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Snowflake&body=${pageURL}`;\r\nwindow.open(shareURL);\r\n}\r\n\r\n\r\n\r\n\r\n\r\n});",":type":"snowflake-site/components/markup-editor","isGSAPEnabled":false}},":itemsOrder":["markup_editor"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"}},":itemsOrder":["container_949147658"]},"cq:LiveSyncConfig":{"cq:isDeep":true,"cq:rolloutConfigs":[],"cq:master":"/content/experience-fragments/snowflake-site/language-masters/en/site/share-icons/share-icons",":type":"cq:LiveCopy"}},":itemsOrder":["root","cq:LiveSyncConfig"],"classNames":"aem-xf","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"}},":itemsOrder":["text","experiencefragment"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-small"},"flexible_column_content_container_2":{"id":"fundamentals-main-content","layout":"SIMPLE",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"container":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"overview","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-911bad2621","type":"heading2","lines":["Overview"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-4ad9524655","text":"\u003Cp\u003EAs organizations navigate an increasingly data-driven world, the need for flexible, scalable and cost-effective data storage and analytics solutions has never been greater. Data lakes have emerged as a cornerstone of modern data infrastructure, designed to handle the volume, variety and velocity of today’s data.\u003C/p\u003E\r\n\u003Cp\u003EThis article explores not only what a data lake is, but also delves into the underlying architecture that makes it work effectively at scale.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"data","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-3a38e3bfef","additionalClasses":"headline-decoration","type":"heading2","lines":["What Is a Data Lake?"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-10bc83e615","text":"\u003Cp\u003EA data lake is a centralized repository that allows organizations to store all types of data — structured, semi-structured and unstructured — in its raw format. Unlike traditional databases or data warehouses, data lakes do not require predefined schemas or formatting before storage, making them highly flexible and agile.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-f84d392bc0","type":"heading4","lines":["Key characteristics:"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text_copy":{"id":"text-f94d9ffcc7","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ESchema-on-read: \u003C/b\u003EData is interpreted when it’s accessed, not when it’s ingested\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESupports diverse data types:\u003C/b\u003E From CSV files and JSON logs to images and video\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESupports multiple languages: \u003C/b\u003ESuch as SQL, Spark and more\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EScalable and cost efficient: \u003C/b\u003EBuilt on inexpensive object storage, often in the cloud\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDesigned for a broad range of users:\u003C/b\u003E From data analysts to data scientists and engineers\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDesigned for the full data lifecycle: \u003C/b\u003EFrom ingestion and storage, processing and transformation to advanced analytics and AI\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text","title_v2_copy_copy_c","text_copy"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"is","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-7c62cf8d7f","additionalClasses":"headline-decoration","type":"heading2","lines":["Data Lake vs. Data Warehouse"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-707d66539b","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EA \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-lake-vs-data-warehouse-vs-data-mart/\"\u003Edata lake and a data warehouse\u003C/a\u003E are both used for storing and analyzing data, but they differ in important ways.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EData structure:\u003C/b\u003E A data lake stores raw, unprocessed data without a predefined structure. It uses a "schema-on-read" approach, where data is organized only when it's accessed for analysis. A \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-warehouse-architecture-and-design/\"\u003Edata warehouse\u003C/a\u003E requires data to be cleaned and transformed to fit a predefined schema before storage, a process called "schema-on-write."\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EData types: \u003C/b\u003EA data lake can accommodate all data types, including unstructured, semi-structured and structured data. Traditional data warehouses store primarily structured data that is ready for analysis.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EUsers and purpose:\u003C/b\u003E Data warehouses are often optimized for business intelligence (BI) and reporting and are used by business analysts. Data lakes are often used by data scientists and data engineers for advanced analytics and machine learning on a wider variety of data types.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EHybrid approach:\u003C/b\u003E Many organizations combine both systems in a "lakehouse" architecture to get the flexibility of a data lake with the performance of a data warehouse.