Best Ssas Courses

Find the best online Ssas Courses for you. The courses are sorted based on popularity and user ratings. We do not allow paid placements in any of our rankings. We also have a separate page listing only the Free Ssas Courses.

Data Warehouse Developer-SQL Server/ETL/SSIS/SSAS/SSRS/T-SQL

Develop and Implement a Data Warehouse Solution Step by step

Created by Bluelime Learning Solutions - Learning made simple

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Students: 17582, Price: $39.99

Students: 17582, Price:  Paid

This course describes how to  design and implement a data warehouse solution.
students will learn how to create a data warehouse with Microsoft SQL Server implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

The Primary responsibilities of a data warehouse developer include:
Implementing a data warehouse.
Developing SSIS packages for data extraction, transformation, and loading.
Enforcing data integrity by using Master Data Services.
Cleansing data by using Data Quality Services.

Prerequisites :

Experience of working with relational databases, including:
Designing a normalized database.
Creating tables and relationships.
Querying with Transact-SQL.
Some exposure to basic programming constructs (such as looping and branching).
An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable.

Students will learn how to :

••Deploy and Configure SSIS packages.
••Download and installing SQL Server
••Download and attaching AdventureworksDW database
••Download and installing SSDT
••Download and installing Visual studio
••Describe data warehouse concepts and architecture considerations.
••Select an appropriate hardware platform for a data warehouse.
••Design and implement a data warehouse.
••Implement Data Flow in an SSIS Package.
••Implement Control Flow in an SSIS Package.
••Debug and Troubleshoot SSIS packages.
••Implement an ETL solution that supports incremental data extraction.
••Implement an ETL solution that supports incremental data loading.
••Implement data cleansing by using Microsoft Data Quality Services.
••Implement Master Data Services to enforce data integrity.
••Extend SSIS with custom scripts and components.
••Databases vs. Data warehouses
••Choose between star and snowflake design schemas
••Explore source data
••Implement data flow
••Debug an SSIS package
••Extract and load modified data
••Enforce data quality
••Consume data in a data warehouse

The volume of data available is huge and increasing daily. Structured Query Language -SQL (pronounced as sequel) is the standard language used to communicate and interact with data stored in relational management database systems like Microsoft  SQL Server Oracle, PostgreSQL,MySQL etc.

Different database management systems have their own proprietary  version of the SQL language  but they all conform to using some commands in SQL the same way.   Microsoft SQL Server's version of SQL is known as Transact-SQL  (T-SQL).

You will learn the basics of the SQL language and Transact-SQL since  both use certain commands in the same way.

What You will learn includes:

  • Installing SQL Server

  • Install SSMS

  • Basic Database  Concepts

  • Creating Database

  • Creating Table

  • Creating Views

  • Creating stored procedures

  • Reading data from a database

  • Updating database records

  • Backing up database

  • Deleting Records

  • Truncating Table

  • Dropping Table

  • Dropping Database

  • Restore Database

Learn MSBI , SSIS , SSRS and SSAS Step by Step

This course teaches you MSBI from basic level to advanced level . Covers all 3 concepts SSIS , SSAS and SSRS.

Created by Shivprasad Koirala - We love recording Step by Step tutorials

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Students: 15981, Price: $19.99

Students: 15981, Price:  Paid

If you are thinking about mastering MSBI then you have reached the ultimate tutorial. Yes , i mean by it :-) .This course teaches MSBI using 43 extensive labs as listed below. It goes in depth in to all the three important pillars of MSBI i.e. SSIS , SSAS and SSRS.

Lab 1 :- MSBI Fundamentals, Data flow, Control Flow, ETL, Dataware house. (SSIS)

Lab 2:- Conditional split, Data conversion and Error handling. (SSIS)

Lab 3:- For Loop, Variables, Parameters and Debugging. (SSIS)

Lab 4:- Packaging and Deployment, File component and running SSIS package as a task.(SSIS)

Lab 5: - For dimension, measures, star schema, snow flake, shared connection managers & packages tasks.(SSIS)

Lab 6:- SCD, Type 0, Type 1, OLEDB Command and Unicode conversions.(SSIS)

Lab 7:- Lookup, Data conversion optimization and updating SSIS package.(SSIS)

Lab 8:- Sort, Merge and Merge Joins.(SSIS)

