{"id":12135,"date":"2021-08-16T13:40:32","date_gmt":"2021-08-16T13:40:32","guid":{"rendered":"http:\/\/ismiletechnologies.com\/?p=12135"},"modified":"2021-10-11T23:28:07","modified_gmt":"2021-10-11T17:58:07","slug":"traditional-data-warehouse-vs-data-lake","status":"publish","type":"post","link":"https:\/\/ismiletechnologies.com\/en_us\/digital-transformation\/traditional-data-warehouse-vs-data-lake\/","title":{"rendered":"Traditional data warehouse Vs Data lake"},"content":{"rendered":"\t\t
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Data\u00a0Lake Architecture\u00a0vs.\u00a0Traditional Datawarehouse Architecture<\/strong>\u00a0<\/p>\n\n

Datalake\u00a0architecture\u00a0<\/p>\n\n

Data\u00a0lake is a repository for storing huge volumes of structured, unstructured and semi-structured data. There is no limit on the file size or the format that can be stored.\u00a0\u00a0<\/p>\n\n

Data\u00a0lake architectural components<\/strong>\u00a0<\/p>\n\n

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  • Data Ingestion- It contains connectors to extract data from multiple data sources\u00a0(databases, servers, emails etc) ,\u00a0in a variety of format( structured, semi-structured and unstructured). It provides data curation options.\u00a0<\/li>\n
  • Data storage component- It is able to store raw and curated data in any format. This component allows compression and encryption of data.\u00a0<\/li>\n
  • Security components- Security is enabled at all stages of information flow in data lake be it data ingestion, data storage, data consumption or data discovery\u00a0<\/li>\n<\/ul>\n\n
      \n
    • Data quality management-\u00a0Data Lake\u00a0implementation allows options for setting data quality rules, data quality reporting and remediation\u00a0<\/li>\n
    • Meta data management \u2013\u00a0Data Lake\u00a0has mechanisms for data audits, data lineage checks, data lifecycle management and policy enforcement.\u00a0<\/li>\n
    • Data auditing-\u00a0Data Lake\u00a0provides options for complete data auditing and recording data transformation\u00a0from the perspective of risk and compliance. It helps\u00a0audit who\/how\/or when the data elements have been changed\u00a0<\/li>\n<\/ul>\n\n

      Flow of information in a data\u00a0lake<\/strong>\u00a0<\/p>\n\n

      There are multiple layers in this architecture\u00a0<\/p>\n\n

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      • Ingestion\u00a0Tier- This layer ingests data in various format\u00a0<\/li>\n
      • Storage\u00a0Tier- This layer stores the raw data\u00a0<\/li>\n
      • Insights\u00a0Tier- These\u00a0layers\u00a0provide\u00a0insights of the input data\u00a0<\/li>\n<\/ul>\n\n
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        • Distillation\u00a0Tier- This layer consumes data from storage and converts it into structured format for better analysis\u00a0<\/li>\n
        • Processing\u00a0Tier- This layer uses algorithms and processes user queries\u00a0<\/li>\n
        • Presentation layer- This layer presents the results and analysis\u00a0<\/li>\n<\/ul>\n\n

          Traditional data warehouse architecture<\/strong>\u00a0<\/p>\n\n

          It consists of three tiers\u00a0<\/p>\n\n

            \n
          • Ist\u00a0Tier (Bottom Tier)- It contains the database server which extracts data from data sources\u00a0<\/li>\n
          • 2nd Tier (ETL Tier or Middle Tier)- The data is extracted, transformed and loaded into the enterprise data warehouse and then into data marts.\u00a0\u00a0<\/li>\n
          • 3rd Tier (Client layer)- The data prepared for analysis is then thoroughly analysed by high level data analytic tools and presented as reports\u00a0<\/li>\n<\/ul>\n

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            Data\u00a0Lake Architecture\u00a0vs.\u00a0Traditional Datawarehouse Architecture\u00a0 Datalake\u00a0architecture\u00a0 Data\u00a0lake is a repository for storing huge volumes of structured, unstructured and semi-structured data. There is no limit on the file size or the format that can be stored.\u00a0\u00a0 Data\u00a0lake architectural components\u00a0 Data Ingestion- It contains connectors to extract data from multiple data sources\u00a0(databases, servers, emails etc) ,\u00a0in a variety […]<\/p>\n","protected":false},"author":3,"featured_media":12491,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[109],"tags":[],"class_list":["post-12135","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-transformation"],"_links":{"self":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts\/12135","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/comments?post=12135"}],"version-history":[{"count":3,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts\/12135\/revisions"}],"predecessor-version":[{"id":18918,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/posts\/12135\/revisions\/18918"}],"wp:attachment":[{"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/media?parent=12135"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/categories?post=12135"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ismiletechnologies.com\/en_us\/wp-json\/wp\/v2\/tags?post=12135"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}