Published on July 21, 2014

Author: quontra123



Introduction to Data Warehousing BY QUONTRA SOLUTIONS phone : (404)-900-9988 email: [email protected] Introduction to Data Warehousing BY QUONTRA SOLUTIONS phone :   (404)-900-9988 email : [email protected] Data Warehouse: Data Warehouse Maintain historic data Analysis to get better understanding of business Better Decision making Definition: A data warehouse is a subject-oriented integrated time-varying non-volatile collection of data that is used primarily in organizational decision making. -- Bill Inmon , Building the Data Warehouse 1996 Subject Oriented: Subject Oriented Data warehouse is organized around subjects such as sales, product, customer. It focuses on modeling and analysis of data for decision makers. Excludes data not useful in decision support process. Integrated: Integrated Data Warehouse is constructed by integrating multiple heterogeneous sources. Data Preprocessing are applied to ensure consistency. RDBMS Legacy System Data Warehouse Flat File Data Processing Data Transformation Data Processing Data Transformation Non-volatile: Non-volatile Mostly, data once recorded will not be updated. Data warehouse requires two operations in data accessing Incremental loading of data Access of data load access Time Variant: Time Variant Provides information from historical perspective e.g. past 5-10 years Every key structure contains either implicitly or explicitly an element of time Why Data Warehouse?: Why Data Warehouse? Problem Statement: ABC Pvt Ltd is a company with branches at USA, UK,CANADA,INDIA The Sales Manager wants quarterly sales report across the branches. Each branch has a separate operational system where sales transactions are recorded. Why Data Warehouse?: Why Data Warehouse? USA UK CANADA INDIA Sales Manager Get q uarterly s ales figure for each branch and manually calculate sales figure across branches. What if he need daily sales report across the branches? Why Data Warehouse?: Why Data Warehouse? Solution: Extract sales information from each database. Store the information in a common repository at a single site. Why Data Warehouse?: Why Data Warehouse? USA UK CANADA INDIA Data Warehouse Sales Manager Query & Analysis tools Characteristics of Data Warehouse : Characteristics of Data Warehouse Relational / Multidimensional database Query and Analysis rather than transaction Historical data from transactions Consolidates Multiple data sources Separates query load from transactions Mostly non volatile Large amount of data in order of TBs When we say large - we mean it!: When we say large - we mean it! Terabytes -- 10^12 bytes: Petabytes -- 10^15 bytes: Exabytes -- 10^18 bytes: Zettabytes -- 10^21 bytes: Zottabytes -- 10^24 bytes: Yahoo! – 300 Terabytes and growing Geographic Information Systems National Medical Records Weather images Intelligence Agency Videos OLTP Vs Warehouse: OLTP Vs Warehouse   OLTP System   OLAP System      Source of data Operational data Consolidation data from OLAP   Purpose of data   control and run routine business tasks planning, problem solving, and  decision support Processing Speed Typicall Very Fast read/update Fast read: indexing, partitioning, snapshots Database Design Highly normalized with many tables Typically de-normalized with fewer tables; use of  star and/or snowflake schemas. Backup and Recovery Backup religiously Reload OLTP? Periodic backups? Age Of Data   Current Historical  Queries simple queries returning relatively few records  complex queries involving aggregations  Data Base Operations   Add , Modify , Delete , Update and Read  Read What the data Reveals A snapshot of ongoing business processes Multi-dimensional views of various kinds of  business activities OLTP Vs Data Warehouse (OLAP): OLTP Vs Data Warehouse (OLAP) OLTP Data Warehouse (OLAP) Indexes Few Many Data Normalized Generally De-normalized Joins Many Some Derived data and aggregates Rare Common Data Warehouse Architecture: Data Warehouse Architecture Flat Files ETL (Extract Transform and Load) Data Warehouse Inventory Data Mart Data Mining Analysis Reporting Generic Data Mart Sales Data Mart Operational System Operational System Flat Files ETL: ETL ETL stands for E xtract, T ransform and L oad Data is distributed across different sources Flat files, Streaming Data, DB Systems, XML, JSON Data can be in different format CSV, Key Value Pairs Different units and representation Country: IN or India Date: 20 Nov 2010 or 20101020 ETL Functions: ETL Functions Extract Collect data from different sources Parse data Remove unwanted data Transform Project Generate Surrogate keys Encode data Join data from different sources Aggregate Load ETL Steps: ETL Steps The first step in ETL process is mapping the data between source systems and target database. The second step is cleansing of source data in staging area. The third step is transforming cleansed source data. Fourth step is loading into the target system. Data before ETL Processing: Data after ETL Processing: ETL Glossary: ETL Glossary Mapping: Defining relationship between source and target objects. Cleansing: The process of resolving inconsistencies in source data. Transformation: The process of manipulating data. Any manipulation beyond copying is a transformation. Examples include aggregating, and integrating data from multiple sources. Staging Area: A place where data is processed before entering the warehouse. Dimension: Dimension Categorizes the data. For example - time , location , etc. A dimension can have one or more attributes . For example - day , week and month are attributes