{"id":10279,"date":"2016-07-26T14:48:09","date_gmt":"2016-07-26T18:48:09","guid":{"rendered":"http:\/\/www.iri.com\/blog\/?p=10279"},"modified":"2024-12-17T15:46:23","modified_gmt":"2024-12-17T20:46:23","slug":"the-enterprise-data-warehouse-then-and-now","status":"publish","type":"post","link":"https:\/\/www.iri.com\/blog\/data-transformation2\/the-enterprise-data-warehouse-then-and-now\/","title":{"rendered":"The Enterprise Data Warehouse, Then and Now"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">For the last 30 or so years, the precursor to most large scale business intelligence (BI) environments has been the Enterprise Data Warehouse (EDW). A data warehouse (DW) is usually a central database (DB) for reporting, planning, and analyzing summarized, subject-matter data integrated from disparate, historical transaction sources. An EDW uses technology to move internal and external data sources into a cross-functional DW.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The typical EDW environment includes:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">disparate storage and systems that provide the source data<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">data integration and staging through extract-transform-load (<\/span><a href=\"http:\/\/www.iri.com\/solutions\/data-integration\/etl\"><span style=\"font-weight: 400;\">ETL<\/span><\/a><span style=\"font-weight: 400;\">) processes<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">data quality and governance processes to ensure the DW fulfills its purposes<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">tools and applications to profile sources, feed the DW DB, and analyze the results<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">The basic architecture of the EDW has remained more or less as follows:<\/span><\/p>\n<p><a href=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/1-Basic-architecture-of-the-EDW-.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-10281\" src=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/1-Basic-architecture-of-the-EDW-.png\" alt=\"Basic architecture of the EDW\" width=\"488\" height=\"317\" srcset=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/1-Basic-architecture-of-the-EDW-.png 488w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/1-Basic-architecture-of-the-EDW--300x195.png 300w\" sizes=\"(max-width: 488px) 100vw, 488px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><i><span style=\"font-weight: 400;\">Source: Oracle Corp.<\/span><\/i><\/p>\n<p><span style=\"font-weight: 400;\">Traditional data sources are relational DB tables, flat files, and web services; but now CRM, ERP, IoT, NoSQL, social media, web log, public, and other \u201cbig data\u201d sources are in the mix.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Unlike source OLTP DBs with normalized tables optimized for complex queries and modeled in E-R diagrams, the DW DB is denormalized for simple joins, and thus faster OLAP queries. Its data models reflect more advanced \u201cstar\u201d or \u201csnowflake\u201d <\/span><a href=\"http:\/\/www.iri.com\/blog\/data-transformation2\/schema-migration-relational-to-star\/\"><span style=\"font-weight: 400;\">schema<\/span><\/a><span style=\"font-weight: 400;\">. They are also considered \u201cnonvolatile\u201d and \u201ctime-variant\u201d because they produce the same reports for different periods in time. Modern EDWs and logical data warehouses (LDW) are more volatile.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Data marts are smaller, departmental-level DWs that either use subsets created from the main DW (dependent), or they are designed for one business unit (independent). The operational data store (ODS), which we\u2019ll cover separately, is an interim DW DB, usually for customer files.<\/span><\/p>\n<h3><b>(Slightly) Deeper Dive<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">Though there are variations of \u201cback room\u201d DW\/BI architectures to stage and integrate data (including extract-load-transform (ELT) and hybrids of either), IRI subscribes to the Ralph Kimball <\/span><a href=\"http:\/\/www.iri.com\/solutions\/data-integration\/etl\"><span style=\"font-weight: 400;\">ETL<\/span><\/a><span style=\"font-weight: 400;\"> convention, with BI data stores in the \u201cfront room\u201d and presentation services in between:<\/span><\/p>\n<p><a href=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/2-Back-Room-Front-Room.