{"id":47014,"date":"2023-03-09T13:40:48","date_gmt":"2023-03-09T13:40:48","guid":{"rendered":"https:\/\/www.arimetrics.com\/glosario-digital\/data-lake"},"modified":"2026-10-01T11:50:38","modified_gmt":"2026-10-01T11:50:38","slug":"data-lake","status":"publish","type":"encyclopedia","link":"https:\/\/www.arimetrics.com\/en\/digital-glossary\/data-lake","title":{"rendered":"Data Lake"},"content":{"rendered":"<p><img decoding=\"async\" class=\"boxpad alignright wp-image-46695 size-full\" src=\"https:\/\/www.arimetrics.com\/wp-content\/uploads\/2023\/03\/data_lake.jpg\" alt=\"Data Lake\" width=\"300\" height=\"300\" srcset=\"https:\/\/www.arimetrics.com\/wp-content\/uploads\/2023\/03\/data_lake.jpg 300w, https:\/\/www.arimetrics.com\/wp-content\/uploads\/2023\/03\/data_lake-150x150.jpg 150w\" sizes=\"(max-width: 300px) 100vw, 300px\" \/> <strong>Definition:<\/strong><\/p>\n<p>A <strong>data lake<\/strong> is a repository designed to retain large volumes of data in its original format or with minimal transformation. It can bring together structured, semi-structured, and unstructured data from applications, databases, sensors, logs, documents, images, and other sources.<\/p>\n<p>Its function is not limited to storing files. To make the data discoverable, understandable, and usable, the repository needs <strong>metadata and cataloging<\/strong>, access controls, quality rules, and engines capable of processing it. Many current implementations use <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/cloud-storage\">cloud storage<\/a>, although the concept does not require the entire architecture to be cloud-based.<\/p>\n\n<h2>How a data lake works<\/h2>\n<p>The architecture separates <strong>storage<\/strong> from the systems that transform, query, or analyze the information. Different workloads can therefore use the same data without requiring a single structure for every use to be defined from the outset.<\/p>\n<p>A typical architecture includes these <strong>functional layers<\/strong>:<\/p>\n<ul>\n<li><strong>Ingestion:<\/strong> Receives batch or real-time data from internal and external systems.<\/li>\n<li><strong>Storage:<\/strong> Retains objects and files with identifiers, versions, and lifecycle policies.<\/li>\n<li><strong>Catalog:<\/strong> Records schemas, origin, owner, meaning, and other metadata to support discovery.<\/li>\n<li><strong>Processing:<\/strong> Cleans, combines, or transforms data through engines independent of storage.<\/li>\n<li><strong>Access:<\/strong> Provides query interfaces, APIs, and connections for analytics tools.<\/li>\n<\/ul>\n<p>In many data lakes, the structure required for a query is applied when the data is read. This approach, known as <strong>schema-on-read<\/strong>, provides flexibility, but it does not mean that the content lacks a schema or controls. Formats, partitions, and metadata still affect quality and performance.<\/p>\n<h2>Data and uses<\/h2>\n<p>A data lake can retain information with different <strong>levels of structure<\/strong>. Structured data includes application and transaction tables; semi-structured data covers formats such as JSON, XML, and event logs; unstructured data may include free text, audio, video, or images.<\/p>\n<p>This breadth is useful when an organization needs to explore data before choosing a final model, combine <strong>heterogeneous sources<\/strong>, or retain history for new analyses. It can support <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/big-data\">big data<\/a> processing, log analysis, research, anomaly detection, and <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/machine-learning\">machine learning<\/a> projects.<\/p>\n<p>Retaining the original data makes it possible to process it again under different rules, but it does not require keeping everything indefinitely. Source selection, retention, and cost should respond to a <strong>defined need<\/strong> and comply with applicable legal and operational constraints.<\/p>\n<h2>Data lakes and data warehouses<\/h2>\n<p>A <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/data-warehouse\">data warehouse<\/a> brings together integrated data prepared for <strong>known queries<\/strong>, reports, and metrics. It generally applies models and controls before making information available to users. A data lake prioritizes the flexible retention of varied data and may perform part of the transformation when the data is used.<\/p>\n<p>The difference is not limited to structured versus unstructured data, and it does not mean that one system always replaces the other. A warehouse can incorporate semi-structured data, and a lake can contain curated datasets. The main distinction lies in the <strong>degree of preparation<\/strong>, the access model, and the workloads for which each repository is designed.<\/p>\n<p>Both can form part of the <strong>same architecture<\/strong>. The data lake may receive and retain information from multiple sources, while downstream processes supply models optimized for business intelligence, dashboards, or recurring analyses.<\/p>\n<h2>Benefits and limitations<\/h2>\n<p>The usefulness of a data lake depends on its architecture and management. Its <strong>potential benefits<\/strong> include the following:<\/p>\n<ul>\n<li><strong>Flexibility:<\/strong> Supports different sources and formats without requiring a single model in advance.<\/li>\n<li><strong>Scalability:<\/strong> Can expand storage and processing independently, depending on the platform used.<\/li>\n<li><strong>Reuse:<\/strong> Allows the same dataset to support later analyses and transformations.<\/li>\n<li><strong>Traceability:<\/strong> Can retain original versions for comparison with processed data.<\/li>\n<\/ul>\n<p>These benefits are not automatic. A query over disorganized data may be slower and more complex than one over a prepared model. Costs also rise when files are duplicated, inefficient jobs are run, or retention policies are absent. The <strong>separation of resources<\/strong> provides room for adjustment, but it requires observability and control.<\/p>\n<h2>Data governance<\/h2>\n<p>Without a catalog, assigned owners, and quality rules, the repository can become a <strong>data swamp<\/strong>, a collection of data that is difficult to find, interpret, or trust. Governance should record origin and transformations, identify duplicates, document schemas, and establish who can access each asset.<\/p>\n<p>Security includes encryption, identity management, <strong>least-privilege<\/strong> permissions, auditing, and the separation of sensitive information. It must also cover classification, retention, and deletion, because storing raw data does not remove privacy obligations or guarantee its integrity.<\/p>\n<p>A <a href=\"https:\/\/www.arimetrics.com\/en\/digital-glossary\/data-strategy\">data strategy<\/a> connects these measures with their intended uses. When cataloging, quality, security, and lifecycle are managed together, the data lake operates as a <strong>reusable platform<\/strong> rather than a simple file repository.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Definition: A data lake is a repository designed to retain large volumes of data in its original format or with minimal transformation. It can bring together structured, semi-structured, and unstructured data from applications, databases, sensors, logs, documents, images, and other sources. Its function is not limited to storing files. To make the data discoverable, understandable, [&hellip;]<\/p>\n","protected":false},"author":28,"featured_media":0,"template":"","encyclopedia-tag":[305],"class_list":["post-47014","encyclopedia","type-encyclopedia","status-publish","hentry","encyclopedia-tag-data-en"],"_links":{"self":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia\/47014","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia"}],"about":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/types\/encyclopedia"}],"author":[{"embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/users\/28"}],"wp:attachment":[{"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/media?parent=47014"}],"wp:term":[{"taxonomy":"encyclopedia-tag","embeddable":true,"href":"https:\/\/www.arimetrics.com\/en\/wp-json\/wp\/v2\/encyclopedia-tag?post=47014"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}