{"id":4311,"date":"2017-06-07T09:35:01","date_gmt":"2017-06-07T04:05:01","guid":{"rendered":"http:\/\/www.allerin.com\/blog\/?p=4311"},"modified":"2017-06-07T13:53:15","modified_gmt":"2017-06-07T08:23:15","slug":"designing-data-lakes-key-elements-to-consider","status":"publish","type":"post","link":"https:\/\/www.allerin.com\/blog\/designing-data-lakes-key-elements-to-consider\/","title":{"rendered":"Designing data lakes – key elements to consider"},"content":{"rendered":"

\"\"<\/a><\/p>\n

Customer requirements are constantly evolving, and along with advances in data storage technologies, the current enterprise data warehousing solutions are proving to be inadequate. Businesses dealing with advanced analytics require a data storage solution that is based on an IT \u201cpush\u201d model, which enables multiple and varied analytics use cases across the enterprise. This new solution is called a \u2018data lake\u2019, which supports multiple reporting tools in a self-serve capacity, and allows rapid ingestion of new datasets without extensive modeling. While designing a data lake you must allow users to cleanse and process the data iteratively and easily track the lineage of data for compliance. Moreover, it should also be able to incorporate technologies like machine learning and text analytics.<\/p>\n

<\/h6>\n

While designing a data lake, you must first completely understand its architecture. A data lake consists of a data-centered architecture that features a repository capable of storing vast quantities of data in various formats. Data from various sources such as web server logs, social media, databases, and third-party data is ingested into the data lake. In a data lake, curation occurs by capturing metadata and lineage, and making it available in the data catalog. Data flows into the data lake either in real-time processing or batch processing of streaming data.<\/p>\n

<\/h6>\n

After you properly understand the data lake\u2019s architecture, you should now know about the key elements that are necessary while designing a data lake.<\/p>\n

\"\"<\/a><\/h1>\n

<\/h1>\n

Domain specifications<\/h1>\n

The data lake should be tailored to the specific industry. For example, a data lake customized for\u00a0 retail industry would be significantly different from one tailored for the manufacturing industry. A data lake requires data-locating capability to enable business users find, explore, and understand the data. The search capability that you provide should facilitate an intuitive means for navigation, including graphical search.<\/p>\n

Designing a data lake with PoC<\/h1>\n

While designing a data lake you must create and execute Proofs-of-Concept (PoC) for demonstrating the viability of the design approach. Key capabilities of your data lake should be demonstrated using leading-edge bases and other selected tools.<\/p>\n

Operating model design and rollout<\/h1>\n

You should customize your operating models to meet the requirements of individual client\u2019s processes, organizational structure, governance, and rules. This includes establishing chargeback models, reporting mechanisms, and consumption tracking techniques.<\/p>\n

<\/h6>\n

Data lake is an effective data management solution<\/a> for advanced analytics experts and business users as it allows them to analyze a large variety and volume of data, when and how they want. Thus, for designing a successful data lake you must focus on implementing a multitude of products, while being relevant to the industry and providing users with extensive, scalable customization.<\/p>\n","protected":false},"excerpt":{"rendered":"

Customer requirements are constantly evolving, and along with advances in data storage technologies, the current enterprise data warehousing solutions are proving to be inadequate. Businesses dealing with advanced analytics require…<\/p>\n","protected":false},"author":1,"featured_media":4516,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[126,125],"tags":[350],"class_list":["post-4311","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data","category-iot","tag-designing-a-data-lake"],"yoast_head":"\nDesigning data lakes - key elements to consider | Big Data |<\/title>\n<meta name=\"description\" content=\"While designing a data lake you must allow users to cleanse and process the data iteratively and easily track the lineage of data for compliance\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.allerin.com\/blog\/designing-data-lakes-key-elements-to-consider\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Designing data lakes - key elements to consider\" \/>\n<meta property=\"og:description\" content=\"Customer requirements are constantly evolving, and along with advances in data storage technologies, the current enterprise data warehousing solutions are\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.allerin.com\/blog\/designing-data-lakes-key-elements-to-consider\/\" \/>\n<meta property=\"og:site_name\" content=\"Artificial Intelligence, ROBOTICS, AUTOMATION\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/allerintech\" \/>\n<meta property=\"article:published_time\" content=\"2017-06-07T04:05:01+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2017-06-07T08:23:15+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.allerin.com\/wp-blog\/wp-content\/uploads\/2017\/06\/data-lake-3.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"770\" \/>\n\t<meta property=\"og:image:height\" content=\"513\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Naveen Joshi\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@joshinav\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Naveen Joshi\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/designing-data-lakes-key-elements-to-consider\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/designing-data-lakes-key-elements-to-consider\\\/\"},\"author\":{\"name\":\"Naveen Joshi\",\"@id\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/#\\\/schema\\\/person\\\/d01e43be365179dae3ff572b2007c64e\"},\"headline\":\"Designing data lakes – key elements to consider\",\"datePublished\":\"2017-06-07T04:05:01+00:00\",\"dateModified\":\"2017-06-07T08:23:15+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/designing-data-lakes-key-elements-to-consider\\\/\"},\"wordCount\":447,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/designing-data-lakes-key-elements-to-consider\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/wp-content\\\/uploads\\\/2017\\\/06\\\/data-lake-3.jpg\",\"keywords\":[\"Designing a data lake\"],\"articleSection\":[\"Big Data\",\"IOT\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.allerin.com\\\/blog\\\/designing-data-lakes-key-elements-to-consider\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/designing-data-lakes-key-elements-to-consider\\\/\",\"url\":\"https:\\\/\\\/www.allerin.com\\\/blog\\\/designing-data-lakes-key-elements-to-consider\\\/\",\"name\":\"Designing data lakes - 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