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-10282\" src=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/2-Back-Room-Front-Room.png\" alt=\"Back Room-Front Room\" width=\"580\" height=\"310\" srcset=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/2-Back-Room-Front-Room.png 580w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/07\/2-Back-Room-Front-Room-300x160.png 300w\" sizes=\"(max-width: 580px) 100vw, 580px\" \/><\/a><\/p>\n<p style=\"text-align: center;\"><span style=\"font-weight: 400;\">Source: <\/span><a href=\"http:\/\/www.kimballgroup.com\/data-warehouse-business-intelligence-resources\/books\/data-warehouse-dw-lifecycle-toolkit\/\"><i><span style=\"font-weight: 400;\">The Data Warehouse Lifecycle Toolkit, Second Edition<\/span><\/i><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Beyond basic <\/span><a href=\"http:\/\/www.iri.com\/blog\/data-transformation2\/etl-vs-elt-we-posit-you-judge\/\"><span style=\"font-weight: 400;\">ETL vs. ELT<\/span><\/a><span style=\"font-weight: 400;\"> decisions is a long list of other considerations, including the hardware and software systems running in the bottom (DW\/ETL), middle (OLAP), and top (BI) job tiers of the EDW. As a threshold matter, EDWs mostly use SQL-driven relational DBs; though with data pushing into petabyte ranges, mainframes, multi-core Unix servers, and Hadoop data nodes are now the norm, along with SQL layers on NoSQL DBs like <a href=\"https:\/\/www.iri.com\/solutions\/database-acceleration\/mongodb\">MongoDB<\/a> and <\/span><a href=\"http:\/\/www.iri.com\/blog\/migration\/data-migration\/using-marklogic-data-in-iri-voracity\/\"><span style=\"font-weight: 400;\">MarkLogic<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the bottom tier, <\/span><a href=\"http:\/\/www.iri.com\/products\/voracity\"><span style=\"font-weight: 400;\">IRI Voracity<\/span><\/a><span style=\"font-weight: 400;\"> and other vendor offerings handle ETL and related data delivery issues, including change data capture, migration and replication, and various \u2018types\u2019 of slowly changing dimension updates. In addition to built-in data discovery, masking, test data and BI capabilities, Voracity\u2019s <\/span><a href=\"http:\/\/www.iri.com\/products\/voracity\/why-is-voracity-better\"><span style=\"font-weight: 400;\">advantages<\/span><\/a><span style=\"font-weight: 400;\"> lie in uniquely combining the strengths of its competitors:<\/span><\/p>\n<p><a href=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/05\/voracity-prism.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-12076\" src=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/05\/voracity-prism-1024x478.png\" alt=\"Voracity Decision Prism\" width=\"600\" height=\"280\" srcset=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/05\/voracity-prism-1024x478.png 1024w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/05\/voracity-prism-300x140.png 300w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2016\/05\/voracity-prism-768x359.png 768w\" sizes=\"(max-width: 600px) 100vw, 600px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">In the middle tier, the choice of a <\/span><a href=\"http:\/\/searchoracle.techtarget.com\/definition\/relational-online-analytical-processing\"><span style=\"font-weight: 400;\">MOLAP<\/span><\/a><span style=\"font-weight: 400;\"> or <\/span><a href=\"http:\/\/searchoracle.techtarget.com\/definition\/relational-online-analytical-processing\"><span style=\"font-weight: 400;\">ROLAP<\/span><\/a><span style=\"font-weight: 400;\"> is usually made, where the DB is either multi-dimensional and stores \u201cfacet\u201d views (like sales by time) in arrays, or relational, where similar results require SQL queries. MDDBs are thus faster at analytic processing, but RDBs are more common in EDWs, where BI tools feed off them in the top tier instead. Choices of both <\/span><a href=\"http:\/\/www.tutorialspoint.com\/dwh\/dwh_partitioning_strategy.htm\"><span style=\"font-weight: 400;\">partitioning strategy<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"https:\/\/en.wikipedia.org\/wiki\/Database_normalization\"><span style=\"font-weight: 400;\">normalization form<\/span><\/a><span style=\"font-weight: 400;\"> are thus often made to speed the RDB\u2019s queries.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the top tier, the choices for BI and data mining are virtually endless, and dictated by reporting or <\/span><a href=\"http:\/\/www.iri.com\/blog\/business-intelligence\/big-data-analytics-in-use\/\"><span style=\"font-weight: 400;\">analytic<\/span><\/a><span style=\"font-weight: 400;\"> (diagnostic, predictive, prescriptive, etc.) requirements. Here, an external data preparation solution &#8212; such as CoSort-powered data blending or munging jobs in Voracity &#8212; removes integration from the BI layer, and speeds <\/span><a href=\"http:\/\/www.iri.com\/solutions\/business-intelligence\/bi-tool-acceleration\"><span style=\"font-weight: 400;\">popular<\/span><\/a><span style=\"font-weight: 400;\"> visualization platforms up to 20X.<\/span><\/p>\n<h3><b>EDW Uses and Benefits<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">In his TechTarget <\/span><a href=\"http:\/\/searchdatamanagement.techtarget.com\/feature\/The-benefits-of-deploying-a-data-warehouse-platform\"><span style=\"font-weight: 400;\">article<\/span><\/a><span style=\"font-weight: 400;\">, DW consultant Craig Mullins delineated that an EDW can:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">track, manage, and improve corporate performance<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">monitor and modify a marketing campaign<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">review and optimize logistics and operations<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">increase the efficiency and effectiveness of product management and development<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">query, join, and access disparate information culled from multiple sources<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">manage and enhance customer relationships<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">forecast future growth, needs, and deliverable<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">cleanse and improve the quality of your organization&#8217;s data.