MSBI Training in India

1. Basics and architecture of MSBI
2. Data Modeling, Representation, and Transformation
3. Fundamental concepts and architectures of SSIS, SSAS, and SSRS
4. Multidimensional modeling, ETL, and Transformations in SSIS
5. Creating graphical visualizations in SSRS
6. Getting a grasp of OLAP databases and tables in SSAS
7. Relationship types in SSAS

• Software Architects, ETL Developers, and Data Analysts
• Business Intelligence Professionals and people looking to upskill themselves in MSBI

Anybody can take up this online training course and get trained in MSBI.

A Data Scientist earns approximately Rs. 6,20,244 lakhs in India per year.

Data Science is one of the most trending streams in India. Hence, an increasing number of Data Science professionals are being recruited by top MNCs across the country.

Information Technology is one of the fastest growing sectors in India. Hence, a lot of lucrative job opportunities are emerging every day for the individuals with different skills. The Data Scientist is increasingly becoming the most desirable job profile in India. Learn Microsoft Business Intelligence from Intellipaat and get ahead in your career.

• Worldwide Business Intelligence and Analytics market to grow to $22.8 billion in revenue in the next two years – Gartner
• Average US Salary for a Microsoft BI Professional is $107,000 – Indeed

MSBI certification training course in India provides around 60 hours of lab practice sessions and real-life projects to improve the hands-on experience of the learner. This training covers all topics needed to score high in the MCSE: Business Intelligence Certification.

Introduction to Business Intelligence, understanding the concept of Data Modeling, Data Cleaning, learning about Data Analysis, Data Representation, Data Transformation.

Introduction to ETL, the various steps involved Extract, Transform, Load, using a user’s email ID to read a flat file, extracting the User ID from email ID, loading the data into a database table.

Introduction to Connection Managers – logical representation of a connection, the various types of Connection Managers – Flat file, database, understanding how to load faster with OLE DB, comparing the performance of OLE DB and ADO.net, learning about Bulk Insert, working with Excel Connection Managers and identifying the problems.

Learning what is Data Transformation, converting data from one format to another, understanding the concepts of Character Map, Data Column and Copy Column Transformation, import and export column transformation, script and OLEDB Command Transformation, understanding row sampling, aggregate and sort transformation, percentage and row sampling.

Understanding Pivot and UnPivot Transformation, understanding Audit and Row Count Transformation, working with Split and Join Transformation, studying Lookup and Cache Transformation, Integrating with Azure Analysis Services, elastic nature of MSBI to integrate with the Azure cloud service, scale out deployment option for MSBI, working with cloud-borne data sources and query analysis. Scaling out the SSIS package, deploying for tighter windows, working with larger amount of data sources, SQL Server vNext for enhancing SQL Server features, more choice of development languages and data types both on-premise and in the cloud.

Understanding data that slowly changes over time, learning the process of how new data is written over old data, best practices.Detail explanation of three types of SCDs –Type1, Type2 and Type3, and their differences.

Understanding how Fuzzy Lookup Transformation varies from Lookup Transformation, the concept of Fuzzy matching

Learning about error rows configuration, package logging, defining package configuration, understanding constraints and event handlers.

Get introduced to the SSRS Architecture, components of SSRS Report Building tool, learning about the data flow in different components.

Understanding the concepts of Matrix and Tablix, working with Text Box, learning about formatting, row/column grouping, understanding sorting, formatting, concepts of Header, Footer, Totals, Subtotals and Page Breaks.

Learning about Parameters, filter and visibility expression, understanding drill-through and drill-down, defining variables, custom code.

Introduction to various aspects of Bar Chart, Line Chart, Combination Chart, Shape Chart, Sub Reports,Integration of Power Query and M language with SSRS, working with additional data sources in MSBI, rich transformation capabilities addition to MSBI, reusing M functions build for PBIX in SSRS.

Learn how to build a Dashboard with Sparklines, Data Bars, Map Charts, Gauge Charts and drilling into reports, the basics of ad hoc reporting.

