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Data warehouse and ssas terms
 

Data warehouse and ssas terms

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Overview of Data Warehousing and Analysis Services terms

Overview of Data Warehousing and Analysis Services terms

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  • It is useful because the natural primary key (i.e. Customer Number in Customer table) can change and this makes updates more difficult. Example:On Jan 1 2010, Emp A belongs to Dept1, whatever sales made by this employee added to Dept1 but on June 1 2010 Emp A moved to Dept2. All his new sales contribution should be added to Dept2 from that day onwards and the old one should belong to the Dept2.If let's say in this case we have used business key (Primary key as stated in RDBMS) within data warehouse everything would be allocated to Dept2 even what actually belongs to Dept1If you use surrogate keys you could create on the 1st June a new record for the Employee 'A' in your Employee Dimension with a new surrogate key. This way in your fact table you have your old data (before June) with the SID of the Employee 'E1' + 'Dept1' All new data (after June) would take the SID of the employee 'E1' + 'Dept2' Key Points:

Data warehouse and ssas terms Data warehouse and ssas terms Presentation Transcript

  • About Presenter Karan Gulati is SQL Server Analysis Services Maestro (MCM), working as Support Escalation Engineer in Microsoft for last five years. Currently he is focusing more on SQL BI and SQL PDW. He is very Active blogger and contributed to multiple whitepapers which are published on MSDN or TechNet site. He had also written tools which are available on CodePlex.0 Karan Gulati (SSAS Maestro)
  • Data Warehousing Concepts Overview of Data Warehousing and Analysis Services terms1 Karan Gulati (SSAS Maestro)
  • What are we covering Understanding terms used in SSAS and Data Warehousing world: • What is Data Warehouse • OLAP • Cube • Measures • Dimensions • Schema • Star • Snow-Flake • Surrogate Keys • Slowly Changing Dimensions • SCD1 • SCD2 • SCD32 Karan Gulati (SSAS Maestro)
  • Data Warehousing A data warehouse is a general structure for storing the data needed for good BI (Business Intelligence). Data in a warehouse is of little use until it is converted into the information that decision makers need. The large relational databases, typical of data warehouses, need additional help to convert the data into information.3 Karan Gulati (SSAS Maestro)
  • Why Use OLAP? Provides fast and interactive access to aggregated data and the ability to drill down to detail. Lets users view and interrogate large volumes of data (often millions of rows) by pre-aggregating the information. Puts the data needed to make strategic decisions directly into the hands of the decision makers, through pre-defined queries and reports, because it gives end users the ability to perform their own ad hoc queries, minimizing users dependence on database developers.4 Karan Gulati (SSAS Maestro)
  • OLAP Secret It leverages existing data from a relational schema or data warehouse (data source) by placing key performance indicators (measures) into context (dimensions). Once processed into a multidimensional database (cube), all of the measures are pre-aggregated, which makes data retrieval significantly faster. The processed cube can then be made available to business users who can browse the data using a variety of tools, making ad hoc analysis an interactive and analytical process rather than a development effort. SQL Server 2005s BI Workbench substantially improves upon SQL Server 2000s BI capability.5 Karan Gulati (SSAS Maestro)
  • SQL BI Tools The SQL Server BI Workbench suite consists of five basic tools: SQL Server Relational Database: Used to create relational database Analysis Services: Used to create multidimensional model (measures, dimensions and schema) Data Transformation Services (DTS (Integration Services)): Used to extract, transform and load data from source(s) to the data warehouse or schema Reporting Services: Used to build and manage enterprise reporting using the relational or multidimensional sources Data Mining: Used to extract information based on predetermined algorithms6 Karan Gulati (SSAS Maestro)
  • Architecture7 Karan Gulati (SSAS Maestro)
  • What is Cube? A collection of one or more related measure groups and their associated dimensions8 Karan Gulati (SSAS Maestro)
  • Cube Example Consider the following Imports cube. It contains: Two measures: Packages Last Three related dimensions: Route Source Time9 Karan Gulati (SSAS Maestro)
