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MSBI Tutorial - A Definitive Guide to Learn MSBI

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by Anjaneyulu Naini
Last modified: September 11th 2021

Business Intelligence(BI) is all about fetching crude data from a data source, converting it into utilizable data, and consuming it for making reports and explanatory graphics for the data analysis.  Visually representing the tabular data is known as data visualization. It allows us to visualize the essential information through graphs, charts, maps, KPIs, etc. MSBI(Microsoft Business Intelligence) is a famous suite tool in the Business Intelligence domain. It enables retrieving and storing the data for rapid decisions and smart processing. The MSBI suite contains tools like BIDS(Business Intelligence Development Studio), SSAS, SSRS, SSIS, and Data Warehouse. The demand for SSRS, SSAS, and SSIS professionals is increasing gradually,  and many organizations are looking for skilled business intelligence candidates. So, if you want to make a promising career in the Business Intelligence field, join our MSBI training. Ziprecruiter.com shows that an MSBI  developer can earn more than $124k per annum. 

In this MSBI Tutorial, I will be discussing the following topics:

What is MSBI? 

Microsoft Business Intelligence or MSBI in short makes use of Microsoft Excel to perform data analysis. Because of the use of Microsoft Excel as an easy way of data analysis, data collection and eye-catching reports are easy to generate. SQL Server table is used to grab the data using a spreadsheet.
MSBI has three further types:

SSIS

SSIS is a data integration service that combines data from many sources such as Sybase, Oracle, Text, Excel, and MySQL into a single format, after which it refreshes and cleans the data. The OLTP (Online Transaction Processing) module of MS SQL Server is used to complete this integration process.

SSAS

SSAS stands for "analyze service," and it analyses the data that has been saved. The OLAP (Online Analytical Processing) component and data mining capabilities are both used by SSAS. To do data analysis and obtain useful information, it creates multi-dimensional structures known as CUBES (multidimensional data sources which have dimensions and facts) and mining models.

SSRS

SSRS is a reporting service that is now used to display and analyze data in a graphical fashion. SSRS provides reports to analyze data, such as reports, plans, dashboards, scorecards, and Excel.

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Why should you learn MSBI?

  • MSBI has the ability to store and retrieve data to perform smart processing.
  • It helps in taking rapid decisions for the business.
  • The BI suite of Microsoft consists of the best convenient tools to perform business intelligence decisions.
  • Many reputed companies are looking for candidates who are skilled in business intelligence.
  • Various MSBI online training platforms allow the candidate to learn the complete package of SQL Server Services and tools.

Features and Uses of MSBI

Following are the features and uses of MSBI:

  • Well-organized, balanced score and dashboard.
  • Data warehouse applications to perform data analysis.
  • To make effective decisions, it provides a ‘Single Version of Truth.'
  • 'Instinctive Decisions' are eliminated or reduced.
  • Provides quick and timely responses to the business, allowing it to respond more quickly to changing business trends.
  • Reduce the amount of manual and monotonous labour that is prone to errors.
  • Support for advanced analysis that is reliable
  • Historical data is supported.
  • Support for data that has been summarised

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Architecture of MSBI

MSBI is a popular software for smart businesses. It consists of the tools to solve queries of the businesses. It enables users to improve their access to correct and up-to-date information in order to make better business decisions. It also includes a variety of tools for various procedures that are vital in BI solutions. MSBI consists of the following components in its architecture:

Data and Information:

Data is the starting point for any transaction or event analysis. When data is appropriately evaluated utilizing various BI approaches, it becomes information.

Database (DB):

A database is a structured collection of data that can be retrieved using a variety of tools or queries.

Database Management Systems (DBMS):

are particularly built software apps that interact with users, other tools, or the database itself based on business needs. A general-purpose database management system is built in such a way that it can create, modify, and administer databases as needed.

Data Warehouse:

This tool is useful for corporate analysis and reporting. It is a central repository for MSBI and is an output of integrated data from numerous sources. A data warehouse may hold both current and historical data, making corporate reporting and data analysis far more straightforward than you might anticipate. By summarising the facts, it assists top management in making speedy decisions.