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Specific industry <\/span><a href=\"http:\/\/www.iri.com\/customers\/industries\"><span style=\"font-weight: 400;\">cases<\/span><\/a><span style=\"font-weight: 400;\"> from IRI CoSort or Voracity <\/span><a href=\"http:\/\/www.iri.com\/solutions\/data-integration\"><span style=\"font-weight: 400;\">ETL<\/span><\/a><span style=\"font-weight: 400;\"> sites show DW and EDWs are used to:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Assess call, click, and other consumption habits<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Detect insurance fraud or set rates<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Evaluate treatment outcomes and recommend drug therapies<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Manage goods inventories and shipments<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Monitor device\/equipment health and service levels<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Optimize pricing and promotion decisions<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Predict crime and prevent terrorism<\/span><\/li>\n<li style=\"font-weight: 400;\"><span style=\"font-weight: 400;\">Streamline staffing, fleet, and facilities.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">A key technical benefit of EDWs is their separation from operational processes in production applications and transactions. Mullins explained that performing analytics and queries in the EDW delivers a practical way to view the past without affecting daily business computing. This, in turn, means more efficiency, and ultimately, profit. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Also, from a financial point of view, the EDW is a relative bargain among data delivery paradigms, especially compared to less open, stable, or governed ones, like appliances, Hadoop, and data lakes. And thanks to competitive tool and talent markets, the barriers to entry have dropped for those who previously found EDWs too expensive or complex to implement.<\/span><\/p>\n<h3><b>EDW Evolution<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">The EDW emerged from the convergence of opportunity, capability, infrastructure, and the need for converting transactional data into information, all of which have increased exponentially in the last twenty years. As related information technologies evolved, many business rules were changed or broken to make way for data-driven rules. Processes fluctuated from simple to complex, and data would shrink or grow in an ever-changing enterprise environment.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Now, in the era of big data, many more sources and targets are in play. There are well-known challenges of data volume, variety, velocity, veracity, and value putting pressure on traditional EDWs. These concepts and their consequences are creating shifts in enterprise data management architecture from older paradigms like the operational data store to newer ones, like the <a href=\"http:\/\/www.iri.com\/blog\/data-transformation2\/browsing-the-operational-data-store-ods\/\">Enterprise Data Hub<\/a> (EDH),\u00a0<\/span><a href=\"http:\/\/www.iri.com\/blog\/big-data-2\/voracity-and-the-logical-data-warehouse-ldw\/\"><span style=\"font-weight: 400;\">logical data warehouse<\/span><\/a><span style=\"font-weight: 400;\"> (LDW), and\u00a0<\/span><a href=\"http:\/\/www.iri.com\/blog\/business-intelligence\/the-use-of-data-lakes\/\"><span style=\"font-weight: 400;\">data lake<\/span><\/a><span style=\"font-weight: 400;\">, which were designed to also accommodate more modern data stores and analytic needs.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a result, some data management experts now consider EDWs to be a legacy architecture, but one still able to perform routine workloads associated with queries, reports, and analytics.<\/span><span style=\"line-height: 1.5;\">\u00a0<\/span><\/p>\n<h3><b>Voracity and the EDW<\/b><\/h3>\n<p><span style=\"font-weight: 400;\">The IRI Voracity platform for data discovery, integration, migration, governance, and analytics supports traditional EDW architectures, as well as operational data stores, LDWs, and data lakes. Voracity is powered by the multi-threaded IRI CoSort transformation engine by default, or by Hadoop MR2, Spark, Spark Stream, Storm, or Tez engines against data in HDFS. <\/span><\/p>\n<p><span style=\"font-weight: 400;\">Both use the same <\/span><a href=\"http:\/\/www.iri.com\/products\/cosort\/sortcl-metadata\"><span style=\"font-weight: 400;\">metadata<\/span><\/a><span style=\"font-weight: 400;\"> and <\/span><a href=\"http:\/\/www.iri.com\/products\/workbench\"><span style=\"font-weight: 400;\">Eclipse IDE<\/span><\/a><span style=\"font-weight: 400;\"> for job management, so that execution choice is just a (seamless &#8220;map once, deploy anywhere&#8221;) click-to-run <a href=\"http:\/\/www.iri.com\/solutions\/big-data\/hadoop-optional\">option<\/a>. Either way, the goal is data warehouse optimization and big data integration through fast, consolidated data transformations.