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6 It s more than a buzz word there are people making lots of money and highly sought a?er by businesses BI allows companies to track employees how long they ve been with the company, when are they going on vacadons, what s the impact of that vacadon, how are sales tracked a?er a major ad campaign, etc Time, money and resources: o?en Dmes wasted in research efforts by companies End results: Minimal results Lots of errors Missed opportunides in decision making Errors usually get worse when muldple employees need access to the data Good BI: No longer will we forget employees that have been loyal to the company for years, ability to beoer manage resources, ability to beoer run markedng campaigns targeted towards the right demographics 6

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10 When creadng a soludon it is important to first understand what the overall soludon consists of, how each component is combined to crate the whole soludon SmartArt diagram shows the steps that are vital for a successful project 10

11 SmartArt diagram shows the steps that are vital for a successful project SITE SOURCE: Pro SQL Server 2012 BI SoluDons by Randal Root and Caryn Mason Apress books Define each step: Step 1 (Interview and IdenDfy Data): Interviewing clients Reviewing exisdng data sources: Spreadsheets, exisdng databases, Simple text files, log files, XML text files, paper documents How much is relevant? Step 2 (Plan the BI SoluDon): These are the blueprints that have been documented for a good BI soludon (something many developers fail to do) Step 3 (Create a Data Warehouse): BI SoluDon data will end up in a warehouse SQL Server makes building a data warehouse easy thanks to Microso? allowing the use of tools such as Access Online TransacDon Processing (OLTP) VS Data Warehouse: OLTP provides data to the warehouse, while Online AnalyDcal Processing (OLAP) helps analyze the data Step 4 (Create an ETL Process): Extract, Transform, and Load. This is the process of moving the 11

12 SmartArt diagram shows the steps that are vital for a successful project SITE SOURCE: Pro SQL Server 2012 BI SoluDons by Randal Root and Caryn Mason Apress books Define each step: Step 1 (Interview and IdenDfy Data): Interviewing clients Reviewing exisdng data sources: Spreadsheets, exisdng databases, Simple text files, log files, XML text files, paper documents How much is relevant? Step 2 (Plan the BI SoluDon): These are the blueprints that have been documented for a good BI soludon (something many developers fail to do) Step 3 (Create a Data Warehouse): BI SoluDon data will end up in a warehouse SQL Server makes building a data warehouse easy thanks to Microso? allowing the use of tools such as Access Online TransacDon Processing (OLTP) VS Data Warehouse: OLTP provides data to the warehouse, while Online AnalyDcal Processing (OLAP) helps analyze the data Step 4 (Create an ETL Process): Extract, Transform, and Load. This is the process of moving the 12

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14 Talk to clients: Answers to Why do we need it? Goal of BI soludon? Who will be using the soludon? Is cost worth it? What s being built? What will be part of the soludon, and what won t? How will the soludon be built? Can it ib released in increments? How will we communicate the soludon to developers? Who will build the soludon? Do we have the right skill sets? When will we need it? How will signoffs occur? Finishing the soludon training, repordng bugs, etc Licensing SQL comes with a collecdon of BI applicadons; making it one of the most affordable soludons 14

15 Usually Excel is used to document all this It allows many worksheets in one workbook 15

16 Data warehouse is a collecdon of muldple data marts Each data mart would consist of 1+ fact tables and suppordng dimension tables IN OTHER WORDS It s data in an form (like text files) that can be easily extracted for informadon Usually it s a reladonal database so is it a repordng database? developers may think so. Data Mart: similar to a data warehouse but more specific it is created for one event like sales, or inventory Typically, it should not be designed for one department several departments should have access to it this will prevent redundancy 16

17 Data will be from outside sources when moving in, data must be clean, consistent, and usable Do we use full or incremental loading? Full loading is a concept known as flush and fill technique it would be the simplest approach Flush/Fill may not be ideal for large tables (millions of rows) filling up the data may be the best approach SSIS VS SQL Developer Studio (SSIS advantages): Cross- server/dbs processes. You can, within a single package, source data in muldple server and type of sources and load it to wide ranges of desdnadons. Using linked servers and SPs does not yield the same funcdoanlity nor performance in most cases SSIS has wide range of predefined tasks and transformadons to cover common data integradon tasks and processes. That would save you Dme It is a lot easier to get a visual of the logic by looking to a SSIS package than a T- SQL script. With SSIS, you could off load your source/target DB server from the ETL workload by scaling out. 17

18 SSAS: SQL Server Analysis Services Cube: A cube is a set of related measures and dimensions that is used to analyze data. A measure is a fact, which is a transacdonal value or measurement that a user may want to aggregate. Measures are sourced from columns in one or more source tables, and are grouped into measure groups. A dimension is a group of aoributes that represent an area of interest related to the measures in the cube, and which are used to analyze the measures in the cube. For example, a Customer dimension might include the aoributes Customer Name, Customer Gender, and Customer City, which would enable measures in the cube to be analyzed by Customer Name, Customer Gender, and Customer City. AOributes are sourced from columns in one or more source tables. The aoributes within each dimension can be organized into hierarchies to provide paths for analysis. A cube is then augmented with calculadons, key performance indicators (KPIs), acdons, parddons, perspecdves, and transladons. A cube is essendally synonymous with a Unified Dimensional Model (UDM). For more informadon about UDMs, seeunified Dimensional Model. In Microso? SQL Server 2005 Analysis Services (SSAS), a cube is developed based on tables and views that are modeled in a data source view. 18

19 Data cubes provide the final piece of the puzzle. A cube aggregates the facts in each level of each dimension in a given OLAP schema. The business intelligence industry uses the word "cube" because it best describes the resuldng data. For example, let's consider our star schema. When you create a cube from that schema, you take the freight, quandty, discount, and other facts and add them up by city, by year, by city and year, and by every other possible combinadon of dimension and hierarchy level. Those calculadons produce the following type of data structure: (Image) Note Data cubes are not "cubes" in the strictly mathemadcal sense because they do not have equal sides. However, virtually all analysts use the term, and it is an industry standard. Here is where things get really excidng. Because the cube contains all of your data in an aggregated form, it seems to know the answers in advance. For example, if a user asks for total sales by year and city, those numbers are already available. If the user asks for total sales by quarter, category, zip code, and employee, those numbers and names are already available. If it helps you to understand them, think of cubes as specialized small databases that know the answers before you even ask the quesdons. That is the big advantage of a cube. You can ask any perdnent quesdon and get an answer, usually at warp speed. For instance, the largest cube in the world is currently 1.4 terabytes and its average response Dme to any query is 1.2 seconds! In addidon, you can view cube data with any valid tool, including spreadsheets, Web pages, the 19

20 SSRS: SQL Server ReporDng Services 20

21 Tuning RAM RAID ConfiguraDon 0, 1, 1+0 RAID 0 (mirroring) is fastest and allows for more growth RAID 1: Striping RAID 0+1 is a combo Processing Adding filters to SSAS data would help Rendering Reports: HTML VS PDF 21

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