  • Elements of Cubes Measures Dimensions Schema Star Snowflake10 Karan Gulati (SSAS Maestro)
  • Measures Measures are the key performance indicators that you want to evaluate. To determine which of the numbers in the data might be measures, here is a rule of thumb: If a number makes sense when it is aggregated, then it is a measure.11 Karan Gulati (SSAS Maestro)
  • Dimensions Dimensions are the categories of data analysis. Here is the rule of thumb: When a report is requested "by" something, that something is usually a dimension.12 Karan Gulati (SSAS Maestro)
  • Schema Methodology of arranging your Fact and Master tables: Star Schema Snow-Flake Schema13 Karan Gulati (SSAS Maestro)
  • Star Schema The figure shows a basic star schema; with the dimension tables arranged around a central fact table that contains the measures. A fact table contains a column for each measure as well as a column for each dimension. Each dimension column has a foreign-key relationship to the related dimension table, and the dimension columns taken together are the key to the fact table.14 Karan Gulati (SSAS Maestro)
  • Snowflake Normalizing each of the dimension tables so that there are many joins for each dimension results in a Snowflake Schema. It is called a Snowflake Schema because the “points” of the star get broken up into little branches that look like a snowflake.15 Karan Gulati (SSAS Maestro)
  • Which Schema works for you? Good question: It all depends on your requirement, I will say Star is very simple to understand and manage in comparison to Snow-flake but in real world you cant fit everything in one table so Normalize needs to be done.16 Karan Gulati (SSAS Maestro)
  • Surrogate Keys Also known: Meaningless keys Substitute keys Non-natural keys Artificial keys A surrogate key is a unique value, usually an integer, assigned to each row in the dimension. This surrogate key becomes the primary key of the dimension table and is used to join the dimension to the associated foreign key field in the fact table.17 Karan Gulati (SSAS Maestro)
  • What’s benefit of Surrogate Keys A surrogate key is a unique value, usually an integer, assigned to each row in the dimension. This surrogate key becomes the primary key of the dimension table and is used to join the dimension to the associated foreign key field in the fact table. Surrogate keys helps in maintaining history in case of Slowly Changing Dimensions18 Karan Gulati (SSAS Maestro)
  • Slowly Changing Dimensions There are 3 Versions of SCD SCD 1 The Type 1 methodology overwrites old data with new data, and therefore does not track historical data at all. This is most appropriate when correcting certain types of data errors, such as the spelling of a name. (Assuming you wont ever need to know how it used to be misspelled in the past)19 Karan Gulati (SSAS Maestro)
  • So, what’ Dis-Advantage of SCD1 The obvious disadvantage to this method of managing SCDs is that there is no historical record kept in the data warehouse. You cant tell if your suppliers are tending to move to the Midwest, for example. But an advantage to this is that these are very easy to maintain. Type 220 Karan Gulati (SSAS Maestro)
  • SCD 2 The Type 2 method tracks historical data by creating multiple records in the dimensional tables with separate keys. With Type 2, we have unlimited history preservation as a new record is inserted each time a change is made. In the same example, if the supplier moves to Illinois, the table would look like this: Another popular method for tuple versioning is to add effective date columns.21 Karan Gulati (SSAS Maestro)
  • SCD 3 The Type 3 method tracks changes using separate columns. Whereas Type 2 had unlimited history preservation, Type 3 has limited history preservation, as its limited to the number of columns we designate for storing historical data. Where the original table structure in Type 1 and Type 2 was very similar, Type 3 will add additional columns to the tables: Note: Type 3, keeps separate columns for both the old and new attribute values—sometimes called “alternate realities.” In our experience, Type 3 is less common because it involves changing the physical tables and is not very scalable.22 Karan Gulati (SSAS Maestro)
  • Slowly Changing Dimension You can use SSIS or TSQL for implementing SCD in DW Here is a reference – http://blogs.msdn.com/b/karang/archive/2010/09/29/slowly-changing- dimension-using-ssis.aspx23 Karan Gulati (SSAS Maestro)
  • Thanks Contact Speaker - http://karanspeaks.com http://blogs.msdn.com/karang https://twitter.com/karangspeaks http://in.linkedin.com/in/karanspeaks24 Karan Gulati (SSAS Maestro)