David Menninger's Analyst Perspectives

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class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/agentmanagement" class="minimal-mobile-menu-link"><span>Agent Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/contactcenter" class="minimal-mobile-menu-link"><span>Contact Center</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/customerexperiencemanagement" class="minimal-mobile-menu-link"><span>Customer Experience Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/fieldservice" class="minimal-mobile-menu-link"><span>Field Service</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/intelligentselfservice" class="minimal-mobile-menu-link"><span>Intelligent Self-Service</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/customerexperience/voiceofthecustomer" class="minimal-mobile-menu-link"><span>Voice of the Customer</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/digitalbusiness" class="minimal-mobile-menu-link"><span>Digital Business</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-applications" class="minimal-mobile-menu-link"><span>Digital Applications</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digitalcommunications" class="minimal-mobile-menu-link"><span>Digital Communications</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-intelligence" class="minimal-mobile-menu-link"><span>Digital Intelligence</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/digital-operations" class="minimal-mobile-menu-link"><span>Digital Operations</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/experiencemanagement" class="minimal-mobile-menu-link"><span>Experience Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/generative-ai" class="minimal-mobile-menu-link"><span>Generative AI</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/performancemanagement" class="minimal-mobile-menu-link"><span>Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/sustainabilityandesg" class="minimal-mobile-menu-link"><span>Sustainability & ESG</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitalbusiness/workmanagement" class="minimal-mobile-menu-link"><span>Work Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/digitaltechnology" class="minimal-mobile-menu-link"><span>Digital Technology</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/businesscontinuity" class="minimal-mobile-menu-link"><span>Business Continuity</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/cloudcomputing" class="minimal-mobile-menu-link"><span>Cloud Computing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/devops" class="minimal-mobile-menu-link"><span>DevOps and Platforms</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/digitalsecurity" class="minimal-mobile-menu-link"><span>Digital Security</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/intelligentautomation" class="minimal-mobile-menu-link"><span>Intelligent Automation</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/internetofthings" class="minimal-mobile-menu-link"><span>IoT and Edge Computing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/itops" class="minimal-mobile-menu-link"><span>ITOps</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/it-service-management" class="minimal-mobile-menu-link"><span>IT Service Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/digitaltechnology/observability" class="minimal-mobile-menu-link"><span>Observability</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/humancapitalmanagement" class="minimal-mobile-menu-link"><span>Human Capital Management</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/employeeexperience" class="minimal-mobile-menu-link"><span>Employee Experience</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/learningmanagement" class="minimal-mobile-menu-link"><span>Learning Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/payrollmanagement" class="minimal-mobile-menu-link"><span>Payroll Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/talentmanagement" class="minimal-mobile-menu-link"><span>Talent Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/totalcompensationmanagement" class="minimal-mobile-menu-link"><span>Total Compensation Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/humancapitalmanagement/workforcemanagement" class="minimal-mobile-menu-link"><span>Workforce Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/marketing" class="minimal-mobile-menu-link"><span>Marketing</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/customerdataplatforms" class="minimal-mobile-menu-link"><span>Customer Data Platforms</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/digitalexperienceplatforms" class="minimal-mobile-menu-link"><span>Digital Experience Platform</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/digitalmarketing" class="minimal-mobile-menu-link"><span>Digital Marketing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/intelligentmarketing" class="minimal-mobile-menu-link"><span>Intelligent Marketing</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/marketingperformancemanagement" class="minimal-mobile-menu-link"><span>Marketing Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/marketing/productexperiencemanagement" class="minimal-mobile-menu-link"><span>Product Experience Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/officeoffinance" class="minimal-mobile-menu-link"><span>Office of Finance</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/businessplanning" class="minimal-mobile-menu-link"><span>Business Planning</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/digitalfinance" class="minimal-mobile-menu-link"><span>Digital Finance</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/erpandcontinuousaccounting" class="minimal-mobile-menu-link"><span>ERP & Continuous Accounting</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/consolidate-and-close-management" class="minimal-mobile-menu-link"><span>Consolidate & Close Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/procure-to-pay" class="minimal-mobile-menu-link"><span>Procure-to-Pay</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeoffinance/order-to-cash" class="minimal-mobile-menu-link"><span>Order-to-Cash</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/officeofrevenue" class="minimal-mobile-menu-link"><span>Office of Revenue</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/digitalcommerce" class="minimal-mobile-menu-link"><span>Digital Commerce</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/partnermanagement" class="minimal-mobile-menu-link"><span>Partner Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/revenuemanagement" class="minimal-mobile-menu-link"><span>Revenue Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/revenueperformancemanagement" class="minimal-mobile-menu-link"><span>Revenue Performance Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/salesengagement" class="minimal-mobile-menu-link"><span>Sales Engagement</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/officeofrevenue/subscriptionmanagement" class="minimal-mobile-menu-link"><span>Subscription Management</span></a></span> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/operationsandsupplychain" class="minimal-mobile-menu-link"><span>Operations & Supply Chain</span></a></span> <ul class="minimal-mobile-menu-submenu level-3"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/continuousplanning" class="minimal-mobile-menu-link"><span>Continuous Planning</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/continuousupplychainanderp" class="minimal-mobile-menu-link"><span>Continuous Supply Chain & ERP</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/productinformationmanagement" class="minimal-mobile-menu-link"><span>Product Information Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/propertytechnology" class="minimal-mobile-menu-link"><span>Property