Lab 9 :- Creating SSAS Cube. (SSAS)

Lab 10:- SSAS Time series and Excel display.(SSAS)

Lab11: - What are Transactions and CheckPoints in SSIS? (SSIS)

Lab12: - Simple SSRS report & implementing Matrix, Tabular, Parameters, Sorting, Expressions. (SSRS)

Lab 13:- Using Data Profiling task to check data quality. (SSIS)

Lab 14:- Hierarchical Dimensions. (SSAS)

Lab 15:- WebServices and XML Task. (SSIS)

Lab16:- DrillDown and Subreports. (SSRS)

Lab17 :- SSAS KPI (Key Performance Indicators). (SSAS)

Lab 18:- Pivot, UnPivot and Aggregation. (SSIS)

Lab 19 :- SSAS Calculation.(SSAS)

Lab 20:- SQL Execute Task. (SSIS)

Lab 21:- Reference and Many-to-Many Relationship. (SSAS)

Lab 22 :- Script Task and Send Mail Task. (SSIS)

Lab 23 :- Script component(SSIS)

Lab 24 :- Bar chart, Gauge and Indicators.(SSRS)

Lab 25:- Partitions in SSAS. (SSAS)

Lab 26 :- CDC(Changed Data Capture) in SSIS. (SSIS)

Lab 27:- Additive, Semiadditive and non-additive measures in SSAS.(SSAS)

Lab 28:- Buffer Size Tuning (SSIS)

Lab 29 :- How to implement Multithreading in SSIS?(SSIS)

Lab 30:- Processing SSAS cube in background.(SSAS)

Lab 31 :- Explain Asynchronous, Synchronous, Full, Semi and Non blocking Components. (SSIS)

Lab 32 :- SSRS Architecture and Deployment (SSRS)

Lab 33 :- DQS( Data Quality Services ) (SSIS)

Lab 34 :- Explain Tabular Model and Power Pivot (SSAS).

Lab 35 :- MDX (Multidimensional Expressions) Queries.(SSAS)

Lab 36 :- Data Mining (Fundamentals and Time Series Algorithm).(SSAS)

Lab 37 :- Page Split and Performance issues with SSIS.(SSIS)

Lab 38 :- Aggregations in SSAS.(SSAS)

Lab 39 :- ROLAP, MOLAP and HOLAP.(SSAS)

Lab 40 :- Instrumentation using Data Taps (SSIS).

Lab 41:- Lookup caching modes and Cache Transform. (SSAS)

Lab 42: - Perspectives & Translations. (SSAS)

Lab 43 :- Tabular Training 1 :- Installation, Xvelocity, Vertipaq, DAX,Creating cubes,measures, KPI, Partition and Translation?

Become an SQL Developer: Learn (SSRS, SSIS, SSAS,T-SQL,DW)

Learn SQL Developer Skills from Scratch(SSRS, SSIS, SSAS,T-SQL,Data Warehouse(DW))

Created by Bluelime Learning Solutions - Learning made simple

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Students: 8945, Price: $129.99

Students: 8945, Price:  Paid

SQL developers are expected to have a variety of skills that will enable them perform tasks such as: design, implementation and maintenance of structured query language (SQL) databases. They may work as database or web developers, depending on the specific position. SQL developers often spend many work hours seated in front of computers.

As an SQL developer, you should have strong analytical, communication, and problem-solving skills. A knowledge of SQL servers, SQL Server Analysis Services -SSAS, SQL Server integration services (SSIS) and server reporting services (SSRS) is also important.
This course is Hands-on  and by the end of the course you will have experience  of working with relational database management systems like Microsoft SQL Server .

Some of what you will learn on the  course includes following:
How to implement a data warehouse solution
Debug and Troubleshoot SSIS packages.
Cleansing data by using Data Quality Services.
Extend SSIS with custom scripts and components.
How to create ETL (Extract,Transform, Load) process
How to deploy SSIS Package
How to create SSIS package using SQL Server Integration Services
How to download and install SQL Server Data Tools
How to perform data analysis with SQL Server Analysis Server - SSAS
How to create reports using SQL Server Reporting Services
How to download and install SQL Server
Introduction to T-SQL basics
How to implement Table Joins
Using Aggregate functions