ETL:

Extract, transform, and load (ETL) is an acronym for extract, transform, and load. It takes data from a variety of sources in various formats, transforms it into a usable format, and feeds it into a final destination such as a data warehouse or data mart.

Database Engine:

This component of the engine is responsible for driving and creating relational databases.

Datamart:

A data mart is a tiny section of a data warehouse that provides summarized information.

Difference between OLTP and OLAP

OLTP OLAP
Storing the current data (always a production environment) Storing historical and current data from multiple locations
Perform all DML (create, update, read, delete) operations Perform only read operation
High Availability Flexible access to data
Normalized database De-normalized with fewer tables because of less performance with a large volume of data.
Data will update frequently Periodically update the Data

Power BI vs MSBI

Power BI MSBI
Power BI is a suite of tools that enables us to convert raw data into visual reports and share them in the cloud. In MSBI, SSRS integrates programming interfaces and processing components for deploying and testing the reports.
It can access the data from both cloud storage and on-premises. It is on-site software, and it cannot use the data from the cloud storage.
Power BI has the best data modeling and visualization tools for high-level visual representation. It has a drill-down capacity that allows us to focus the classified data in a comprehensive manner.
Power BI can handle the data up to 33,000 rows or 10MB. If the data surpasses the limit, we must execute the queries in the system. MSBI can manage big datasets without emphasizing the data engine.
It enables us to create data models, dashboards, and reports that Power Apps and other web browsers can use. In MSBI, we can use only SSRS for creating visualizations and reports.
PowerBI offers AI features that allow even non-technical professionals to create reports by developing queries in a natural language. MSBI needs previous knowledge of programming and data analysis.

MSBI Tutorial: DTS and SSIS

DTS (Data Transformation Services) SSIS (SQL Server Integration Services)
Limited error handling Complex and powerful error handling
Message boxes in ActiveX scripts Message boxes in .Net scripting
No deployment wizard Interactive deployment wizard
A limited set of transformation A good number of transformations
No business intelligence functionality Complete business intelligence transaction

MSBI Tutorial: Error handling in SSIS

Errors might arise when a data flow component transforms column data, extracts data from sources, or loads data into destinations. Unexpected data values are a common cause of errors.

Following are the types of errors:

  • Connection Manager cannot be initialized with the connection string, resulting in Data Connection Errors. This holds true for both Data Sources and Data Destinations, as well as Control Flows that employ Connection Strings.
  • Data Transformation Errors arise when data is translated from source to destination using a Data Pipeline.
  • Expression Evaluation Errors are errors that occur when expressions are evaluated at run time and behave incorrectly.

MSBI Tutorial: Transformations available in SSIS

AGGREGATE:

This command applies aggregate functions to Record Sets in order to generate new output records from aggregated values.

AUDIT:

Adds metadata to packages and tasks at the package and task level, such as Machine Name, Execution Instance, Package Name, Package ID, and so on.

CHARACTER MAP:

Changes string data at the SQL Server level, such as changing data from lower to upper case.

CONDITIONAL SPLIT:

Splits available input into multiple output pipelines using Boolean Expressions for each output.

COPY COLUMN:

Make a copy of the column in the output so that we can transform it later while maintaining the original for auditing.

CONVERT DATA TYPES:

Converts column data types from one type to another. Explicit Column Conversion is what it stands for.

DATA MINING QUERY:

This command is used to run data mining queries against analysis services and to manage Predictions Graphs and Controls.

DERIVED COLUMN:

From specified expressions, create a new (computed) column.

EXPORT COLUMN:

This command is used to export a single Image column from a database to a flat-file.

FUZZY GROUPING:

A data purification technique that identifies rows that are probable duplicates.

FUZZY LOOKUP:

A fuzzy logic-based pattern matching and ranking algorithm.

IMPORT COLUMN -

This command reads an image-specific column from a database and saves it to a flat-file.

LOOKUP:

Looks up (or searches) a set of reference objects against a data source. It's only used for exact matches.

MERGE:

This command combines two sorted data sets into a single data flow.

MERGE JOIN:

A join junction is used to combine two data sets into a single dataset.

MULTI CAST:

Duplicates the Data Source and sends it to several Destinations.

ROW COUNT:

Saves the result of the data flow/transformation as a variable.