Technology</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/supplierrelationshipmanagement" class="minimal-mobile-menu-link"><span>Supplier Relationship Management</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/focus/operationsandsupplychain/sustainabilitymanagement" class="minimal-mobile-menu-link"><span>Sustainability Management</span></a></span> </li> </ul> </li> </ul> </li> <li class="minimal-has-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industries" class="minimal-mobile-menu-link"><span>Industries</span></a></span> <ul class="minimal-mobile-menu-submenu level-2"> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/automotive" class="minimal-mobile-menu-link"><span>Automotive</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/banking" class="minimal-mobile-menu-link"><span>Banking</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/consumer-products" class="minimal-mobile-menu-link"><span>Consumer Products</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/financial-services" class="minimal-mobile-menu-link"><span>Financial Services</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/food-beverage" class="minimal-mobile-menu-link"><span>Food & Beverage</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/healthcare" class="minimal-mobile-menu-link"><span>Healthcare</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/hospitality" class="minimal-mobile-menu-link"><span>Hospitality</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a href="https://www.isg-research.net/vertical-industry/insurance" class="minimal-mobile-menu-link"><span>Insurance</span></a></span> </li> <li class="minimal-no-mobile-submenu minimal-mobile-menu-item hs-skip-lang-url-rewrite"> <span><a 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We offer a portfolio of advisory, research, thought leadership and digital education services to help optimize market strategy, planning and execution.</p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/analyst_relations" rel="noopener">Analyst Relations</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/demand_generation" rel="noopener">Demand Generation</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/services/vendors/product-marketing" rel="noopener">Product Marketing</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/vendor-coverage" target="_self" role="menuitem">Market Coverage</a></strong></p> <p><strong><a href="https://www.ventanaresearch.com/briefing" target="_self" role="menuitem">Request a Briefing</a></strong><br><br><br></p></div> <span id="hs_cos_wrapper_dnd_area-module-2_" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_simple_menu" style="" data-hs-cos-general-type="widget" data-hs-cos-type="simple_menu"><ul></ul></span> <div class="bottom_rich_text"> </div> </div> </div> </div> </div> </div> <script src="https://davidmenninger.isg-research.net/hs-fs/hub/2035844/hub_generated/template_assets/90531223305/1725882478811/Ventana_Research_2023/js/mmenu.min.js"></script> </div> </div> </div> </div> </div> </div> </div> </div> <div id="hs_cos_wrapper_Structured_data" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"></div> <div class="body-container-wrapper"> <div class="blog-header background-image post_page_2" data-bg-color="secondary" style=" background-size:cover; background-position:50%; background-repeat:no-repeat; background-image:url(https://davidmenninger.isg-research.net/hubfs/_images/_backgrounds_expertise/2024_VR_AI_Expertise_Image_AdobeStock_637057734_1200px.jpg); ">  <div class="row-fluid"> <div class="blog-header__inner dnd-row"> <div class="blog--post__image dnd-section"> <img class="analyst-header-photo" src="https://davidmenninger.isg-research.net/hubfs/_images/_team/Menninger.David%20Pic.Circle.png" style="padding: 10px; "> <br> <h1 class="analyst-header-title">David Menninger's Analyst Perspectives</h1>  </div> </div> </div> </div> <div class="content-wrapper-listing blog-single"> <div class="blog-content-wrapper"> <div>  <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/microsoft-azure-cloud-computing-for-data-and-analytics">Microsoft Azure: Cloud Computing for Data and Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">17 March 2021 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png?width=200&name=AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png?width=100&name=AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png?width=200&name=AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png?width=300&name=AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png?width=400&name=AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png?width=500&name=AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png?width=600&name=AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed <a href="/emerging-data-platforms-tackle-big-challenges" rel="noopener" target="_blank">here</a> are essential for organizations to effectively manage their data assets.</p> </div> <a href="https://davidmenninger.isg-research.net/microsoft-azure-cloud-computing-for-data-and-analytics"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/embedded-analytics">embedded analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/analytics">Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/collaboration">Collaboration</a>, <a href="https://davidmenninger.isg-research.net/topic/data-governance">Data Governance</a>, <a href="https://davidmenninger.isg-research.net/topic/data-lake">Data Lake</a>, <a href="https://davidmenninger.isg-research.net/topic/data-preparation">Data Preparation</a>, <a href="https://davidmenninger.isg-research.net/topic/data">Data</a>, <a href="">Microsoft Azure</a>, <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/diving-deeper-into-the-data-lake">Diving Deeper into the Data Lake</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">13 November 2020 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png?width=200&name=Ventana_Research_2020_Assertion_Data_Lakes.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png?width=100&name=Ventana_Research_2020_Assertion_Data_Lakes.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png?width=200&name=Ventana_Research_2020_Assertion_Data_Lakes.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png?width=300&name=Ventana_Research_2020_Assertion_Data_Lakes.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png?width=400&name=Ventana_Research_2020_Assertion_Data_Lakes.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png?width=500&name=Ventana_Research_2020_Assertion_Data_Lakes.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png?width=600&name=Ventana_Research_2020_Assertion_Data_Lakes.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called <a href="/david-menninger/2016/05/11/data-lakes-safe-way-to-swim-in-big-data" rel="noopener" target="_blank">Data Lakes: Safe Way to Swim in Big Data?</a> for background. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings.