{"id":5239,"date":"2023-03-30T16:01:32","date_gmt":"2023-03-30T20:01:32","guid":{"rendered":"https:\/\/solutionsreview.com\/data-management\/?p=5239"},"modified":"2023-03-30T16:05:21","modified_gmt":"2023-03-30T20:05:21","slug":"gartner-da-summit-2023-the-gartner-view-of-the-data-lake-lakehouse","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/data-management\/gartner-da-summit-2023-the-gartner-view-of-the-data-lake-lakehouse\/","title":{"rendered":"Gartner D&A Summit 2023: The Gartner View of the Data Lake & Lakehouse"},"content":{"rendered":"

\"Data<\/p>\n

Independent analyst for data and analytics Philip Russom PhD. offers commentary on the Gartner view of data lake and lakehouse from the recent Gartner Data & Analytics Summit 2023.<\/strong><\/em><\/p>\n

\"\"When the Gartner Data & Analytics Summit met recently (March 19-22, 2023 in Orlando, Florida), I was there attending sessions presented by Gartner analysts, vendor sponsors, and other experts. The summit covered most aspects of data and analytics (D&A), plus their best practices, tools, technologies, and team structures. However, a topic that came up in multiple sessions was the data lake and its variation: the data lakehouse. Allow me to summarize the Gartner View of data lakes and lakehouses, as presented at the Gartner D&A Summit 2023.<\/p>\n

Background for Data Lakes and Lakehouses<\/strong><\/h4>\n

Data Lakes and Data Lakehouses are drivers of innovation for databases and storage. For example, many D&A programs deploy a data warehouse and a data lake side-by-side, because they are complementary. The warehouse is mostly relational data for business reporting and keeping a history of corporate performance, whereas the lake is mostly about data science and advanced analytics, using data of any structure or file format. The Gartner view is that the warehouse and lake are now converging into the data lakehouse, which is a single data architecture that combines and unifies the architectures and capabilities of lakes and warehouses. The point of the data lakehouse is to enable greater agility for all analytics, but with less data redundancy, a simpler architecture, and a more consistent view of semantics for all analytics data.<\/p>\n

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Data Lake Gartner<\/strong><\/h2>\n
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Avoiding Data Lake Failures<\/strong><\/h3>\n

Over the fifteen years of the data lake\u2019s existence, Gartner analysts have seen many failures of data lake implementations among its user clients and other end-user organizations. Much-needed guidance for users hoping to succeed with a data lake was offered in the summit session \u201cAvoid Data Lake Failures by Addressing Modern Lake Requirements,\u201d presented by Donald Feinberg, a Distinguished VP Analyst at Gartner Inc.<\/p>\n

The primary premise of the presentation is that organizations fail with a data lake when they build it the way early lakes were designed and managed. Instead, organizations need to understand the business requirements and use the available technologies that are appropriate to today\u2019s data lakes.<\/p>\n

For example, the earliest data lakes were simple and raw, because most were built for data scientists who had minimal data requirements for data. Most early lakes had little or no support for:<\/p>\n