1/6/2024 0 Comments Gartner hype cycle 2021![]() If you build that kind of data lake today, it will fail. If your organization is implementing a data lake today, make sure it follows modern standards.Īs the Gartner report describes, “The first data lakes were built on Hadoop, for data science only, and they lacked metadata, relational functionality and governance. Best practices are still changing and developing. That being said, challenges will continue to present themselves. Data warehouses will continue to be relevant, but only when modernized.Data exploration has become a common practice.There is an increasing demand for the expansion of analytics programs. ![]() Organizations continue to be driven by data and analytics.We believe, according to the analysis by Phillip Russom and Henry Cook in the Gartner Hype Cycle for Data Management, 2021, the top drivers for success with data lakes are: And as the Hype Cycle graphic shows, they’re getting ready to enter a new phase of maturity in the next year: the Slope of Enlightenment. Data lakes complement data warehouses with an open philosophy, offering schema-on-read, loosely coupled storage/compute and flexible use cases that combine to drive innovation by reducing the time, cost, and complexity of data management.Īs a result, data lakes are seeing renewed excitement and successful implementations. Today, data lakes are built on cloud object storage and can be activated to support multi-dimensional analytics use cases such as full text search, relational queries, and machine learning. To activate the data lake and deliver on its promise, modern approaches are driving this market into maturity. Without any organization, governance, or integration with known ETL or analytics tools, the data lake often became a “data swamp” where data would sit stagnant because users didn’t know how to effectively access or glean insights from it.Īll the while, the macro problem has persisted: how to access and analyze multiple data types without constraints inherent to storage and infrastructure.Īs a result, there’s been tremendous innovation around solving this problem since those first implementations built on Hadoop. Organizations were dumping large volumes of raw data into their lake but struggled to give end users access to the datasets they needed, or to integrate known front-end tools. This initial concept sounded great in theory - enter the “Innovation Trigger” stage of the Hype Cycle - but struggled to deliver real value in practice. The first data lakes were built on Apache Hadoop, which allowed users to store unstructured and multi-structured datasets at scale, and run application workloads on clusters of on-premise commodity hardware. What have been the game-changing factors allowing data lakes to persist past the plateau? Is it possible to optimize such a vast and seemingly chaotic storage platform variety? Read on for our take on how we’ve gotten here, or download your copy of the latest Hype Cycle for Data Management here. Source: Gartner Hype Cycle for Data Management, 2021, Philip Russom, Donald Feinberg, 27 th July 2021. This year’s Hype Cycle for Data Management report was just released, revealing that modern data lakes are poised to exit the Trough of Disillusionment and enter the Slope of Enlightenment in 2022. To some, that roller coaster ride might resemble the canonical Hype Cycle graphic, trademarked by Gartner to show the maturity curve of technologies in a given category over time. IT leaders’ experiences with data lakes have been a roller coaster ride since their inception in 2010. By Courtney Pallotta, VP Marketing, ChaosSearch on Sep 9, 2021
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