</p> </div> <a href="https://davidmenninger.isg-research.net/diving-deeper-into-the-data-lake"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/data-governance">Data Governance</a>, <a href="https://davidmenninger.isg-research.net/topic/data-lake">Data Lake</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/data">Data</a>, <a href="https://davidmenninger.isg-research.net/topic/digital-technology">Digital Technology</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/david-menninger/2016/08/18/denodo-makes-data-virtualization-relevant-to-big-data-and-analytics">Denodo Makes Data Virtualization Relevant to Big Data and Analytics</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">18 August 2016 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/virtualizationtrending.jpg?width=200&name=virtualizationtrending.jpg" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/virtualizationtrending.jpg?width=100&name=virtualizationtrending.jpg 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/virtualizationtrending.jpg?width=200&name=virtualizationtrending.jpg 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/virtualizationtrending.jpg?width=300&name=virtualizationtrending.jpg 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/virtualizationtrending.jpg?width=400&name=virtualizationtrending.jpg 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/virtualizationtrending.jpg?width=500&name=virtualizationtrending.jpg 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/virtualizationtrending.jpg?width=600&name=virtualizationtrending.jpg 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>Data virtualization is not new, but it has changed over the years. The term describes a process of combining data on the fly from multiple sources rather than copying that data into a common repository such as a data warehouse or a data lake, which <u><a href="https://davidmenninger.ventanaresearch.com/2016/05/11/data-lakes-safe-way-to-swim-in-big-data/" target="_blank">I have written about</a></u>. There are many reasons for an organization concerned with managing its data to consider data virtualization, most stemming from the fact that the data does not have to be copied to a new location. It could, for instance,...</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2016/08/18/denodo-makes-data-virtualization-relevant-to-big-data-and-analytics"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/data-integration">Data Integration</a>, <a href="https://davidmenninger.isg-research.net/topic/data-lake">Data Lake</a>, <a href="https://davidmenninger.isg-research.net/topic/information-applications">Information Applications</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/data-virtualization">Data Virtualization</a> <hr> </div> <div class="blog-index__post-content" style="margin-bottom: 80px"> <div class="post-meta"> <h2 style="font-size: 1.875rem;"><a href="https://davidmenninger.isg-research.net/david-menninger/2016/05/11/data-lakes-safe-way-to-swim-in-big-data">Data Lakes: Safe Way to Swim in Big Data?</a></h2> <div class="author-meta"> Posted by <a href="https://davidmenninger.isg-research.net/author/david-menninger"> David Menninger </a> on <span class="post-date">11 May 2016 </span> </div>  <hr> </div> <div class="list-card-p"><div class="wrapingimage"> <img src="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=200&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png" width="200" !important: srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=100&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=200&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=300&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 300w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=400&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 400w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=500&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 500w, https://davidmenninger.isg-research.net/hs-fs/hubfs/Imported_Blog_Media/vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png?width=600&name=vr_big_data_analytics_21_external_data_sources_for_big_data_analytics.png 600w" sizes="(max-width: 200px) 100vw, 200px"> </div> <p>It has been more than five years since James Dixon of Pentaho coined the term “data lake.” <a href="https://jamesdixon.wordpress.com/2010/10/14/pentaho-hadoop-and-data-lakes/" target="_blank">His original post</a> suggests, “If you think of a data mart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state.” The analogy is a simple one, but in my experience talking with many end users there is still mystery surrounding the concept. In this post I’d like to clarify what a data lake is, review the...</p> </div> <a href="https://davidmenninger.isg-research.net/david-menninger/2016/05/11/data-lakes-safe-way-to-swim-in-big-data"><span class="readmore"> Read More </span></a><br><br> Topics: <a href="https://davidmenninger.isg-research.net/topic/big-data">Big Data</a>, <a href="https://davidmenninger.isg-research.net/topic/data-science">Data Science</a>, <a href="https://davidmenninger.isg-research.net/topic/predictive-analytics">Predictive Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/social-media">Social Media</a>, <a href="https://davidmenninger.isg-research.net/topic/business-analytics">Business Analytics</a>, <a href="https://davidmenninger.isg-research.net/topic/business-intelligence">Business Intelligence</a>, <a href="https://davidmenninger.isg-research.net/topic/data-governance">Data Governance</a>, <a href="https://davidmenninger.isg-research.net/topic/data-lake">Data Lake</a>, <a href="https://davidmenninger.isg-research.net/topic/governance-risk-compliance-grc">Governance, Risk & Compliance (GRC)</a>, <a href="https://davidmenninger.isg-research.net/topic/information-management">Information Management</a>, <a href="https://davidmenninger.isg-research.net/topic/uncategorized">Uncategorized</a>, <a href="https://davidmenninger.isg-research.net/topic/stratahadoop">Strata+Hadoop</a> <hr> </div> <script> // Check active classes var checkClass = function() { if ( $('.fil_item').hasClass('hide') ) { $('.fil_item').removeClass('hide'); } }; // Category filters $('.all').click( function() { checkClass(); }); // Active tag $('.bttn').click(function(){ $('.bttn').removeClass('active'); $(this).addClass('active'); }) </script> </div> <div class="blog-numeric-pagination span12 ">   </div> </div> <div class="blog-sidebar">   <style> .blog-sidebar h3 { font-size: 18px; margin-bottom: 30px; position: relative; color: var(--light_blue); padding-top:40px; } .btn-secondary, .filter-expand-link { text-transform: uppercase; background-color: #113049!important; border-color: #113049!important; color: #ffffff!important; transition: all .15s ease-in-out; animation-timing-function: ease-in-out; font-family: 'Open Sans', sans-serif; font-style: normal; font-weight: 500; text-decoration: none; font-size: 0.875rem; line-height: 1.4000000000000001rem; border-radius: 6px; height: 3.5rem; text-align: center; padding: 0 2.8125rem; border: 1px solid; display: -webkit-inline-box; display: -ms-inline-flexbox; display: inline-flex; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } .filter-expand-link { text-transform: uppercase; background-color: #113049!important; border-color: #113049!important; color: #ffffff!important; transition: all .15s ease-in-out; animation-timing-function: ease-in-out; font-family: 'Open Sans', sans-serif; font-style: normal; font-weight: 500; text-decoration: none; font-size: 0.875rem; line-height: 1.4000000000000001rem; border-radius: 6px; height: 3.5rem; text-align: center; padding: 0 2.8125rem; border: 1px solid; display: -webkit-inline-box; display: -ms-inline-flexbox; display: inline-flex; -webkit-box-align: center; -ms-flex-align: center; align-items: center; } </style> <span class="hs-cta-wrapper" id="hs-cta-wrapper-960cd0b7-7494-40d3-ac1c-5825d5a075be"><span class="hs-cta-node hs-cta-960cd0b7-7494-40d3-ac1c-5825d5a075be" id="hs-cta-960cd0b7-7494-40d3-ac1c-5825d5a075be"><a href="https://cta-redirect.hubspot.com/cta/redirect/2035844/960cd0b7-7494-40d3-ac1c-5825d5a075be"><img class="hs-cta-img" id="hs-cta-img-960cd0b7-7494-40d3-ac1c-5825d5a075be" style="border-width:0px;" src="https://no-cache.hubspot.com/cta/default/2035844/960cd0b7-7494-40d3-ac1c-5825d5a075be.png" alt="JOIN OUR COMMUNITY"></a></span><script charset="utf-8" src="https://js.hscta.net/cta/current.js"></script><script type="text/javascript"> hbspt.cta.load(2035844, '960cd0b7-7494-40d3-ac1c-5825d5a075be', {"useNewLoader":"true","region":"na1"}); </script></span> <span id="hs_cos_wrapper_module_1495215326694198" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_rich_text" style="" data-hs-cos-general-type="widget" data-hs-cos-type="rich_text"><h3 style="color: var(--light_blue);">Our Analyst Perspective Policy</h3> <ul> <li style="display: none; list-style: none;"> <p>Ventana Research’s Analyst Perspectives are fact-based analysis and guidance on business, <g class="gr_ gr_12 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation only-ins replaceWithoutSep" id="12" data-gr-id="12">industry</g> and technology vendor trends. 