Data applications and other data-centric workloads are no longer the Wild West of the enterprise system world. Gone are the days of rogue data scientists spinning up siloed environments with shadow IT to run experiments that may or may not have executive support. Now, smart companies like yours are integrating advanced analytics techniques and the modern data platforms that support them into corporate strategies at the highest level.
In this first part of my blog series, I explain what modern data analytics means for your data storage and management needs, your engineering staff, and your business. And I tell you how NetApp can help.
In pursuit of improving sales, operations, and product offerings, businesses across sectors are developing data-centric strategies. And it’s no wonder why, when IDC has predicted that by 2025, the volume of data created every year will be over 160ZB. With numbers like that, it’s easy to see that access to data isn’t enough. Modern data platforms must also fulfill three characteristics: self-service, agility, and flexible scaling.
Individual teams across functional groups within your company need to use your business data, without the gatekeeping access that your data scientists or business intelligence professionals have. The necessary level of access requires a platform that is not only accessible, but also intuitive to use. Your individual teams, whether they’re marketers, financial professionals, or part of the legal group, should be able to perform basic analysis, derive insights, and understand the context of the data that they have gathered.
Agility for groups across siloes
Software development has been agile for 2 decades. But legacy data management systems are complicated. Getting access, making changes, understanding context— simple functions become nearly impossible on old systems. A modern data platform focuses on two fundamental principles: availability and elasticity.
A modern data platform separates compute from storage. As a function of agility, flexibility in your infrastructure is key. As your projects grow, the data supporting them inevitably grows too. You need an infrastructure solution that’s robust enough for an enterprise system, but flexible enough to meet your data demands as they evolve. By using data lake technology, a modern data platform can store enormous amounts of data for a lower cost while being connected to the cloud.
NetApp provides innovative, industry-leading modern data analytics solution strategies that help you maximize business benefits, overcome data analytics challenges, keep pace with technology trends, and beat the competition. With NetApp® solutions, you can quickly move your legacy architecture to cloud and cost-effectively handle modern data analytics workflows, no matter what your industry or use cases are. We work with many valued technology partners, integrating our expertise with theirs to develop solutions that meet your specific needs.
With NetApp technology, it’s easy for you to manage and to move your big data, and the NetApp Active IQ® Digital Advisor tool helps you monitor and proactively improve system health, availability, and security. Your developers, data scientists, and data engineers can use NetApp DataOps Toolkit, a Python library, to clone a data volume or development workspace to provision a new one almost instantaneously. Our solutions also support big data architectures from Hadoop to Spark, and your software-defined storage
Whether your data analytics workloads require containers, enterprise data management, archiving, or tiering, NetApp has a solution to help. Our strategies promote simplicity, availability, high performance, and manageability of your data to meet the ever-increasing demands of modern AI and analytics workloads.
Start making the moves to a more agile, flexible, and self-service platform for your analytics workloads. To learn more, check out our hub page.
And be on the lookout for part two in this blog series to learn more about NetApp’s role in a modern data platform.
Mackinnon joined NetApp and the Solutions Marketing team in 2020. In her time, she has focused on Enterprise Applications and Virtualization, but uncovered a passion in Artificial Intelligence and Analytics. In her current role as a Marketing Specialist, Mackinnon strives to push messaging and solutions that focus on the intersection of authentic human experience and innovative technology. With a background that spans industries like Software Development, Fashion, and small business operations, Mackinnon approaches AI topics with a fresh, outsider perspective. Mackinnon holds a Masters of Business Administration from the Leeds School of Business at the University of Colorado, Boulder. She continues to live in Colorado with an often sleeping greyhound and a growing collection of empty Margaux bottles.