SQL Server SSAS (Multidimensional MDX) – an Introduction

Create cubes from databases, analyse them in Excel, SSRS etc. using SSAS MDX (a Business Intelligence tool)

Created by Phillip Burton - Best Selling Instructor - over 340,000 students so far

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Students: 7801, Price: $79.99

Students: 7801, Price:  Paid

Reviews:

"Good Stuff Overall!!! In my opinion, the instructor did great with the "How-tos" which helped for sure in grasping the whole concept of how to create cubes, set up data source and source views, dimensions, add attributes etc." -- Lakeside David-Debo

"A fantastic course which gets you rolling very quickly and comfortably, thanks for the short and condensed knowledge delivery. Thanks, Phillip you made SSAS very simple for me." - Anup Kale

"This is really the perfect course for beginners! Easy to learn and very inspirational for further investigations in SSAS. Thank you very much, Phillip!" - Marina Barinova

Welcome to this course on SQL Server SSAS and MDX Cubes – an Introduction.

You may have become experienced with creating SQL statements in SQL Server Management Studio. Building databases is ideal when you want to quickly add data – that’s why they are called OLTP – Online Transaction Processing – they are designed for speed for adding transactions.

 

But what if you want to get to get information about? OLTP databases are not based designed for this. What you need instead is a process whereby data is pre-aggregated – in other words, a lot of the calculations you may write have been calculated before you ask for them. It saves a lot of time. It would also be useful if the end user didn’t have to bother with SQL queries, and could use something a bit more hands-on, although retaining something more advanced for advanced users. That’s where cubes come in, full of pre-aggregated data, and SQL Server Analytical Services– or SSAS – (Online Analytical Processing) allows you to make these cubes.

 

This course is designed for the complete beginner in Multidimensional cubes, or someone who wants to refresh their memory. We’ll create a cube to start with from an ordinary database, and then I’ll ask you to create one from a special database known as a Data Warehouse. We’ll export our cube in SQL Server Management Studio, and into SSRS – and we’ll even have a bit of a look at the more advanced way of querying that is MDX.

BI Developer : Learn ( Power BI |SSIS |SSRS |SSAS|DW|T-SQL )

Gain Business Intelligence from Transforming Data

Created by Bluelime Learning Solutions - Learning made simple

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Students: 3556, Price: $99.99

Students: 3556, Price:  Paid

Power BI is a business analytics solution that lets you visualize your data and share insights across your organization, or embed them in your app or website. Connect to hundreds of data sources and bring your data to life with live dashboards and reports.

Discover how to quickly glean insights from your data using Power BI. This formidable set of business analytics tools—which includes the Power BI service, Power BI Desktop, and Power BI Mobile—can help you more effectively create and share impactful visualizations with others in your organization.

In this beginners course you will learn how to  get started with this powerful toolset.  We will  cover topics like  connecting to and transforming web based data sources.  You will learn how to publish and share your reports and visuals on the Power BI service.

You will learn  how to import data, create visualizations, and arrange those visualizations into reports. You will learn how to how to pin visualizations to dashboards for sharing. You  will also learn how to use  DAX  language( Data Analysis Expressions) to perform calculations on data models.

Topics include:

  • Connecting to SQL Server and PostgreSQL Databases

  • Connecting to Microsoft Access Database File

  • Creating reports with data visualizations

  • Modifying existing reports

  • Creating and managing data dashboards

  • Creating Power BI datasets, dashboards, reports, and workbooks

  • Connecting to web based data source

  • Connecting to Excel dataset

  • Using Query Editor

  • Joining tables and creating tables

  • Formulating via DAX logic

  • Using quick measures and dynamic measures

  • Using conditional statements

  • Performing various transformation on connected dataset

  • How to implement a data warehouse solution

  • Debug and Troubleshoot SSIS packages.

  • Cleansing data by using Data Quality Services.

  • Extend SSIS with custom scripts and components.

  • How to create ETL (Extract,Transform, Load) process

  • How to deploy SSIS Package

  • How to create SSIS package using SQL Server Integration Services

  • How to download and install SQL Server Data Tools

  • How to perform data analysis with SQL Server Analysis Server - SSAS

  • How to create reports using SQL Server Reporting Services

  • How to download and install SQL Server

  • Learning some key operations using T-SQL

BI developers are expected to have a variety of skills that will enable them perform tasks such as: design, implementation and maintenance of structured query language (SQL) databases. They may work as database or web developers, depending on the specific position. SQL developers often spend many work hours seated in front of computers.