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If you have any issue with an Analyst Perspective, please email them to <a href="mailto:ChiefResearchOfficer@isg-research.net">ChiefResearchOfficer@isg-research.net</a></p> </li> </ul> <a class="btn-secondary" href="https://www.isg-research.net/analyst-perspective-policy">View Policy</a></span>  <div id="hs_cos_wrapper_module_168621267066296" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_module" style="" data-hs-cos-general-type="widget" data-hs-cos-type="module"><style> </style> <h3 style="color: #255a89;">Our Analysts</h3>  <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://davidmenninger.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png" alt="David Menninger" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=50&name=Menninger.David%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=100&name=Menninger.David%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=150&name=Menninger.David%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=200&name=Menninger.David%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=250&name=Menninger.David%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Menninger.David%20Pic.Circle.png?width=300&name=Menninger.David%20Pic.Circle.png 300w"> <h5 class="analyst_name">David Menninger</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://jefforr.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png" alt="Jeff Orr" width="100" data-constrained="true" caption="false" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=50&name=Orr.Jeff.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=100&name=Orr.Jeff.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=150&name=Orr.Jeff.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=200&name=Orr.Jeff.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=250&name=Orr.Jeff.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Orr.Jeff.Circle.png?width=300&name=Orr.Jeff.Circle.png 300w" sizes="(max-width: 100px) 100vw, 100px"> <h5 class="analyst_name">Jeff Orr</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://keithdawson.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png" alt="Keith Dawson" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=50&name=Dawson.Keith%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=100&name=Dawson.Keith%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=150&name=Dawson.Keith%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=200&name=Dawson.Keith%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=250&name=Dawson.Keith%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Dawson.Keith%20Pic.Circle.png?width=300&name=Dawson.Keith%20Pic.Circle.png 300w"> <h5 class="analyst_name">Keith Dawson</h5> </a> </div> <div class="span6" style="text-align:center;"> <a href="https://marksmith.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png" alt="Mark Smith" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=50&name=Smith.Mark%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=100&name=Smith.Mark%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=150&name=Smith.Mark%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=200&name=Smith.Mark%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=250&name=Smith.Mark%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Smith.Mark%20Pic.Circle.png?width=300&name=Smith.Mark%20Pic.Circle.png 300w"> <h5 class="analyst_name">Mark Smith</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://mattaslett.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png" alt="Matt Aslett" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=50&name=Aslett.Matt%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=100&name=Aslett.Matt%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=150&name=Aslett.Matt%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=200&name=Aslett.Matt%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=250&name=Aslett.Matt%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Aslett.Matt%20Pic.Circle.png?width=300&name=Aslett.Matt%20Pic.Circle.png 300w"> <h5 class="analyst_name">Matt Aslett</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://matthewbrown.isg-research.net/"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png" alt="Matthew Brown" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=50&name=profile_vr_matthew_brown_circle_900x900.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=100&name=profile_vr_matthew_brown_circle_900x900.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=150&name=profile_vr_matthew_brown_circle_900x900.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=200&name=profile_vr_matthew_brown_circle_900x900.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=250&name=profile_vr_matthew_brown_circle_900x900.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/profile_vr_matthew_brown_circle_900x900.png?width=300&name=profile_vr_matthew_brown_circle_900x900.png 300w"> <h5 class="analyst_name">Matthew Brown</h5> </a> </div> </div>   <div class="row-fluid"> <div class="span6" style="text-align: center;"> <a href="https://robertkugel.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png" alt="Robert Kugel" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=50&name=Kugel.Robert%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=100&name=Kugel.Robert%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=150&name=Kugel.Robert%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=200&name=Kugel.Robert%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=250&name=Kugel.Robert%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Kugel.Robert%20Pic.Circle.png?width=300&name=Kugel.Robert%20Pic.Circle.png 300w"> <h5 class="analyst_name">Robert Kugel</h5> </a> </div> <div class="span6" style="text-align: center;"> <a href="https://stephenhurrell.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png" alt="Stephen Hurrell" width="100" data-constrained="true" caption="false" sizes="(max-width: 100px) 100vw, 100px" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=50&name=Hurrell.Stephen%20Pic.Circle.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=100&name=Hurrell.Stephen%20Pic.Circle.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=150&name=Hurrell.Stephen%20Pic.Circle.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=200&name=Hurrell.Stephen%20Pic.Circle.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=250&name=Hurrell.Stephen%20Pic.Circle.png 250w, https://davidmenninger.isg-research.net/hs-fs/hubfs/_images/_team/Hurrell.Stephen%20Pic.Circle.png?width=300&name=Hurrell.Stephen%20Pic.Circle.png 300w"> <h5 class="analyst_name">Stephen Hurrell</h5> </a> </div> </div>   <div class="row-fluid">  <div style="text-align: center;"> <a href="https://blog.isg-research.net"> <img class="analyst_img" style="width: 100px;" src="https://davidmenninger.isg-research.net/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=100&name=ISG_Bug_Reverse_Transparent.png" alt="ISG Software Research" width="100" data-constrained="true" caption="false" srcset="https://davidmenninger.isg-research.net/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=50&name=ISG_Bug_Reverse_Transparent.png 50w, https://davidmenninger.isg-research.net/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=100&name=ISG_Bug_Reverse_Transparent.png 100w, https://davidmenninger.isg-research.net/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=150&name=ISG_Bug_Reverse_Transparent.png 150w, https://davidmenninger.isg-research.net/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=200&name=ISG_Bug_Reverse_Transparent.png 