Data Bar, Sparkline, Indicator, Gauge Chart, Map Chart, Report Drilling, What is Ad hoc reporting?

Understanding Report Cache, Authorization, Authentication and Report Snapshot, learning about Subscriptions and Site Security.

Understanding the concept of multidimensional analysis, understanding SSAS Architecture and benefits, learn what is Cube, working with Tables and OLAP databases, understanding the concept of Data Sources, working with Dimension Wizard, understanding Dimension Structure, Attribute Relationships, flexible and rigid relationship.

Learning about Process Dimension, the Process database, creation of Cube, understanding Cube Structure, Cube browsing, defining the various categories, Product Key and Customer Key, Column Naming, processing and deploying a Cube, Report creation with a Cube.

Hands-on Exercise – Create a Cube and name various columns Deploy a cube after applying keys and other rules Create reports with a cube

Understanding Data Dimensions and its importance, the various relationships, regular, referenced, many to many, fact, working on Data Partitions, and Data Aggregations.

Learning about SSAS Cube, the various types of Cubes, the scope of Cube and comparison with Data Warehouse.

The various operations on Cube, the limitations of OLAP Cubes, the architecture of in-memory analytics and its advantages.

Deploying cube with existing data warehouse capabilities to get self-service business intelligence, understanding how in-memory analytics works.

Hands-on Exercise – Deploy cube to get self-service business intelligence

Logical model of the schema used by the Cube, components of Cube, understanding Named Queries and Relationships.

An overview of the Dimensions concept, describing the Attributes and Attributes Hierarchies, understanding Key/Value Pairs, Metadata Reload, logical keys and role-based dimensions.

Hands-on Exercise – Create role based dimensions, Use Attributes Hierarchies

Understanding the Measure of Cube, analyzing the Measure, exploring the relationship between Measure and Measure Group, Cube features and Dimension usage.

Working with Cube Measures, deploying analytics, understanding the Key Performance Indicators, deploying actions and drill-through actions on data, working on data partitions, aggregations, translations and perspectives.

Hands-on Exercise – Work with Cube Measures, Deploy analytics, Deploy actions and drill-through actions on data, Make data partitions

Understanding Multidimensional Expressions language, working with MDX queries for data retrieval, working with Clause, Set, Tuple, Filter condition in MDX.

Hands-on Exercise – Apply Clause, Set and filter condition in MDX query to retrieve data

Learning about MDX hierarchies, the functions used in MDX, Ancestor, Ascendant and Descendant function, performing data ordering

Hands-on Exercise – Create MDX hierarchies, Perform data ordering in ascending order, in descending order

Data Analysis Expressions (DAX), Using the EVALUATE and CALCULATE functions, filter DAX queries, create calculated measures, perform data analysis by using DAX

Hands-on Exercise – Use the EVALUATE and CALCULATE functions, filter DAX queries, create calculated measures, perform data analysis by using DAX

Designing and publishing a tabular data model, Designing measures relationships, hierarchies, partitions, perspectives, and calculated columns

Hands-on Exercise – Design and publish a tabular data model, Design measures relationships, hierarchies, partitions, perspectives, and calculated columns

Configuring and maintaining SQL Server Analysis Services (SSAS), Non-Union Memory Architecture (NUMA), Monitoring and optimizing performance, SSAS Tabular model with vNext, Excel portability, importing model from Power BI Desktop, importing a Power Pivot model, bidirectional cross-filtering relationship in MSBI.