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy__1697459514":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12"},"id":"air","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-5c28d3b639","additionalClasses":"headline-decoration","type":"heading2","lines":["Data Lake Benefits"],":type":"snowflake-site/components/title-v2"},"text":{"id":"text-2f1b56be37","additionalClasses":"list--blue-bullets","text":"\u003Cp\u003EA data lake offers many advantages for organizations looking to manage and analyze modern data at scale.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ECost-effective storage: \u003C/b\u003EData lakes are built on inexpensive object storage, often in the cloud, resulting in lower storage costs compared to traditional systems. This makes it cost-effective to store vast amounts of data without a predefined structure.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EScalability for big data: \u003C/b\u003EData lakes are designed to handle the volume and variety of today's data and can support petabytes of data effortlessly. They are highly scalable and can grow with your business needs.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EFlexibility for multiple data types: \u003C/b\u003EA data lake can ingest any type of data, from any source, at any speed, whether it is \u003Ca href=\"https://www.snowflake.com/en/fundamentals/understanding-structured-semi-structured-and-unstructured-data/\"\u003Estructured, semi-structured or unstructured\u003C/a\u003E. This "schema-on-read" approach allows for maximum flexibility for data exploration.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAdvanced analytics and AI readiness:\u003C/b\u003E A data lake serves as a flexible foundation for a wide range of analytical workloads. It supports advanced analytics, machine learning and real-time analytics. The ability to provide raw data at large volumes is essential for training AI and machine learning models.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_1767623498":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_1241045613":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"what","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-cb20cf1185","additionalClasses":"headline-decoration","type":"heading2","lines":["What Is Data Lake Architecture?"],":type":"snowflake-site/components/title-v2"},"text_copy_1241045613":{"id":"text-101d835a4d","text":"\u003Cp\u003EWhile a data lake is the concept, data lake architecture refers to the underlying structure and components that enable a data lake to function efficiently. It’s a layered system designed to manage the ingestion, storage, processing, discovery, governance and consumption of large-scale data sets.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-ab286ab9ae","type":"heading4","lines":["Core components of data lake architecture:"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"left-alignment"},"text":{"id":"text-ab057fb311","text":"\u003Ctable\u003E\r\n\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003ELayer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EFunctionality\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EIngestion layer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EPulls in data from various sources (streaming, batch, IoT, APIs)\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EStorage layer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EStores raw data in scalable object storage\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EMetadata and cataloging layer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EIndexes, tags and organizes data for discoverability\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EProcessing layer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EHandles \u003Ca href=\"https://www.snowflake.com/en/fundamentals/unlocking-value-through-data-transformation-in-modern-pipelines/\"\u003Edata transformation\u003C/a\u003E via batch/real-time processing tools\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EAccess layer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EEnables querying, exploration and analytics\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003E\u003Cp\u003E\u003Cb\u003EGovernance and security layer\u003C/b\u003E\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003Ctd\u003E\u003Cp\u003EManages data privacy, access controls, auditing and compliance\u003C/p\u003E\r\n\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/text"}},":itemsOrder":["title_v2","text_copy_1241045613","title_v2_copy_copy_c","text"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"id":"rel","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1520074195":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1202161416":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1835740287":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__746839706":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c_1923870193":"aem-GridColumn aem-GridColumn--default--12","text_copy_copy_copy_":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1477902138":"aem-GridColumn aem-GridColumn--default--12","title_v2_copy_copy_c":"aem-GridColumn aem-GridColumn--default--12"},"id":"type","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-4bb3c8619a","type":"heading2","lines":["Supported Data Types"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-13cd6c40b8","text":"\u003Cp\u003EA data lake is designed to store vast amounts of data in its native, raw format and, therefore, supports a wide variety of data types. These can be broadly categorized into three main types:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c":{"id":"title-v2-367760c7da","type":"heading4","lines":["Structured data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1835740287":{"id":"text-6277dc6a13","text":"\u003Cp\u003EThis type of data is highly organized and fits neatly into rows and