200w, https://davidmenninger.isg-research.net/hs-fs/hubfs/ISG_Bug_Reverse_Transparent.png?width=250&name=ISG_Bug_Reverse_Transparent.png 250w, 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<li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/02">February 2012 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2012/01">January 2012 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/12">December 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/11">November 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/10">October 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/09">September 2011 <span class="filter-link-count" dir="ltr">(4)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/08">August 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/07">July 2011 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/06">June 2011 <span class="filter-link-count" dir="ltr">(6)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/05">May 2011 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/04">April 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/03">March 2011 <span class="filter-link-count" dir="ltr">(7)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/02">February 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2011/01">January 2011 <span class="filter-link-count" dir="ltr">(3)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/12">December 2010 <span class="filter-link-count" dir="ltr">(2)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/11">November 2010 <span class="filter-link-count" dir="ltr">(12)</span></a> </li> <li style="display:none;"> <a href="https://davidmenninger.isg-research.net/archive/2010/06">June 2010 <span class="filter-link-count" dir="ltr">(1)</span></a> </li> </ul> <a class="filter-expand-link" href="#">see all</a> </div> </div> </span> <span id="hs_cos_wrapper_post_filter" class="hs_cos_wrapper hs_cos_wrapper_widget hs_cos_wrapper_type_post_filter" style="" data-hs-cos-general-type="widget" data-hs-cos-type="post_filter"><div class="block"> <h3>Posts by Topic</h3> <div class="widget-module"> <ul> <li> <a href="https://davidmenninger.isg-research.net/topic/ai">AI <span class="filter-link-count" dir="ltr">(11)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/ai-and-machine-learning">AI and Machine Learning <span class="filter-link-count" dir="ltr">(95)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/alteryx">alteryx <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/alteryx-inspire">Alteryx Inspire <span class="filter-link-count" dir="ltr">(1)</span></a> </li> <li> <a href="https://davidmenninger.isg-research.net/topic/analytic-data-platforms">analytic data platforms <span class="filter-link-count" dir="ltr">(1)</span></a> </li> 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"@context": "http://schema.org", "@type": "BlogPosting", "headline": "Microsoft Azure: Cloud Computing for Data and Analytics", "mainEntityOfPage": "https://davidmenninger.isg-research.net/microsoft-azure-cloud-computing-for-data-and-analytics", "datePublished": "17/03/2021", "dateModified": "17/03/2021", "articleBody": "Organizations are increasingly using data as a strategic asset, which makes data services critical. Huge volumes of data need to be stored, managed, discovered and analyzed. Cloud computing and storage approaches provide enterprises with various capabilities to store and process their data in third-party data centers. The advent of data platforms previously discussed here are essential for organizations to effectively manage their data assets. Many organizations are shifting their data and analytics workloads toward a consumption-based, hyper-scale public cloud model. Cloud offers organizations the flexibility to offload administration and management of some of their systems. Migrating data to the cloud enables organizations to easily scale resources up and down, on demand. The other major benefit of using cloud service providers is the reduction of upfront costs. Ventana Research asserts that through 2022, more than one-half of organizations will migrate on-premises workloads to cloud data platforms, allowing them to focus more on business needs rather than maintaining systems. Microsoft Azure is a scalable cloud platform with computing, networking, storage, database and management, along with advanced services such as analytics and machine learning (ML). Azure includes a flexible platform that helps developers build, deploy and manage enterprise, mobile, web and internet of things (IoT) applications, for any platform or device, without having to worry about the underlying infrastructure. As a cloud-based platform, Azure enables customers to devote more resources to development and use of applications that benefit their organizations, rather than managing on-premises hardware and software. Featuring unique hybrid capabilities, Azure facilitates easy mobility and a reliable, consistent platform between on-premises and public cloud. Azure provides a broad range of hybrid connections including virtual private networks (VPNs), caches, content delivery networks (CDNs) and ExpressRoute connections to improve usability and performance. Azure is a cloud platform that offers blockchain as a service (BaaS), ML, bots, and cognitive API capabilities. Azure Data Lake is built on Azure Blob Storage, which is the Microsoft object storage system for the cloud. The system features low-cost, tiered storage and high availability plus disaster-recovery capabilities. It integrates with other Azure services, including Azure Data Factory, a tool for creating and running extract, transform and load (ETL) and extract, load and transform (ELT) processes. Azure is based on the Apache Hadoop YARN cluster-management platform. It can scale dynamically across SQL servers within the data lake, as well as servers in Azure SQL Database and Azure SQL Data Warehouse. Because the platform supports all types of data, it can be used to integrate enterprise data from multiple sources into a single data warehouse. Azure Synapse Analytics was introduced last year to help organizations use their data and deploy all types of analysis including artificial intelligence (AI) and ML. Azure Synapse Analytics brings together data integration, enterprise data warehousing and big data analytics. It enables an organization to query both relational and nonrelational data at petabyte scale using languages such as SQL, Python, .NET, Java, Scala and R. This makes it highly suitable for different analysis workloads, data engineering and developer profiles. The integration between Azure Synapse and Power BI offers the capabilities to analyze very large volumes of data. For example, an organization can implement composite models and aggregations to benefit from both the scale of Azure Synapse and the performance of the Power BI Vertipaq, in-memory columnar database engine. Microsoft claims these features can be used to enable interactive analysis over petabyte-scale datasets. Ventana Research asserts that through 2023, three-quarters of Chief Data Officers’ primary concerns will be governing the privacy and security of their organization’s data. Azure Purview is a new data-governance system that is integrated with the Microsoft Information Protection service. It enables enterprises to map, catalogue, understand and manage operational and analytical data. Purview can automate data discovery and data cataloging while minimizing compliance risk. It can be used with Azure Synapse Analytics. Azure Purview includes three main components: Data discovery: Azure Purview can automatically find all of an organization’s data on-premises or in the cloud, even those managed by other providers, and evaluate the characteristics and sensitivity of the data. Data catalog: Azure Purview enables all users to search for data using a simple web-based experience. Visual graphs let users quickly see if data of interest is from a trusted source. Data governance: Azure Purview provides an end-to-end