master data services model

• Using the master data services add-in for Excel

• Enforcing business rules

• Loading data into a model

• Consuming master data services data

After completing this module, you will be able to:

• Describe the key concepts of master data services

• Implement a master data service model

• Manage master data

• Create a master data hub

Module 12: Extending SQL Server Integration Services (SSIS)

This module describes how to extend SSIS with custom scripts and components.

Lessons

• Using scripting in SSIS

• Using custom components in SSIS

Lab : Using scripts

• Using a script task

After completing this module, you will be able to:

• Use custom components in SSIS

• Use scripting in SSIS

Module 13: Deploying and Configuring SSIS Packages

This module describes how to deploy and configure SSIS packages.

Lessons

• Overview of SSIS Deployment

• Deploying SSIS Projects

• Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

• Creating an SSIS catalog

• Deploying an SSIS project

• Creating environments for an SSIS solution

• Running an SSIS package in SQL server management studio

• Scheduling SSIS packages with SQL server agent

After completing this module, you will be able to:

• Describe an SSIS deployment

• Deploy an SSIS package

• Plan SSIS package execution

Module 14: Consuming Data in a Data Warehouse

This module describes how to debug and troubleshoot SSIS packages.

Lessons

• Introduction to Business Intelligence

• An Introduction to Data Analysis

• Introduction to reporting

• Analyzing Data with Azure SQL Data Warehouse

Lab : Using a data warehouse

• Exploring a reporting services report

• Exploring a PowerPivot workbook

• Exploring a power view report

After completing this module, you will be able to:

• Describe at a high level business intelligence

• Show an understanding of reporting

• Show an understanding of data analysis

Module 2: SQL Server I/O

This module covers core I/O concepts, Storage Area Networks and performance testing. It focuses on SQL Server I/O operations and how to test storage performance.

Lessons

• Core Concepts

• Storage Solutions

• I/O Setup and Testing

Lab : Testing Storage Performance

After completing this module, you will be able to:

• Describe the core concepts of SQL I/O

• Describe storage solutions

• Setup and test I/O

Module 3: Database Structures

This module covers Database Structures, Data File and TempDB Internals. It focuses on architectural concepts and best practices related to data files for user databases and TempDB.

Lessons

• Database Structure Internals

• Data File Internals

• TempDB Internals

Lab : Database Structures

After completing this module, you will be able to:

• Describe the internal setup of database structures

• Describe the internal setup of data files.

• Describe the internal setup of TempDB

Module 4: SQL Server Memory

This module covers Windows and SQL Server Memory internals. It focuses on architectural concepts and best practices related to SQL Server Memory Configuration.

Lessons

• Windows Memory

• SQL Server Memory

• In-Memory OLTP

Lab : SQL Server Memory

After completing this module, you will be able to:

• Describe the components of Windows memory

• Describe the components of SQL Server memory

• Describe In-Memory OLTP

Module 5: SQL Server Concurrency

This module covers Transactions and Locking Internals. It focuses on architectural concepts and best practices related to Concurrency, Transactions, Isolation Levels and Locking.

Lessons

• Concurrency and Transactions

• Locking Internals

Lab : SQL Server Concurrency

After completing this module, you will be able to:

• Explain concurrency and transactions

• Describe locking

Module 6: Statistics and Index Internals

This module covers Statistics and Index Internals. It focuses on architectural concepts and best practices related to Statistics and Indexes.

Lessons

• Statistics Internals and Cardinality Estimation

• Index Internals

• Columnstore Indexes

Lab : Statistics and index Internals

After completing this module, you will be able to:

• Describe statistics internals

• Explain cardinality estimation

• Describe why you would use Columnstore indexes and be able to implement one

Module 7: Query Execution and Query Plan Analysis

This module covers Query Execution and Query Plan Analysis. It focuses on architectural concepts of the Optimizer and how to identify and fix query plan issues.

Lessons

• Query execution and optimizer internals

• Query execution plans

• Analyzing query execution plans

• Adaptive query processing

Lab : Query execution and query plan analysis

After completing this module, you will be able to:

• Describe query execution and optimizer

• Analyze query plans and resolve common issues

Module 8: Plan Caching and Recompilation

This module covers Plan Caching and Recompilation. It focuses on architectural concepts, troubleshooting scenarios and best practices related to Plan Cache.

