55232: Writing Analytical Queries for Business Intelligence Training

In this instructor-led training, Microtek Learning teaches students how to write T-SQL queries for database analysis, reporting, and business intelligence. This course presents TSQL within the data analysis context to be specific, creating purpose using data rather than developing transaction-oriented data-tier applications. The course explains measurement levels and quantitative research methodology and submerges all the explained concepts with the presented TSQL topics. The objective here is to give a continuous, straightforward, and meaningful learning path for data retrieval from relational databases to use in analytical tools including SQL SRS, R, Power BI, and Excel.

This training is designed based on the objectives of the course variant 55232A.

• Identifying dependent and independent measurement and variable levels in analytical work scenarios.
• Identifying variables of interest in RDBMS tables.
• Choosing data aggregation level and designing appropriate data set for the intended analysis tool.
• Producing ready-to-use data sets using TSQL SELECT queries for analytic tools like Power BI, SQL SRS, R, SAS, Excel, SPSS, and others.
• Creating stored procedures, functions, and views for modularizing data retrieval code.

The course is aimed at IT pros and data scientists seeking to learn how to use database analysis and reporting tools: SQL SRS, Power BI, R, SAS, Excel and more. They also look to learn using TSQL queries to retrieve data sets effectively through SQL server RDBMS to use with other tools.

Before going to 55232: Writing Analytical Queries for Business Intelligence course, understudies must have:

• Setting learning of information examination and business knowledge situations. For instance, a comprehension of a business related business knowledge venture or need.
• Fundamental information of the Windows working framework and its center usefulness, including document framework route.
• Essential comprehension of the reason for social database administration frameworks, for example, SQL Server.

• 1. Introduction to TSQL for Business Intelligence

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.

• 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
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2. Turning Table Columns into Variables for Analysis: SELECT List Expressions, WHERE, and ORDER BY
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.
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
3. Combining Columns from Multiple Tables into a Single Dataset
The JOIN Operators discusses creating single datasets for analysis by combining results from multiple database tables using JOIN.
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
4. Creating an Appropriate Aggregation Level Using GROUP 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.
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
5. Subqueries, Derived Tables and Common Table Expressions
This module covers the use of subqueries, derived tables, and common table expressions in SELECT queries as techniques for creating intermediate result sets.
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
6. Encapsulating Data Retrieval Logic
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.
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
7. Getting Your Dataset to the Client
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.
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