view of a company’s data landscape, enabling data officers to efficiently govern data use. This enables governance insights such as the distribution of data across environments, how data is moving and where sensitive data is stored. When integrated with Microsoft’s BI tools, Purview can provide enhanced governance and cataloguing capabilities. This integration will help with better discovery of hybrid data, facilitating a more complete understanding of data and analytics regardless of where they reside. As Microsoft has built the Azure Synapse platform, it has migrated and integrated the on-premises SQL Server offerings. Azure Synapse integrates many pieces of software required in data and analytics processes, saving organizations the time and effort of combining offerings from different vendors. Organizations already working with Microsoft products, especially those using Microsoft’s Azure cloud services, will find Synapse fits easily into their environments. Those who are using other cloud services will want to compare their provider’s offerings and integrations with Microsoft’s. The Azure Synapse platform provides a comprehensive and competitive offering in the marketplace that builds on Microsoft’s widespread presence. It positions Microsoft to capture more of the emerging cloud-based data warehouse and cloud-based data lake markets. Organizations can apply Azure Synapse to any of their data and analytics processes and the inclusion of Purview will help organizations provide proper data governance, which is often an afterthought. Our Dynamic Insights research on data lakes shows Marketing, Customer Service, Sales, and Finance are the most common application areas for data platforms. As a broad data platform Synapse also includes Azure Stream Analytics for real-time processing and analysis of fast-moving streams of data. These are often new applications where organizations are evaluating and selecting technologies. Clearly, organizations that are “Microsoft shops,” especially SQL Server and/or Power BI shops, should evaluate Synapse and Purview. However, organizations must also evaluate how well the offerings fit with their existing investments and skills in other technologies. While Synapse offers significantly expanded capabilities, it should be compared with other data-platform vendors. When evaluating Synapse, consider whether its hybrid and multi-cloud options meet your organization's needs. And, although Synapse represents an improvement in integration, there are still many parts to the solution. Further integration and ease of use would help organizations implement, and be more productive with, the platform. Data and analytics are moving to the cloud and vendors’ platforms are becoming more sophisticated and integrated. Azure Synapse represents a significant advance in Microsoft’s offering. With Purview, it brings to the fore a focus on governance that all organizations should have. We recommend organizations consider Synapse when evaluating their data platform requirements. Regards, David Menninger", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/AP_DM_Microsoft%20Azure_Cloud%20Computing%20for%20Data%20and%20Analytics-1.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Diving Deeper into the Data Lake", "mainEntityOfPage": "https://davidmenninger.isg-research.net/diving-deeper-into-the-data-lake", "datePublished": "13/11/2020", "dateModified": "13/11/2020", "articleBody": "A data lake is a centralized repository designed to house big data in structured, semi-structured and unstructured form. I have been covering the data lake topic for several years and encourage you to check out an earlier perspective called Data Lakes: Safe Way to Swim in Big Data? for background. Our data lake research has uncovered some points to consider in your efforts, and I’d like to offer a deeper dive into our findings. Planning and deploying data lakes can be time consuming and complicated. And while there are complications and risks in working with data lakes, there are also significant benefits such as creating a competitive advantage and helping lower operational costs. You can learn more about data lake benefits and challenges in this dedicated perspective on the confluence with data warehouses. Our research shows that data lakes require some commitment and determination. Organizations using data lakes for at least two years are more satisfied with their deployment results than those who have been using them for less than two years. One of the reasons data lakes require time and investment is that most successful implementations rely on a big data framework designed to store and process huge datasets. Hadoop was popular in the early days of data lakes and is still widely deployed by organizations. It has been successfully used to scale up to thousands of nodes, and to store and process petabytes of structured and unstructured data. More recently, organizations have turned to cloud-based object storage adoption as well as on-premises object storage as an alternative to HDFS (Hadoop Distributed File System). Object storage is generally inexpensive and allows for separation of storage and compute, which enables for better scalability required by data lakes. Regardless of whether Hadoop or object storage was used, our research shows improved confidence and satisfaction compared to organizations relying on relational data lakes or other alternatives. We found that 60% of those using Hadoop or object storage reported they were satisfied/somewhat satisfied compared to only 18% who are not (and 51% are confident/very confident compared to only 27% that are not) in their organization’s ability to analyze big data. Organizations are continuously growing their data lakes, collecting data from various sources, teams and departments. But collecting data is only half of the equation. Many organizations can’t take full advantage of their data lakes because they don’t know what data actually exists. The data needs to be cataloged so that it can be called up for on-demand analysis by analysts and data scientists. Catalogs can help provide context—where the data originated, when it was last updated and how it can and should be used. Data catalogs also improve governance and reduce the risk of errors by indicating which sources are approved and certified. Perhaps the most fundamental benefit of a data catalog is that it makes it easier to search for and find the information needed for an analysis. Our research found that data catalog users are significantly more satisfied (68% compared to 39% who are not using data catalogs) in their organization’s ability to analyze big data. Organizations face a multitude of challenges with data lakes such as replicating data, data security and data governance, which includes regulations such as GDPR restricting data location. Data virtualization can be used to solve some of these challenges by accessing data in place as it is needed rather than moving or copying the data to another location. In our research, we found that nearly one-quarter (24%) of organizations already include data virtualization in their data lake implementations. Additionally, half (47%) of organizations are planning to include the capability at some point in the future. Data virtualization can integrate various data sources across multiple data types and locations, providing the end-user with a single logical layer, through which data can be easily accessed and shared across organizations. It also offers a way to unify data governance and security controls. Our research shows a significant difference in satisfaction between organizations using Data Virtualization and those that are not. More than three-quarters (79%) of organizations using data virtualization are satisfied with their data lake deployments compared to only 36% that are not using data virtualization. Armed with this information, your organization will be better prepared