Lessons

• Plan cache internals

• Troubleshooting plan cache issues

• Automatic tuning

• Query store

Lab : Plan caching and recompilation

After completing this module, you will be able to:

• Describe plan cache

• Troubleshoot plan cache issues

• Describe query store and why you would use it

Module 9: Extended Events

This module covers Extended Events. It focuses on architectural concepts, troubleshooting strategy and usage scenarios for Extended Events.

Lessons

• Extended events core concepts

• Working with extended events

Lab : Extended events

After completing this module, you will be able to:

• Describe the core concepts of extended events

• Implement extended events

Module 10: Monitoring, Tracing, and Baselining

This module covers tools and techniques to monitor, trace and baseline SQL Server performance data. It focuses on data collection strategy and techniques to analyze collected data.

Lessons

• Monitoring and tracing

• Baselining and benchmarking

Lab : Monitoring, Tracing and Baselining

After completing this module, you will be able to:

• Describe various options for monitoring and tracing

• Describe various options for benchmarking and baselining

Module 2: Data Modelling

This module describes data modelling techniques.

Lessons

• Data modelling

• ANSI/SPARC database model

• Entity relationship modelling

Lab: Entity relationship modelling

After completing this module, you will be able to:

• Understand the common data modelling techniques

• Describe the ANSI/SPARC database model

• Describe entity relationship modelling

Module 3: Normalization

This module describes normalization and denormalization techniques.

Lessons

• Why normalize data?

• Normalization terms

• Levels of normalization

• Denormalization

Lab: Normalizing raw data

After completing this module, you will be able to:

• Describe normalization benefits and notation

• Describe important normalization terms

• Describe the normalization levels

• Describe the role of denormalization

Module 4: Relationships

This module describes relationship types and effects in database design.

Lessons

• Schema mapping

• Referential integrity

Lab: Designing relationships

After completing this module, you will be able to:

• Describe relationship types

• Describe the use, types, and effects of referential integrity

Module 5: Performance

This module introduces the effects of database design on performance.

Lessons

• Indexing

• Query performance

• Concurrency

Lab: Query performance

After completing this module, you will be able to:

• Discuss the performance effects of indexing

• Describe the performance effects of join and search types

• Describe the performance effects of concurrency

Module 6: Database Objects

This module introduces commonly used database objects.

Lessons

• Tables

• Views

• Stored procedures

• Other database objects

Lab: Using SQL Server in a hybrid cloud

After completing this module, you will be able to:

• Describe the use of tables in SQL Server

• Describe the use of views in SQL Server

• Describe the use of stored procedures in SQL Server

• Describe other database objects commonly used in SQL Server

Module 2: Introduction to T-SQL Querying

This module describes the elements of T-SQL and their role in writing queries. Describe the use of sets in SQL Server. Describe the use of predicate logic in SQL Server. Describe the logical order of operations in SELECT statements.

Lessons

• Introducing T-SQL

• Understanding Sets

• Understanding Predicate Logic

• Understanding the Logical Order of Operations in SELECT statements

Lab : Introduction to T-SQL Querying

• Executing Basic SELECT Statements

• Executing Queries that Filter Data using Predicates

• Executing Queries That Sort Data Using ORDER BY

After completing this module, you will be able to:

• Describe the role of T-SQL in writing SELECT statements.

• Describe the elements of the T-SQL language and which elements will be useful in writing queries.

• Describe the concepts of the set theory, one of the mathematical underpinnings of relational databases, and to help you apply it to how you think about querying SQL Server

• Describe predicate logic and examine its application to querying SQL Server.

• Explain the elements of a SELECT statement, delineate the order in which the elements are evaluated, and then apply this understanding to a practical approach to writing queries.

Module 3: Writing SELECT Queries

This module introduces the fundamentals of the SELECT statement, focusing on queries against a single table.

Lessons

• Writing Simple SELECT Statements

• Eliminating Duplicates with DISTINCT

• Using Column and Table Aliases

• Writing Simple CASE Expressions

Lab : Writing Basic SELECT Statements

• Writing Simple SELECT Statements

• Eliminating Duplicates Using DISTINCT

• Using Column and Table Aliases

• Using a Simple CASE Expression

After completing this module, you will be able to:

• Describe the structure and format of the SELECT statement, as well as enhancements that will add functionality and readability to your queries

• Describe how to eliminate duplicates using the DISTINCT clause

• Describe the use of column and table aliases

• Understand and use CASE expressions

Module 4: Querying Multiple Tables

This module describes how to write queries that combine data from multiple sources in Microsoft SQL Server.

Lessons