As a BI developer, you should have strong analytical, communication, and problem-solving skills. A knowledge of SQL servers, SQL Server Analysis Services -SSAS, SQL Server integration services (SSIS) and server reporting services (SSRS) is also important.

SQL Server Analysis Services(SSAS)

Learn SSAS from Scratch

Created by VenkatM M - Technical Architect at MNC

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Students: 589, Price: $19.99

Students: 589, Price:  Paid

SSAS full form is SQL Server Analysis Services.

Multi-dimensional OLAP server as well as an analytics engine that allows you to slice and dice large volumes of data.

SSAS is an analysis service platform, which can be used to create and manage the analysis database.

SSAS Contains Preaggregated data , internally creates analysis database, and once the analysis database is ready, it can be used for many purposes

It has 2 variants Multidimensional and Tabular.

One or more cubes can be presented in the analysis database.

Advantages of SSAS:

· High performance reports

· Multidimensional data analysis

· Slice and data analysis

· Data mining purpose

Using the Cube Data Base or Analysis Data Base:

There are several client tools to use Analysis database.

a. Analyzing the cube database data in the SSDT/BIDS browser.

b. Using the PIVOT table in the excel application to connect and work with cube database.

c. Using Reporting tools (SSRS, Cognos) to generate the reports.

d. By writing the MDX queries in the cube database.

e. Using Panorama Novaview and ProClarity tools to analyze the data.

SSAS Developer Roles:

· Understanding the Data base structure

· Designing the cubes

· Scripting

· Mdx language

SSAS Admin Roles:

· Installation

· Configuration

· Deployment

· Processing

· Security

· Managing[Backup and Restore]

· Monitoring & Trouble shooting

The basic concepts of OLAP include:

  • Cube

  • Dimension table

  • Dimension

  • Level

  • Fact table

  • Measure

  • Schema

Data Source:

Connection string that defines how Analysis Services connects to a physical data store.

Data Source Views

A data source view contains the logical model of the schema used by Analysis Services database objects—namely cubes, dimensions, and mining structures. A data source view is the metadata definition, stored in an XML format.

Named Queries:

A Named Query is a SQL expression represented as a table. It Can be used to divide large and complex dimension table to smaller and simple dimensions, it can also help us to unite columns from multiple tables to single table .It allows us to extend our table schema without modifying underlying base tables.

Cube

The basic unit of storage and analysis in Analysis Services is the cube. A cube is a collection of data that's been aggregated to allow queries to return data quickly.

Cubes are ordered into dimensions and measures. Dimensions come from dimension tables, while measures come from fact tables.

Dimension table

A dimension table contains hierarchical data by which you'd like to summarize. Examples would be an Orders table, which you might group by year, month, week, and day of receipt, or a Books table that you might want to group by genre and title.

Dimension

Each cube has one or more dimensions, each based on one or more dimension tables. A dimension represents a category for analyzing business data: time or category in the examples above. Typically, a dimension has a natural hierarchy so that lower results can be "rolled up" into higher results. For example, in a geographical level you might have city totals aggregated into state totals, or state totals into country totals.

Level

Each type of summary that can be retrieved from a single dimension is called a level. For example, you can speak of a week level or a month level in a time dimension.

Fact table

A fact table contains the basic information that you wish to summarize. This might be order detail information, payroll records, drug effectiveness information, or anything else that's amenable to summing and averaging. Any table that you've used with a Sum or Avg function in a totals query is a good bet to be a fact table.

Measure

Every cube will contain one or more measures, each based on a column in a fact table that you'd like to analyze. In the cube of book order information, for example, the measures would be things such as unit sales and profit.

Schema

Fact tables and dimension tables are related, which is hardly surprising, given that you use the dimension tables to group information from the fact table. The relations within a cube form a schema. There are two basic OLAP schemas: star and snowflake.

Star Schema:

Every dimension table is related directly to the fact table.

Snowflake Schema:

Some dimension tables are related indirectly to the fact table.

For example, if your cube includes OrderDetails as a fact table, with Customers and Orders as dimension tables, and Customers is related to Orders, which in turn is related to OrderDetails, then you're dealing with a snowflake schema.