columns, much like a relational database or a spreadsheet. It has a predefined schema, making it easy to search, analyze and manage. Examples of structured data types include:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy":{"id":"text-c493cb19f8","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ERelational database tables: \u003C/b\u003EData organized with fixed columns and rows\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESpreadsheets (e.g., CSV files):\u003C/b\u003E Tabular data with defined columns\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENumerical data:\u003C/b\u003E Integers, floating-point numbers, decimals\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECategorical data:\u003C/b\u003E Labels or categories with a limited number of values\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EDate and time data:\u003C/b\u003E Timestamps, dates and time values\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1923870193":{"id":"title-v2-9c506fd6fa","type":"heading4","lines":["Semi-structured data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__1477902138":{"id":"text-6ad56ef434","text":"\u003Cp\u003EThis data does not conform to a rigid tabular structure but has some organizational properties, making it easier to analyze than unstructured data. It often contains tags or markers that separate semantic elements and enforce hierarchies. Examples include:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy":{"id":"text-9de0681fd9","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EJSON (JavaScript Object Notation): \u003C/b\u003EA lightweight format using key-value pairs and nested objects\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EXML (extensible markup language): \u003C/b\u003EA markup language that defines a set of rules for encoding documents in a human-readable and machine-readable format\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ECSV (comma separated values) with complex structures: \u003C/b\u003EWhile basic CSV is structured, it can become semi-structured with varying numbers of columns or nested data within fields\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ELog files: \u003C/b\u003EOften contain timestamps, event types and messages, which can be parsed\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENoSQL databases:\u003C/b\u003E Documents or key-value stores where the schema can vary between entries\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EHTML (hypertext markup language):\u003C/b\u003E While primarily for web pages, it contains structured elements and data\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EYAML (YAML Ain't Markup Language™):\u003C/b\u003E A human-friendly data serialization standard for all programming languages\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"title_v2_copy_copy_c_1520074195":{"id":"title-v2-30861a702a","type":"heading4","lines":["Unstructured data"],":type":"snowflake-site/components/title-v2","appliedCssClassNames":"snowflake-responsive-component-top-padding-extra-small"},"text_copy_860073138__746839706":{"id":"text-91d5cb1351","text":"\u003Cp\u003EThis data does not have a predefined format or organization, making it challenging to analyze using traditional methods. It often requires specialized tools and techniques like natural language processing or machine learning to extract insights. Examples of unstructured data types include:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_copy_copy_":{"id":"text-4309b07239","additionalClasses":"list--blue-bullets","text":"\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003EText files:\u003C/b\u003E Documents (.txt, .doc, .pdf), emails, social media posts\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EImage files:\u003C/b\u003E JPEG, PNG, GIF\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EAudio files: \u003C/b\u003EMP3, WAV\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EVideo files:\u003C/b\u003E MP4, AVI, MOV\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ESensor data:\u003C/b\u003E Streams of data from IoT devices that may not have a consistent structure\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003EBinary files:\u003C/b\u003E Executable files, proprietary data formats\u003C/li\u003E\r\n\u003C/ul\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1202161416":{"id":"text-b1de437cc7","text":"\u003Cp\u003EThe ability to store and process all these diverse data types in their native format is a key characteristic and advantage of a data lake. This "schema-on-read" approach allows for flexibility and enables data scientists and analysts to explore and analyze data in various ways without the constraints of a predefined structure.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"}},":itemsOrder":["title_v2","text_copy_860073138_","title_v2_copy_copy_c","text_copy_860073138__1835740287","text_copy_copy","title_v2_copy_copy_c_1923870193","text_copy_860073138__1477902138","text_copy_copy_copy","title_v2_copy_copy_c_1520074195","text_copy_860073138__746839706","text_copy_copy_copy_","text_copy_860073138__1202161416"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"}},":itemsOrder":["container_copy_copy_"],"appliedCssClassNames":"snowflake-responsive-container-inner-padding-extra-small"},"container_981691511":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"flexible_column_cont":"aem-GridColumn aem-GridColumn--default--12","container_copy_copy_":"aem-GridColumn aem-GridColumn--default--12"},"id":"container-9eb24dfb72","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"container_copy_copy_":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"text_copy_860073138_":"aem-GridColumn