to take a deeper dive into data lakes. Adopting a big data framework may not be as easy as trying to use your existing platform, but the research shows it can be worth the effort. Think about adopting a data catalog, if you haven’t already, to make it easier for your analysts to find and take advantage of the data you have collected. And consider the role of data virtualization in your data lake implementation for assembling and governing large amounts of data from various parts of the organization. Ventana Research is currently conducting Data Lake Dynamic Insights research. We invite you to take the survey and contribute your organization’s perspective on data lakes and gain immediate guidance on improving your efforts. Thank you for your consideration! Regards, Dave", "image": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/Ventana_Research_2020_Assertion_Data_Lakes.png", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Denodo Makes Data Virtualization Relevant to Big Data and Analytics", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2016/08/18/denodo-makes-data-virtualization-relevant-to-big-data-and-analytics", "datePublished": "18/08/2016", "dateModified": "18/08/2016", "articleBody": "Data virtualization is not new, but it has changed over the years. The term describes a process of combining data on the fly from multiple sources rather than copying that data into a common repository such as a data warehouse or a data lake, which I have written about. There are many reasons for an organization concerned with managing its data to consider data virtualization, most stemming from the fact that the data does not have to be copied to a new location. It could, for instance, eliminate the cost of building and maintaining a copy of one of the organization’s big data sources. Recognizing these benefits, many database and data integration companies offer data virtualization products. Denodo, one of the few independent, best-of-breed vendors in this market today, brings these capabilities to big data sources and data lakes. Google Trends presents a graphic representation of the decline of the popularity of the term data federation and the rise in popularity of the term data virtualization over time. The change in terminology corresponds with a change in technology. The industry has evolved from a data federation approach to today’s cost-based optimization approach. In a federated approach, queries are sent to the appropriate data sources without much intelligence about the overall query or the cost of the individual parts of the federated query. Each underlying data source performs its portion of the workload as best it can and returns the results. The various parts are combined and additional post-processing performed if necessary, for example to sort the combined result set. Denodo takes a different approach. Its tools consider the costs of each part of the individual query and evaluate trade-offs. As the saying goes, there’s more than one way to skin a cat; in this case there’s more than one way to execute a SQL statement. For example, suppose you wish to create a list of all sales of a certain set of products. Your company has 1,000 products (maintained in one system) and hundreds of millions of customer transactions (maintained in another system). The federated approach would bring both data sets to the federated system, join them and then find the desired subset of products. An alternative would be to ship the table of 1,000 products to the system that holds the customer transactions, load it as a temporary table and join it to the customer transaction data to identify the desired subset before sending the product data back to its source. Today’s data virtualization evaluates the costs in time of the two alternatives and selects the one that would produce the result set the fastest. Data virtualization can make it easier, and therefore faster, to set up access to data sources in an organization. Using Denodo users connect to existing data sources, which become available as a virtual resource. In the case of data warehouses or data lakes, this virtual representation is often referred to as a logical data warehouse or a logical data lake. No matter how hard you work to consolidate data into a central repository, there are often pieces of data that have to be combined from multiple data sources. We find that such issues are common. In our big data integration benchmark research one-fourth (26%) of organizations said that data virtualization is a key activity for their big data analytics, yet only 14 percent said that they have adequate data virtualization capabilities. Not all the work is eliminated by data virtualization. You must still design the logical model for the data that you want to provide, such as which tables and which columns to include, but that’s all. Virtualization eliminates load processes and the need to update the data. In the case of big data, there are no extra clusters to set up and maintain. The logical data warehouse or data lake uses the security and governance system already in place. As a result, users can avoid some of the organizational battles about data access since the “owner” of the data continues to maintain the rights and restrictions on the data. Our research shows that organizations that have adequate data virtualization capabilities are more often satisfied with the way their organization manages big data than are organizations as a whole (88% vs. 58%) and are more confident in the data quality of their big data integration efforts (81% vs. 54%). In its most recent release, version 6.0, Denodo enhanced its cost-based query optimizer for data virtualization. Many of the optimizer’s features would be found in any decent relational database management system, but the challenge becomes greater when the underlying resources are scattered among multiple systems. To address this issue Denodo collects and maintains statistics about the various data sources that are evaluated at run time to determine the optimal way to execute queries. The product offers connectivity to a variety of data sources, both structured and unstructured, including Hadoop, NoSQL, documents and websites. It can be deployed on premises, in the cloud using Amazon Web Services or in a hybrid configuration. Performance can be a key factor in user acceptance of data virtualization; users will balk if access is too slow. Denodo has published some benchmarks showing that performance of its product can be nearly identical to accessing data loaded into an analytical database. I never place much emphasis on vendor benchmarks as they may or may not reflect an actual organization’s configuration and requirements. However, the fact that Denodo produces this type of benchmark indicates its focus on minimizing the performance overhead associated with data virtualization. When I first looked at Denodo, prior to the 6.0 release, I expected to see more optimization techniques built into the product. There’s always room for improvement, but with the current release the company has made great strides and addressed many of these issues. In order to maximize the software’s value to customers, I’d like to see the company invest in developing more technology partnerships with providers of data sources and analytic tools. Users would also find it valuable if Denodo could help manage and present consolidated lineage information. Not only do users need access to data, they need to understand how data is transformed both inside and outside Denodo. If your organization is considering data virtualization technology, I recommend you evaluate Denodo. The company won the 2015 Ventana Research Technology Innovation Award for Information Management, and its customer Autodesk won the 2015 Leadership Award in the Big Data Category. If your organization is deluged with big data but is not considering data virtualization, it probably should be. As our