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Lake"],":type":"snowflake-site/components/title"},"image":{"id":"image","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--fcadecbf-8567-411f-b545-75a1d3c23bae/default-simplified-lead-space.png?quality=85&preferwebp=true","alt":"Long exposure top down view of people walking on a pavement ","lazyEnabled":true,"height":"403","width":"1280",":type":"snowflake-site/components/image"},":type":"snowflake-site/components/simplified-lead-space"},"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"marketo_container":"aem-GridColumn 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The two have recently converged to form a new and richer data architecture. The architecture is fairly new, and not many organizations have embraced it yet, although the majority of respondents to our recent TDWI Best Practices Report survey see it as an opportunity because it provides more options for managing an increasingly diverse range of data structures, end user types, and business use cases.\u003C/p\u003E\n\u003Cp\u003EWithin this evolved environment, data warehouses and data lakes can incorporate distinct, but integrated, overlapping, and interoperable architectures that incorporate standard functional layers. These unifying layers include data storage, mixed workload management, data virtualization, content ETL, and data governance and protection. 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