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As 2019 winds down, 2020 is winding up all the way to edge – edge computing of course – the next frontier in the enterprise storage industry. As data storage balloons to the exabyte level for many enterprises, ways of managing storage are going to change drastically in 2020. Here’s how edge computing as well as managing AI and machine learning workloads, not to mention the proliferation of the private clouds, will affect organizations over the next year:
Turning the Hybrid Cloud Storage Model on its Side: Hello Edge Computing!
Hybrid cloud storage involves keeping data both on-premises and in the public cloud and having the ability to seamlessly move data across these environments. In 2020, the hybrid storage strategy will be turned on its side as edge computing becomes the next frontier. The Internet of Things (IoT) and 5G will drive demand for AI applications processing and data collection at the edge – e.g., traffic signals, surveillance cameras, and smart cars. In turn, there will be an increasing need for a different form of hybrid storage – one that will require more robust storage near the edge and the ability to move analyzed data sets between the edge and on-prem or public cloud systems.
Therefore, applying analytics near where data is created (e.g., where sensors are located) will require a different approach to storage.
Object Storage will be Key to Processing AI and ML Workloads
As data volumes continue to explode, one of the key challenges is how to get the full strategic value of this data. In 2020, we will see a growing number of organizations capitalizing on object storage to create structured/tagged data from unstructured data, allowing metadata to be used to make sense of the tsunami of data generated by AI and ML workloads.
While traditional file storage defines data with limited metadata tags (file name, date created, date last modified, etc.) and organizes it into different folders, object storage defines data with unconstrained types of metadata and locates it all from a single API, searchable and easy to analyze. For example, a traditional X-ray file would only have metadata describing basics like creation date, owner, location, and size. An X-ray object, on the other hand, could use metadata that identifies patient name, age, injury details, and which area of the body was X-rayed. Furthermore, the file could be analyzed using AI/ML, and the findings written to the metadata. This combination both enhances the value of the data and makes it easier to search.
In 2020,object storage will be instrumental in helping to process AI and ML workloads, as this newer storage architecture leverages metadata in ways traditional file storage doesn’t.
The Rise of the Hybrid Cloud Infrastructure: Putting the Right Data in the Right Place
When people refer to the cloud today, they usually mean the public cloud. In 2020, the term “cloud” might become more nuanced as private clouds rise in popularity and organizations increasingly pursue a hybrid cloud storage strategy. Organizations with large-scale storage needs – such as those in healthcare, scientific research, and media and entertainment – face unique challenges in managing capacity-intensive workloads that can reach tens of petabytes. Private clouds address these challenges by providing the scale and flexibility benefits of public clouds along with the performance, access, security, and control advantages of on-premises storage.
While the public cloud has served myriad of purposes – providing agility and convenience while decreasing infrastructure expenses for many organizations – the bandwidth and accessibility costs in public clouds can be significant. Given that, organizations such as Bank of America are taking a new approach. It was recently reported that Bank of America was able to save up to $2 billion per year bypassing traditional public clouds and going private.
In 2020, we’ll see more organizations taking advantage of private clouds in a hybrid cloud infrastructure – storing frequently used data on-prem while continuing to utilize the public cloud for disaster recovery, for data analysis, and for transient workloads.