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A Look into Evolution of Data Storage

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By Sahil Chawla, CEO & Co-Founder, Tsecond

There are many factors driving data storage evolution. The first is digital creation. Whether it’s media, industrial 4.0, IoT, or the digital infrastructure of companies, these are the catalysts generating huge amounts of data worldwide.

Consider how we use machines for work and play in our households. Pre-pandemic, majority of the workforce were in offices with all the connecting devices in one place. But now, as we are working from homes, coffee shops, and co-working spaces, the video conferencing technologies and social media we use are generating more and more data. As a result, so much data is being captured from the edge: both static (coffee shops, etc.) and moving (airplanes, cars, ships, etc.).

Coming to industry 4.0, every company is adopting and making their machines smarter. IOT is not just for the workplace anymore, but has become a part of everyday life. For instance, home automation generates a huge amount of data. Think about the Alexa and Google home devices being used across the world – everything from home appliances to our kid’s video games, is generating data.

Evolving Higher Data Storage Capabilities

In the past two decades, due to exponential rise in data usage, data centers developed stringent requirements for greater storage capacity per square area and faster data transmission, the industry continued to evolve. Innovators focused on finding ways to achieve larger capacity and faster throughput, while using limited space and staying within their power budget.

Flash technology became popular because of its small size and ability to deliver faster insights using significantly lower power consumption than hard drive technology. However, even though this option solves some size and power problems, it has limitations. For example, the lifespan of most flash devices allows you to write data to them only a certain number of times before they fail mechanically.

Over the past 90 years, data storage evolved from magnetic drums and tapes to hard disk drives, then to mixed media, flash, and finally cloud storage. That’s where we are today, and as our storage needs increase, innovation continues to evolve in multiple areas.

The Paradigm Shift to Data Storage at Edge
Big data plays a pivotal role in almost everything we do these days, but it’s no longer enough to just have access to data-driven insights—particularly if they are outdated and obsolete. As the amount of data generated grows and data capture increasingly moves closer to edge environments, urgent processing is critical to deliver timely intelligence that reflects real-time circumstances.

Organizations are progressively experiencing more pressure to obtain and apply insights rapidly, before situations change. This fact makes it imperative for business leaders across all mainstream industries to embrace active data and deploy ways of capturing and transporting it for immediate processing.

The Challenges of Managing Big Data
To optimize AI for the future, we also need high-performance systems. These could be storage or cloud-based systems, processed by modern, data-hungry applications. The more data you feed these applications, the faster they can run their algorithms and deliver insights, whether these are for micro strategy tools or business intelligence tools. This is usually called data mining, and, in the past, we did it by putting the data into a warehouse and then running applications to process it.

However, these methods are rife with challenges. Data-generating devices are now continuously churning out ever-growing amounts of information. Whether the source is autonomous vehicles or healthcare, and whether the platform is a drone or edge device, everything is capable of generating larger amounts of data than before. Until now, the data management industry has not been able to capture these quantities, either through networks, 5G, cloud, or any other storage method.

These circumstances have led to 90% of data gathered being dropped because of inadequate storage capacity and the inability to process it quickly and deliver it to a data center. The outcomes also apply to critical data captured at remote sites that have no connectivity or cloud applications running at the edge.

It’s essential for us to develop ways to resolve these challenges, solutions that include timely capture, transportation to data centers/cloud and immediate processing using a single, lightweight storage that enable us to enjoy the advantages of putting AI to work for humanity.

Preparing for the Future
CIOs are always looking for new ways to operate infrastructure cost-effectively. To prepare your company to get high-performance results in the future, all applications you are running need to be highly responsive. Not all data is accessed all the time. Some datasets are only accessed for a short period, and then they are out of date. To optimize your infrastructure in terms of cost, performance, and space elements, you should:

• Analyze the data characteristics and identify datasets and segments that aren’t needed constantly.
• Design a data architecture that includes slow-speed storage for the non-critical data. This could be a hybrid of fast storage technologies and hard drive technologies.
• Layer the architecture so that only active data requires fast storage, and the rest can be stored in slower locations.

There are certainly different types of products coming up in flash SSD drives. Higher dense storage systems will also follow this technology. The Big Data space is seeing some breakthrough innovations to address storage, migration and deployment challenges and one needs to stay abreast to benefit from the data available to us.

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