As enterprises continue to stockpile massive amounts of information generated by people, businesses, vehicles, and a virtually endless list of other sources, many are wondering where they can store all of that data accessibly, safely, securely, and cost effectively.
The data storage business has changed significantly over the last five years and that transformation is continuing and broadening. The big difference today is that while storage used to be about hardware-related issues, such as solid-state drives, faster read/write speeds, and capacity expansion, the cloud and other storage breakthroughs have flipped the market to the opposite side.
"For most organizations, storage is more about software, including software-defined storage, software managing virtualization, and integrating AI and ML to improve storage optimization,” said Scott Golden a managing director in the enterprise data and analytics practice at global business and technology consulting firm Protiviti.
Here's a quick rundown of five promising storage technologies that can now, or at some point in the foreseeable future, help enterprises cope with growing data storage needs.
1. Data lakes
When it comes to handling and getting value from large data sets, most customers still start with data lakes, but they leverage cloud services and software solutions to get more value from their lakes, Golden said. "Data lakes, like Azure ADL and Amazon’s S3, provide the ability to gather large volumes of structured, semi-structured, and unstructured data and store them in Blobs (Binary Large OBjects] or parquet files for easy retrieval."
2. Data virtualization
Data virtualization allows users to query data across many systems without being forced to copy and replicate data. It also can simplify analytics, make them timelier and more accurate, since users are always querying the latest data at its source. "This means that the data only needs to be stored once, and different views of the data for transactions, analytics, etcetera, ... versus copying and restructuring the data for each use," explained David Linthicum, chief cloud strategy officer at business and technology advisor Deloitte Consulting.
Data virtualization has been around for some time, but with increasing data usage, complexity, and redundancy, the approach is gaining increasing traction. On the downside, data virtualization can be a performance drag if the abstractions, or data mappings, are too complex, requiring extra processing, Linthicum noted. There's also a longer learning curve for developers, often requiring more training.
3. Hyper-converged storage
While not exactly a cutting-edge technology, hyper-converged storage is also being adopted by a growing number of organizations. The technology typically arrives as a component within a hyper-converged infrastructure in which storage is combined with computing and networking in a single system, explained Yan Huang, an assistant professor of business technologies at Carnegie Mellon University's Tepper School of Business.
Huang noted that hyper-converged storage streamlines and simplifies data storage, as well as the processing of the stored data. "It also allows independently scaling computing and storage capacity in a disaggregated way," she said. Another big plus is that enterprises can create a hyper-converged storage solution using the increasingly popular NVMe over Fabrics (NVMe oF) network protocol. "Due to the pandemic, remote working became the new normal," Huang said. "As some organizations make part of their workforce remote permanently, hyper-converged storage is attractive because it is well-suited for remote work."
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