55232: Writing Analytical Queries for Business Intelligence Training

This module discusses writing analytical queries vs. transactional DML queries, and describes the typical architecture of a business intelligence environment. It discusses the role of SELECT queries in retrieving data for analysis from relational databases. It introduces the sample database to be used in the course, and begins a presentation of the SELECT query.

Lessons

Two Approaches to SQL Programming

TSQL Data Retrieval in an Analytics / Business Intelligence Environment

The Database Engine

SQL Server Management Studio and the CarDeal Sample Database

Identifying Variables in Tables

SQL is a Declarative Language

Introduction to the SELECT Query

Lab: Introduction to TSQL for Business Intelligence

Create a database diagram

Create and execute basic SELECT queries

After completing this module, students will be able to:

Describe the purpose of analytical queries

Describe the function of TSQL data retrieval in an analytics / business intelligence environment

Describe the primary functions of the database engine

Discuss TSQL as a declarative language

Identify variables of interest in database tables

Write basic SELECT queries

This module covers the identification of and relationship between levels of measurement and column data types. It continues a discussion of the SELECT query and adds the WHERE and ORDER BY clauses.

Lessons

Turning Columns into Variables for Analysis

Column Expressions, Data Types, and Built-in Functions

Column aliases

Data type conversions

Built-in Scalar Functions

Table Aliases

The WHERE clause

ORDER BY

Lab: Write queries using:

Column and table aliases

DISTINCT

WHERE

ORDER BY

Built-in functions

Explicit and implicit data type conversion

After completing this module, students will be able to:

Implement column expressions in SELECT queries

Implement column and table aliases

Describe data types and Implement data type conversions

Implement built-in functions

Use WHERE and ORDER BY clauses in SELECT queries

Module 3 discusses creating single datasets for analysis by combining results from multiple database tables using JOIN.

Lessons

Primary Keys, Foreign Keys, and Joins

Understanding Joins, Part 1: CROSS JOIN and the Full Cartesian Product

Understanding Joins, Part 2: The INNER JOIN

Understanding Joins, Part 3: The OUTER JOINS

Understanding Joins, Part 4: Joining more than two tables

Understanding Joins, Part 5: Combining INNER and OUTER JOINs

Combining JOIN Operations with WHERE and ORDER BY

Lab: Write SELECT queries using:

Inner join

Left, right, and full join

Joins of more than two tables

Join operators, in addition to WHERE and ORDER BY

After completing this module, students will be able to:

Discuss and describe the conceptual flow of JOIN operations

Implement INNER and OUTER JOIN operations on two or more tables

Describe the order of operations of SELECT clauses

Combine JOIN operations with WHERE and ORDER BY

This module covers the aggregation of quantitative column values across grouping factors for the purpose of groupwise comparisons and/or changing the granularity of a dataset.

Lessons

Identifying required aggregation level and granularity

Aggregate Functions

GROUP BY

HAVING

Order of operations in SELECT queries

Lab: Write queries using:

Aggregate functions

Aggregate function with HAVING

Aggregate function with GROUP BY and HAVING

Aggregate function with GROUP BY, HAVING, WHERE, and ORDER BY

After completing this module, students will be able to:

Describe row granularity of result sets

Discuss and implement aggregate functions to achieve required row granularity

Use GROUP BY to calculate aggregate values for groups

Use HAVING to filter records in the result set by aggregate value

Combine GROUP BY and HAVING with WHERE and ORDER BY

This module covers the use of subqueries, derived tables, and common table expressions in SELECT queries as techniques for creating intermediate result sets.

Lessons

Non-correlated and correlated subqueries

Derived tables

Common table expressions

Lab: Write queries using:

Non-correlated subqueries

Correlated subqueries

Derived tables

Common table expressions

Subqueries, derived tables, and common table expressions in combination with other topics covered in previous modules

After completing this module, students will be able to:

Describe and discuss the rationale of creating intermediate results sets within SELECT queries

Implement non-correlated and correlated subqueries

Implement derived tables

Implement Common Table Expressions

Create intermediate to advanced TSQL queries to retrieve result sets for analysis

This module discusses the encapsulation of data retrieval logic in views, table-valued functions, and stored procedures. It also describes scenarios in which these techniques are useful for producing datasets for analysis. Finally, it describes the database security issues involved, and techniques for creating and using these database objects while maintaining current permission sets on source data.

Lessons

Views

Table-valued functions

Stored procedures

Creating objects for read-access users

Creating database accounts for analytical client tools

Lab: Encapsulating Data Retrieval Logic

Create a SQL login

Create a database user and assign required permissions

Create a database schema for views, functions, and stored procs

Create a view

Create a table-values function

Create a stored procedure

Allow a user with read-only access to use views, table-valued functions, and store procedures

After completing this module, students will be able to:

Identify scenarios in which views, table-valued functions, and stored procedures simply data retrieval

Compare and contrast views, table-valued functions, and stored procedures

Create views, table-valued functions, and stored procedures

Describe the security requirement for creating database objects

Implement views, table-valued functions, and stored procedures for users with read-only access to source data

This module covers common techniques for making datasets produced by SELECT queries available to analytical client tools such as SQL Server Reporting Services, PowerBI, Excel, and R. It discusses running queries directly from the client tool, in addition to exporting datasets to text files which can then be accessed by the client tool.

Lessons

Connecting to SQL Server and Submitting Queries from Client Tools

Connecting and running SELECT queries from:

Excel

PowerBI

RStudio

Exporting datasets to files using

Results pane from SSMS

The bcp utility

The Import/Export Wizard

Lab: Getting Your Dataset to the Client

Retrieving the results of a view in Excel

Running an ad-hoc SELECT query from Excel

Running an ad-hoc query from PowerBI

Running an ad-hoc query from RStudio

Using the Import/Export wizard to write the results of a query to a text file

After completing this module, students will be able to:

Describe the properties of database connection strings

Run queries from, and return results to, Excel, PowerBI, and RStudio

Export query results to external text files using the SSMS results pane, the bcp utility, and the Import/Export Wizard