Hands-on Exercise – Configure and maintain SQL Server Analysis Services (SSAS), Monitor and optimize performance

Reading data with R Server from SAS, txt, or excel formats, converting data to XDF format; Summarizing data, rxCrossTabs versus rxCube, extracting quantiles by using rxQuantile; Visualizing data (rxSummary and rxCube, rxHistogram and rxLinePlot) Processing data with rxDataStep Performing transforms using functions transformVars and transformEnvir Processing text using RML packages Building predictive models with ScaleR Performing in-database analytics by using SQL Server

Hands-on Exercise – Read data with R Server from SAS, txt or excel formats, convert data to XDF format; Summarize data, Extract quantiles by using rxQuantile; Visualize data (rxSummary, rxCube, rxHistogram and rxLinePlot) Perform transforms using functions transformVars and transformEnvir Build predictive models with ScaleR Perform in-database analytics by using SQL Server

Analyzing Data with SQL Server Reporting Services

In this MSBI project, you will be creating data flow tasks in SSIS, creating SSAS Cubes,  creating an SSRS Report.

Project 1:  SSIS

Problem Statement: Create a data flow task to extract data from the XLS format and store it into the SQL database, store the subcategory and category-wise sales in a table of the database. Once you get the output, split the dataset into two other tables. Table 1 should contain three columns (Sales < 100,000), Category, and Subcategory. Table 2 should contain (Sales > 100,000), Subcategory, and Category columns. Also, the Sales column should be sorted in both tables. Divide the whole dataset into a ratio of 70:30 percent and store the results in two different tables in the database

Topics: Data Flow, ODBC Set up and Connection Manager, Flat File Connection, Transformation, Import Export Transformation, Split and Join Transformation, Merge and Union All Transformation

Highlights:

• Creating a Data Flow Task
• ODBC Set up and Connection Manager
• Transformations

Project 2:  SSRS:

Problem Statement: In the United States, there are many stores in which a survey was conducted based on students.  By using data set (Student Survey), try to extract meaningful Insights by creating an SSRS Report to show Tabular Visualization, Matrix Visualization, Funnel chart, Pie chart, Scatter plot, Drill-down.

Topics: Report Creation, Deployment, Concepts of Reporting Services, Tablix and Matrix, Expression and Parameters, Charts & Reports

Highlights:

• Tabular Visualization
• Matrix Visualization
• Transformations
• Funnel chart
• Pie chart
• Scatter plot
• Drill down

Project 3:  SSAS:

Problem Statement: Using Adventure Works DW 2014 database, Build a Cube to show the number of products there for each color, total Sales Amount for each currency, Count of products there for each product name. Create a reference relationship between the Product category table and subcategory table hence show how many subcategories are there for each category. Build the partitions for sales amount <= 700 and sales amount > 700. Make the aggregations and stop when performance gain reaches 40%. Build a perspective (Subset of the above cube) with limited measures and Dimension fields.

Topics: Dimensions, Data Dimensions & Cubes, Aggregations, Measures & features of Cube, SSAS Perspectives

Highlights:

• Data Dimensions & Cubes
• SSAS Perspectives

Case Study 1: SSIS

Problem Statement: Create the connection of OLDB & load the data in SQL Server from excel; Create transformation where you have to split the people’s age group; How to create constants and events in package; Create a project level and package parameter at the package level; How to extract the data in an Incremental Order;

Topics: Data Flow, ODBC Set up and Connection Manager, Transformation, Split & Join Transformation, Term Extraction and Lookup

Highlights:

• ODBC Set up and Connection Manager
• Transformations

Case Study 2: SSRS

Problem Statement: Steps to add a correlated column chart; how to create a report server project; Use of Data Connections, Performance point Content library; Steps to create drill-down charts; How to pass the parameter from the main chart to detail chart (pie); Functions of Data bars and Sparklines; Usage of KPI box in SSRS Dashboard;

Topics: Concepts of Reporting Services, Report Creation, Expression and Parameters, Report and Authentication, Deployment

Highlights:

• Expression and parameters
• Report and Authentication
• Deployment

Case Study 3: SSAS

Problem Statement: Knowledge check on data mart, measures, dimension, cube, KPI’s, perspectives in SSIS

Topics:  Dimensions, Data Dimensions & Cubes, Measures & features of Cube, SSAS Perspectives

Highlights:

• Data Dimensions & Cubes
• SSAS Perspectives