<\/span><\/p>\n<p><a href=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/08\/vorcity-flyer-front-2019-no-top-e1567188793648.png\"><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-13122 aligncenter\" src=\"http:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/08\/vorcity-flyer-front-2019-no-top-e1567188793648-1024x596.png\" alt=\"\" width=\"571\" height=\"332\" srcset=\"https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/08\/vorcity-flyer-front-2019-no-top-e1567188793648-1024x596.png 1024w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/08\/vorcity-flyer-front-2019-no-top-e1567188793648-300x175.png 300w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/08\/vorcity-flyer-front-2019-no-top-e1567188793648-768x447.png 768w, https:\/\/www.iri.com\/blog\/wp-content\/uploads\/2019\/08\/vorcity-flyer-front-2019-no-top-e1567188793648.png 1110w\" sizes=\"(max-width: 571px) 100vw, 571px\" \/><\/a><\/p>\n<p><span style=\"font-weight: 400;\">Voracity handles a wide range of data <\/span><a href=\"http:\/\/www.iri.com\/products\/workbench\/data-sources\"><span style=\"font-weight: 400;\">sources<\/span><\/a><span style=\"font-weight: 400;\"> and targets, and addresses a number of data warehouse-related requirements, including: data profiling and classification, ETL diagramming, plus wizards for slowly changing dimensions, change data capture, pivoting, and master data management. Scripts support complex transformations, data quality, and elaborate reporting. All of its design and deployment facilities are exposed in the one GUI.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Governance-minded data warehouse architects should appreciate Voracity\u2019s data discovery and protection wizards, which automate PII data identification and masking. Its subsetting and test data generation wizards facilitate the database and EDW prototyping as well. Metadata management is available through cloud-enabled asset repositories, with graphical lineage impact analysis through Erwin EDGE (formerly the AnalytiX DS Governance platform).<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For users of existing DB, BI, and DW software &#8212; and ETL tools in particular &#8212; Voracity can either <a href=\"http:\/\/www.iri.com\/solutions\/data-integration\/etl-tool-acceleration\">accelerate<\/a> or <a href=\"http:\/\/www.iri.com\/solutions\/data-integration\/replatform-etl\">replace<\/a> them. For example, Voracity engines can be called into existing workflows to optimize unloads, transforms (especially sorts, joins, and aggregations), and loads (through flat-file pre-sort). A<\/span><\/p>\n<p><span style=\"font-weight: 400;\">lternatively, Erwin Mapping Manager and CATfx templates can automate a re-platforming process by converting most of the code in a legacy ETL tool into the equivalent jobs in Voracity. This effort can be undertaken and validated before any Voracity costs are incurred, but once complete, would free up hundreds of thousands of dollars and CPU cycles no longer needed.<br \/>\n<\/span><br \/>\n<span style=\"font-weight: 400;\">Email <\/span><a href=\"mailto:voracity@iri.com\"><span style=\"font-weight: 400;\">voracity@iri.com<\/span><\/a><span style=\"font-weight: 400;\"> for more information on the use of Voracity in an EDW, ODS, LDW or data lake.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>For the last 30 or so years, the precursor to most large scale business intelligence (BI) environments has been the Enterprise Data Warehouse (EDW). A data warehouse (DW) is usually a central database (DB) for reporting, planning, and analyzing summarized, subject-matter data integrated from disparate, historical transaction sources. An EDW uses technology to move internal<\/p>\n<div><a class=\"btn-filled btn\" href=\"https:\/\/www.iri.com\/blog\/data-transformation2\/the-enterprise-data-warehouse-then-and-now\/\" title=\"The Enterprise Data Warehouse, Then and Now\">Read More<\/a><\/div>\n","protected":false},"author":3,"featured_media":12076,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":""},"categories":[1,776,217,34],"tags":[1001,273,25,52,1136,280,14,1163,519,658,1135,1018,106,1042,552,46,102,1139,1079,789,851,281,369,1138,1137,1140,1141,1142,675],"class_list":["post-10279","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-transformation2","category-etl","category-iri","category-business","tag-analytix-ds","tag-bi","tag-big-data","tag-business-intelligence-2","tag-crm","tag-data-discovery","tag-data-masking","tag-data-wrangling","tag-database","tag-db","tag-dwbi-architecture","tag-edw","tag-elt","tag-enterprise-data-warehouse","tag-erp","tag-etl-tools","tag-extract-transform-load","tag-hadoop-mr2","tag-iot","tag-iri-voracity","tag-master-data-metadata-management","tag-metadata-management-2","tag-nosql","tag-oltp-db","tag-social-media","tag-spark","tag-storm","tag-tez","tag-web-log"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v23.4 (Yoast SEO v23.4) - 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