of time dimension. Role of dimensions in data warehousing . Slice and dice Filter by dimensions Types of dimensions: Types of dimensions Conformed Dimension - A dimension that is shared across fact tables . Junk Dimension - A junk dimension is a convenient grouping of flags and indicators. For example, payment method, shipping method. De-generated Dimension - A dimension key, that has no attributes and hence does not have its own dimension table. For example, transaction number, invoice number. Value of these dimension is mostly unique within a fact table. Role Playing Dimensions - Role Playing dimension refers to a dimension that play different roles in fact tables depending on the context. For example, the Date dimension can be used for the ordered date, shipment date, and invoice date. Slowly Changing Dimensions - Dimensions that have data that changes slowly, rather than changing on a time-based, regular schedule. Types of Slowly Changing Dimension : Types of Slowly Changing Dimension Type 1 - The Type 1 methodology overwrites old data with new data, and therefore does not track historical data at all. Type 2 - The Type 2 method tracks historical data by creating multiple records for a given value in dimension table with separate surrogate keys. Type 3 - The Type 3 method tracks changes using separate columns. Whereas Type 2 had unlimited history preservation, Type 3 has limited history preservation, as it's limited to the number of columns we designate for storing historical data. Type 4 - The Type 4 method is usually referred to as using "history tables", where one table keeps the current data, and an additional table is used to keep a record of all changes. Type 1 , 2 and 3 are commonly used. Some books talks about Type 0 and 6 also. http:// Facts: Facts Facts are values that can be examined and analyzed. For Example - Page Views, Unique Users, Pieces Sold, Profit. Fact and measure are synonymous. Types of facts: Additive - Measures that can be added across all dimensions. Non Additive - Measures that cannot be added across all dimensions. Semi Additive - Measures that can be added across few dimensions and not with others. How to store data?: How to store data? Facts and Dimensions: Select the business process to model Declare the grain of the business process Choose the dimensions that apply to each fact table row Identify the numeric facts that will populate each fact table row Dimension Table: Dimension Table Contains attributes of dimensions e.g . Month is an attribute of Time dimension. Can also have foreign keys to another dimension table Usually identified by a unique integer primary key called surrogate key Fact Table: Fact Table Contains Facts Foreign keys to dimension tables Primary Key: usually composite key of all FKs Types of schema used in data warehouse : Types of schema used in data warehouse Star Schema Snowflake Schema Fact Constellation Schema Star Schema: Star Schema Multi-dimensional Data Dimension and Fact Tables A fact table with pointers to Dimension tables Star Schema: Star Schema Snowflake Schema : Snowflake Schema An extension of star schema in which the dimension tables are partly or fully normalized. Dimension table hierarchies broken down into simpler tables . Snowflake Schema: Snowflake Schema Fact Constellation Schema: Fact Constellation Schema A fact constellation schema allows dimension tables to be shared between fact tables. This Schema is used mainly for the aggregate fact tables, OR where we want to split a fact table for better comprehension. For example, a separate fact table for daily, weekly and monthly reporting requirement. Fact Constellation Schema: Fact Constellation Schema In this example, the dimensions tables for time, item, and location are shared between both the sales and shipping fact tables. Operations on Data Warehouse: Operations on Data Warehouse Drill Down Roll up Slice & Dice Pivoting Drill Down: Drill Down Time Region Product Category e.g Home Appliances Sub Category e.g Kitchen Appliances Product e.g Toaster Roll Up: Roll Up Year Quarter Month Fiscal Year Fiscal Quarter Fiscal Month Fiscal Week Day Slice & Dice: Slice & Dice Time Region Product Product = Toaster Time Region Pivoting: Pivoting Also called rotation Rotate on an axis Interchange Rows and Columns Time Region Product Region Time Product Advantages of Data Warehouse: Advantages of Data Warehouse One consistent data store for reporting, forecasting, and analysis Easier and timely access to data Scalability Trend analysis and detection Drill down analysis Disadvantages of Data Warehouse: Disadvantages of Data Warehouse Preparation may be time consuming. High associated cost Case Study: Why Data Warehouse: Case Study: Why Data Warehouse G2G Courier Pvt. Ltd. is an established brand in courier industry which has its own network in main cities and also have sub contracted in rural areas across the country to various partners. The President of the company wants to look deep into the financial health of the company and different performance aspects. Challenges: Challenges Apart from G2G’s own transaction system, each partner has their own system which make the data very heterogeneous. Granularity of data in various systems is also different. For eg : minute accuracy and day accuracy. To do analysis on metrics like Revenue and Timely delivery across various geographical locations and partner, we need to have a unified system. Data warehouse model: Data warehouse model Sales Fact Region Product Product Category Time Thank You : Thank You

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