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Data Analysis. OLTP and OLAP Data Warehouse SQL for Data Analysis Data Mining. Data Processing. Data processing types OLTP ( On Line Transaction Processing ) OLAP ( On-line Analytical Processing ) Database types Transactional Numerous users Dynamic ( flickering )

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## Data Analysis

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**Data Analysis**OLTP and OLAP Data Warehouse SQL for Data Analysis Data Mining Bogdan Shishedjiev Data Analysis**Data Processing**• Data processing types • OLTP (On Line Transaction Processing) • OLAP(On-line Analytical Processing ) • Database types • Transactional • Numerous users • Dynamic (flickering) • Always maintaining current state • Critical (very loaded) • Warehouses • Aa few users (analyzers) • Relatively stable • Maintaining the data history (all states in the time) • Not loaded Bogdan Shishedjiev Data Analysis**Architecture**Bogdan Shishedjiev Data Analysis**Architecture**• Source compnents • Filter – separate and assure the coherency of data to be exported • Export – do the transfer of data portions in precise moments of time. • Warehouse components • Loader – Initial loading and preparing the warehouse. • Refresh – loads the portions • достъп • data mining • Export – to other warehouses. This creates an hierarchy of warehouses Bogdan Shishedjiev Data Analysis**Star**Relational Schemes Bogdan Shishedjiev Data Analysis**Snowflake**Relational Schemes Bogdan Shishedjiev Data Analysis**Data Warehouse Design**• Stages • Choose the activity processes to model • Choose the granularity of the activity procesus • Choose the dimensions that can be applied to every record of the fact table. • Choose the facts that must be recorded in the fact table Bogdan Shishedjiev Data Analysis**Data Warehouse Design**• Fact types – the most valuable are numerical continuous values • Additive – they can be added along all dimensions (Money amounts) • Semi-additive – they can be added along some of dimensions (Precipitations, Product quantities) • Non-additive – they cannot be added along any dimension (Wind speed, wind direction) Bogdan Shishedjiev Data Analysis**Data Warehouse Design**• Recommendations • Use continuous additive numerical values • The fact table is highly normalized • Don’t normalize the dimensions. The gain is < 1% • Design thoroughly the dimension attributes. Most often they are textual and discrete. They are used as headings and constraint sources in the answers to users Bogdan Shishedjiev Data Analysis**Dimension time**Promotion Dimension shop Facts - Sales time_key day_of_ week day_no_in_month day_no_overall week_no_ln_year week_no_overall month month_no_overall quarter fiscal_period holiday_flag weekday_flag last_day_in_month_flag season event promotion_key promotion_name price__reduction_ type ad_type dlsplay_ type coupon_type ad_media_name display_provider promo_cost promo_begin_date promo_end-date ..and others stoie_key store_ name store_number store_street_address store_city store_county store_state store_zip store__manager store_phone store_FАX floor_plan_ type photo_processing_type finance_services_type first_opened_date last_remodel_date store_surface grocery_surface frozen-surface ..and others Time_key product_key Store_key promotion key dollar_sales units_sales dollar_cost customer-count Example – Hypermarket Chain Dimension product product_key SKU(stock keeping units )_description SKU_number package_size brand subcategory category departement package_ type diet_type weight weight_unit_of_mesure units_per_retail_case units_per_shipping_case cases_per_pallet shelf_width shelf_height shelf_depth ..and others Bogdan Shishedjiev Data Analysis**Hypermarket Chain**• Fact table • Granularity – each sell of a product (SKU – Stock Keeping Unit) • Values – Total cost (additive), SKU quantity (semi-additive), price (non-additive), customer count (non additive) • Dimensions • Time • Product • Store • Promotion Bogdan Shishedjiev Data Analysis**Hypermarket Chain**• Calculation of disk space needed • Dimension time : 2 years x 365 days = 730 days • Dimension shop : 300 shops, everyday records • Dimension product : 30.000 products in each shop; 3000 are sold every day in each shop. • Dimension promotion : An article can participate in only one promotion in a shop during one day. • Elementary fact records 300 x 730 x 3000 x 1 = 657 .106 records • Key field number 4; Value field number 4 ; Total number osf fields =8 • Fact table size -657 .106 x 8 fields x 4B = 21 GB Bogdan Shishedjiev Data Analysis**Data Operations for Data Analysis**• General form of a SQL statement select D1.C1, ... Dn.Cn, Aggr1(F,Cl),…, Aggrn(F,Cn)from Fact as F, Dimension1 as D1,... DimensionNas Dn where join-condition (F, D1) and... and join-condition (F, Dn) and selection-condition group by D1.C1, ... Dn.Cn order by D1.C1, ... Dn.C Bogdan Shishedjiev Data Analysis**Data Operations for Data Analysis**• Example select Time.Month, Product.Name, sum(Qty) from Sale, Time, Product, Promotion where Sale.TimeCode = Time.TimeCode and Sale.ProductCode = Product.ProductCode and Sale.PromoCode = Promotion.PromoCode and (Product. Name = ' Pasta' or Product.Name = 'Oil') and Time.Month between 'Feb' and 'Apr' and Promotion.Name = 'SuperSaver' group by Time.Month, Product.Name order by Time.Month, Product.Name pivot Time.Month Feb Mar Apr Oil 5K 5K 7K Pasta 45K 50K 51K Bogdan Shishedjiev Data Analysis**Data Cube**The cube is used to represent data along some measure of interest. Although called a "cube", it can be 2-dimensional, 3-dimensional, or higher-dimensional. Each dimension represents some attribute in the database and the cells in the data cube represent the measure of interest. Bogdan Shishedjiev Data Analysis**Data Cube**• Data cube representation Bogdan Shishedjiev Data Analysis**Totals - the value ANY or ALL or NULL**Data Cube Bogdan Shishedjiev Data Analysis**Data Cube**• Drill down – adding a dimension for more detailed results Bogdan Shishedjiev Data Analysis**Data Cube**• Roll-up - removing dimension Bogdan Shishedjiev Data Analysis**Data Cube**• The whole data cube Bogdan Shishedjiev Data Analysis

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