ROW SAMPLING:

Captures sample data by using a row count of the total rows in the data flow.

UNION ALL:

Merge numerous data sets into a single dataset with UNION ALL.

PIVOT:

Used to turn rows into columns in order to normalize data sources and reduce anomalies.

UNPIVOT:

In the case of creating Data Warehouses, UNPIVOT is used to demoralize the data structure by converting columns to rows.

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MSBI Tutorial: Languages used in SSAS

Following are the languages used in SSAS:

  • Structured Query Language (SQL)
  • Multidimensional Expressions (MDX)
  • Data Mining Extensions (DMX)
  • Analysis Services Scripting Language (ASSL)

MSBI Tutorial: Parameters in SSAS

Three types of parameters are used in SSAS:

  • Single value parameters
  • Multi value parameters
  • Cascading parameters

MSBI Tutorial: SSRS Architecture

In SSRS, we have three kinds of users of reporting services:

  • Report Designers: They are the professionals who design the reports from the available data.
  • Report Users: They are those who work with the data. Mostly, they work on SSAS(SQL Server Analysis Services) and SSIS(SQL Server Integration Services). For building reports, they will also work with SSRS (SQLServer Reporting Services).
     
  • Report Manager: Report Managers are responsible for analyzing and approving the reports. They will see the reports to check whether the reports satisfy the requirements or not.

We have many data sources like Oracle, MySQL, etc. We can connect any of these data sources to Microsoft SQL Server. After connecting the data sources to SQL Server, the data rendering and retrieval process will happen. Data rendering is a process of analyzing and filtering the data for satisfying the requirements. Report processing takes place after data rendering. Report Processing is a process of filtering, modifying, and publishing the available reports. To publish the reports on the website, we use the XML web service interface.

MSBI Tutorial: How does SSRS Works?

We can explain SSRS working through the following steps:

  • Step1: First, users will send a “Report Request” to the SSRS server.
  • Step2: SSRS Server discovers requests in metadata form and sends the data request to a data source.
  • Step3: After processing the data, the data source gives data in a merged report form.
  • Step4: After creating the report, it is sent to end-users.

MSBI Tutorial: Explain the terms OLAP, MOLAP, DOLAP, ROLAP, and HOLAP.

OLAP: Online Analytical Processing

This term refers to a set of applications and technologies that enable the gathering, storage, manipulation, and reproduction of multidimensional data for the purpose of analysis.

MOLAP: Multidimensional Online Analytical Processing

This word refers to a specific type of Cartesian data structure. In this way, MOLAP differs from ROLAP. Joins between tables are already suitable in the former, which improves performance. Joins are computed during the request in the latter. Because it's a shared environment, it's aimed at groups of people. The information is kept in a server-based format. It performs greater in-depth data analysis.

DOLAP: Desktop Online Analytical Processing

Small OLAP products for multidimensional analysis on a local scale OLAP on the desktop. A tiny multidimensional database can be created (using Personal Express), or a datacube can be extracted (using Business Objects). It is designed for a single, low-end departmental user. On the desktop, data is kept in cubes. It's almost as though you had your own spreadsheet. End users don't have to worry about performance issues with the server because the data is local.

ROLAP: Relational Online Analytical Processing

One or more star schemas are kept in relational databases under this name. With data stored in relational databases, this technology allows for multidimensional analysis. Because it handles enormous volumes of data and users, it is ideal for large departments or groups.

HOLAP:

Hybridization of OLAP

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Key Takeaways

To implement MSBI, business experts must first be familiar with MSBI. MSBI tutorial can assist businesses in making appropriate business decisions and preparing their business strategy. In essence, business intelligence is a collection of plans for gathering and analyzing data. The data is typically present in large volumes here, and you can also apply new company concepts.

About Author

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NameAnjaneyulu Naini
Author Bio

Anjaneyulu Naini is working as a Content contributor for Mindmajix. He has a great understanding of today’s technology and statistical analysis environment, which includes key aspects such as analysis of variance and software,. He is well aware of various technologies such as Python, Artificial Intelligence, Oracle, Business Intelligence, Altrex etc, Connect with him on LinkedIn and Twitter.