aem-GridColumn--default--12","title_v2":"aem-GridColumn aem-GridColumn--default--12","text_copy_860073138__1340528379":"aem-GridColumn aem-GridColumn--default--12"},"id":"life","layout":"RESPONSIVE_GRID","columnCount":12,":type":"snowflake-site/components/container",":items":{"title_v2":{"id":"title-v2-f58e1fc085","type":"heading2","lines":["Data Lifecycle"],":type":"snowflake-site/components/title-v2"},"text_copy_860073138_":{"id":"text-a2d85171cb","text":"\u003Cp\u003EWithin a data lake, the data lifecycle describes the stages data goes through from its initial creation or acquisition to its eventual archival or deletion. It's a continuous process that helps ensure data is effectively managed, utilized and governed throughout its existence within the lake. Here's a typical overview of the data lifecycle in a data lake, keeping in mind that specific implementations can vary:\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/text","appliedCssClassNames":"text-color-text-05"},"text_copy_860073138__1340528379":{"id":"text-04a719c57b","text":"\u003Cp\u003E\u003Cb\u003E1. Data ingestion: \u003C/b\u003EThis is the initial stage where data from various source systems is brought into the data lake. These sources can be diverse, including structured databases, semi-structured logs, unstructured documents, streaming data from IoT devices, social media feeds and more. The key characteristic of ingestion into a data lake is often "ingest as-is," meaning data is typically loaded in its raw, native format without significant upfront transformation or schema definition. This allows for maximum flexibility for future analysis. Tools and processes used in this stage include batch loading, real-time streaming ingestion and data connectors.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E2. Data storage and persistence:\u003C/b\u003E Once ingested, the raw data is stored within the data lake. The architecture often utilizes distributed storage systems like Hadoop Distributed File System (HDFS) or cloud-based object storage (e.g., Amazon S3, Azure Data Lake Storage, Google Cloud Storage). The data remains in its original format, allowing for diverse analytical approaches later. The scalability and cost-effectiveness of the storage layer are crucial for handling the potentially vast volumes of data in a data lake. Different storage tiers might be employed based on data access frequency and retention policies.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E3. Data processing and transformation: \u003C/b\u003EThis stage involves preparing the raw data for analysis and consumption. Depending on the specific analytical use case, data might undergo various transformations, including cleaning, filtering, joining, aggregating and enriching. This is where "schema-on-read" comes into play — the schema is applied when the data is being processed for a specific purpose, rather than at the time of ingestion. Various processing engines and frameworks are used in this stage, such as Spark, Hadoop MapReduce, data warehousing tools connected to the lake, and serverless compute services.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E4. Data exploration and analysis: \u003C/b\u003EThis is where data scientists, analysts and business users explore the processed data to discover patterns, gain insights and answer business questions. They might use a variety of tools and techniques, including SQL-like queries, data visualization tools, statistical analysis packages and machine learning algorithms. The flexibility of the data lake allows for diverse analytical approaches on the same data, depending on the specific needs.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E5. Data consumption and action: \u003C/b\u003EThe insights and processed data are then consumed by various downstream applications and users. This could involve generating reports and dashboards, feeding data into operational systems, powering real-time applications or informing business decisions. The consumed data might be in various formats and accessed through different interfaces, depending on the consuming application.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E6. Data governance and security:\u003C/b\u003E Throughout the entire lifecycle, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-governance/\"\u003Edata governance\u003C/a\u003E and security are critical. This includes defining and enforcing policies related to \u003Ca href=\"https://www.snowflake.com/en/fundamentals/a-practical-guide-to-data-quality/\"\u003Edata quality\u003C/a\u003E, metadata management, data lineage, access control, \u003Ca href=\"https://www.snowflake.com/en/fundamentals/data-masking/\"\u003Edata masking\u003C/a\u003E and compliance with regulations. Effective governance helps ensure that the data within the lake is trustworthy, secure and properly managed.\u003C/p\u003E\r\n\u003Cp\u003E\u003Cb\u003E7. Data archival and purging: \u003C/b\u003EAs data ages and its business value diminishes, it may need to be archived for compliance reasons or to optimize storage costs. Eventually, data that is no longer needed may be purged according to defined retention policies. This final stage keeps the data lake efficient and compliant over time.\u003C/p\u003E\r\n\u003Cp\u003EIn essence, the data lifecycle in a data lake is designed to be flexible and adaptable, allowing organizations to ingest vast amounts of diverse data, process it as needed for specific analytical use cases, and extract valuable insights while maintaining proper governance and security. 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