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Modern Analytics: Data Lakes, Data Warehouses, and Clouds

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12 B I G D ATA Q U A RT E R LY | S P R I N G 2 0 2 1 At the core of every digitAl trAnsformAtion effort is data analytics. Of course, this requires data, and lots of it. That's why cloud computing is such a critical resource to build and sustain such efforts. Nearly 80% of data managers surveyed by Database Trends and Applications currently have digital transformation initiatives underway, and 55% of these organizations are deploying business intelligence or data analytics with cloud-based support ("DBTA Digital Transformation and Cloud Workloads Survey," 2021). Cloud is a key foundation for these initiatives going forward. A separate survey conducted by Unisphere Research among PASS members found that 51% manage enterprise data in the cloud, and 16% planned to do so within a year's time ("DBAs Look to the Future: PASS Survey on Trends in Database Administration," January 2020). A majority, 52%, expected the volume of enterprise data in the cloud to grow significantly over the next 3 years. The move to the cloud is about attaining the needed speed, agility, and insight to navigate today's rapidly changing digital economy. As a result, enterprises are embracing strategies to accel- erate the movement of their data analytics capabilities to cloud platforms. Data warehouses and data lakes—once the exclusive domain of the world's larger enterprises—are now available and accessible to companies of all types and sizes. For the incoming generation of data warehouses and data lakes, there is a growing array of choices when it comes to cloud plat- forms, deployment models, and features. At the same time, chal- lenges remain. Data governance and security are still hot-button issues. Real-time data requirements are on the rise at many orga- nizations. And management and monitoring can be a concern whenever you add complexity onto an existing environment. For organizations with growing data warehouses and lakes, the cloud offers almost unlimited capacity and processing power. However, transitioning existing data environments from on- premise systems to cloud platforms can be challenging. Here are some key considerations for making the move: Plan ahead. Undertaking a migration from an on-premise data environment to a cloud-based data warehouse or data lake is not an overnight process. It can be costly, both in terms of the budget and resources required. The organization may have deep investments in a data warehouse going back decades, and there need to be assurances that all data structures—and the ability to query them as before—remain intact. Of course, a cloud-based architecture calls for redesigning the data model. Define the business benefits. Moving data analytics appli- cations to cloud environments typically involves "greenfield" projects that have a fresh start, and are geared toward delivering superior customer or user experiences through mobile apps or highly responsive web-based interfaces. However, especially in the case of data warehouses, existing on-premise systems may have been built up over the years, reflecting millions of dollars of investments and time—and business users may perceive that the system is highly effective and delivering the needed results In a 2019 survey of data managers conducted by Unisphere OPENING UP NEW FRONTIERS for DATA WAREHOUSES and DATA LAKES THROUGH the CLOUD

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