research shows, it can lead to greater satisfaction with and more confidence in the quality of your data. Regards, David Menninger SVP & Research Director Follow Me on Twitter @dmenningerVR and Connect with me on LinkedIn.", "image": { "@type": "ImageObject", "url": "http://cdn2.hubspot.net/hubfs/2035844/Imported_Blog_Media/virtualizationtrending.jpg", "height": 400, "width": 750 }, "author": { "@type": "Person", "name": "David Menninger", "url": "https://davidmenninger.isg-research.net/author/david-menninger", "sameAs": "http://www.linkedin.com/in/davidmenninger" }, "publisher": { "@type": "Organization", "name": "ISG", "url": "davidmenninger.isg-research.net", "logo": { "@type": "ImageObject", "url": "https://www.ventanaresearch.com/hubfs/ISG_Software_Research_Logo_Color@2x.png" } } }</script> <script type="application/ld+json"> { "@context": "http://schema.org", "@type": "BlogPosting", "headline": "Data Lakes: Safe Way to Swim in Big Data?", "mainEntityOfPage": "https://davidmenninger.isg-research.net/david-menninger/2016/05/11/data-lakes-safe-way-to-swim-in-big-data", "datePublished": "11/05/2016", "dateModified": "11/05/2016", "articleBody": "It has been more than five years since James Dixon of Pentaho coined the term “data lake.” His original post suggests, “If you think of a data mart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state.” The analogy is a simple one, but in my experience talking with many end users there is still mystery surrounding the concept. In this post I’d like to clarify what a data lake is, review the reasons an organization might consider using one and the challenges they present, and outline some developments in software tools that support data lakes. Data lakes offer a way to deal with big data. A data lake combines massive storage capabilities for any type of data in any format as well as processing power to transform and analyze the data. Often data lakes are implemented using Hadoop technology. Raw, detailed data from various sources is loaded into a single consolidated repository to enable analyses that look across any data available to the user. To understand why data lakes have become popular it’s helpful to contrast this approach with the enterprise data warehouse (EDW). In some ways an EDW is similar to a data lake. Both act as a centralized repository for information from across an organization. However, the data loaded into an EDW is generally summarized, structured data. EDWs are typically based on relational database technologies, which are designed to deal with structured information. And while advances have been made in the scalability of relational databases, they are generally not as scalable as Hadoop. Because these technologies are not as scalable, it is not practical to store all the raw data that come in to the organization. Hence there is a need for summarization. In contrast, a data lake contains the most granular data generated across the organization. The data may be structured information, such as sales transaction data, or unstructured information, such as email exchanged in customer service interactions. Hadoop is often used with data lakes because it can store and manage large volumes of both structured and unstructured data for subsequent analytic processing. The advent of Hadoop made it feasible and more affordable to store much larger volumes of information, and organizations began collecting and storing the raw detail from various systems throughout the organization. Hadoop has also become a repository for unstructured information such as social media and semistructured data such as log files. In fact, our benchmark research shows that social media data is the second-most important source of external information used in big data analytics. In addition to handling larger volumes and more varieties of information, data lakes enable faster access to information as it is generated. Since data is gathered in its raw form, no preprocessing is needed. Therefore, information can be added to the data lake as soon as it is generated and collected. This approach has caused some controversy with many industry analysts and even vendors to raise concerns about data lakes turning into data swamps. In general, the concerns about data lakes becoming data swamps center around the lack of governance of the data in a data lake, an appropriate topic here. These collections of data should be governed like any other set of information assets within an organization. The challenge was that most of the governance tools and technologies had been developed for relational databases and EDWs. In essence, the big data technologies used for data lakes had gotten ahead of themselves, without incorporating all the features needed to support enterprise deployments. Another, perhaps more minor controversy centers around terminology. I raise this issue so that, regardless of the terminology a vendor chooses, you can recognize data lakes and be aware of the challenges. Cloudera uses the term Enterprise Data Hub to represent essentially the same concept as a data lake. Hortonworks embraces the data lake terminology as evidenced in this post. IBM acknowledges the value of data lakes as well as its challenges in this post, but Jim Kobielus, IBM’s Big Data Evangelist, questioned the terminology in a more recent post on LinkedIn, and the term “data lake” is not featured prominently on IBM’s website. Despite the controversy and challenges, data lakes are continuing to grow in popularity. They provide important capabilities for data science. First, they contain the detailed data necessary to perform predictive analytics. Second, they allow efficient access to unstructured data such as social media or other text from customer interactions. For business this information can create a more complete profile of customers and their behavior. Data lakes also make data available sooner than it might be available in a conventional EDW architecture. Our data and analytics in the cloud benchmark research shows that one in five (21%) organizations are integrating their data in real time. The research also shows that those who integrate their data more often are more satisfied and more confident in their results. Granted, a data lake contains raw information, and it may require more analysis or manipulation since the data is not yet cleansed, but time is money and faster access can often lead to new revenue opportunities. Half the participants in our predictive analytics benchmark research said they have created new revenue opportunities with their analytics. Cognizant of the lack of governance and management tools some organizations hesitated to adopt data lakes, while others went ahead. Vendors in this space have advanced their capabilities in the meantime. Some, such as Informatica, are bringing data governance capabilities from the EDW world to data lakes. I wrote about the most recent release of Informatica’s big data capabilities, which it calls Intelligent Data Lake. Other vendors are bringing their EDW capabilities to data lakes as well. Information Builders and Teradata both made data lake announcements this spring. In addition, a new category of vendors is emerging focused specifically on data lakes. Podium Data says it provides an “enterprise data lake management platform,” Zaloni calls itself “the data lake company,” and Waterline Data draws its name “from the metaphor of a data lake where the data is hidden below the waterline.” Is it safe to jump in? Well, just like you shouldn’t jump into a lake without knowing how to swim, you shouldn’t jump into a data lake without plans for managing and governing the information in it. Data lakes can provide unique opportunities to take advantage of big data and create new revenue opportunities. With the right tools and training, it might be worth testing the water. 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David Menninger's Analyst Perspectives | Data Lake