Data is everything these days and as expected, the growth of the total amount of data created will accelerate exponentially and reach 175 zetabytes by 2025. Every hour we create more data than we did in an entire year 20 years ago. And, when we measure volumes in zettabytes, we need a simple and inexpensive way to collect, store and use the data.

With the advent of the digital era, there are billions and billions of terabytes of data in existence. This is already bearing fruit in the complexity and diversity of data eco-systems. These online environments are becoming more diverse and are trending towards multi-clouds. The use of IoT, AI, smarter devices is becoming more prevalent in the information technology world. Also, computing resources are getting more expensive. As an entrepreneur, you have to admit that it can be hard to manage data these days.

There have been many analysts of the data market, who have identified five trends for the coming year. We have come up with recommendations for businesses in this new environment.

More businesses now use hierarchical security schemes which have proved to be very effective. Visitors go through certain checkpoints before they reach the company’s headquarters.

The adoption of hyperscale software ecosystems like microservices allow developers to develop applications without a large communications infrastructure. The cloud application trend is increasing, so it’s important for companies to invest in their security infrastructure. More companies are choosing to relocate their ai technology at data centers across the world and many more companies will be running in this type of model. It is important to protect user data as it is both stored and transmitted in a distributed deployment. There are various threats, such as physical theft, malware, and other cyberattacks that could compromise this information. One way to protect data is by encrypting it when it is at rest. Even if your industry doesn’t require encryption yet, you need to do it before the government mandates that all agencies switch over. It would be a hassle to have to rewire everything when/if the change occurs. Encryption also protects against data theft and ransomware attacks which can lead to big losses for your company.

The use of object storage in enterprises is becoming more widespread.

Recently, the majority of data is stored in the form of blocks, which makes it easier to index and require less space. One of the main advantages for object storage is that it can be managed with metadata, which makes it easy to save data even on an unlimited number of virtual recipients or physical media. Modern systems need more intelligent processing power. Object storage provides exactly the right tools for this job.

There are three types of storage: block, file, and object. Block storage is most powerful for applications that need advanced performance. File storage is perfect for legacy applications and provides a reliable infrastructure. Object storage offers fast retrieval for distributed workers who store their content in the cloud. And object storage is used in the development of new applications, and it’s often combined with block storage to provide speed and scalability. Many legacy file-based applications are being moved from those systems to an object storage infrastructure, which allows them to.

Object Storage is becoming more popular as an affordable and scalable solution for storing data both today and into the future. Already the standard for high capacity storage, it complements file storage by being more cost effective and scalable than older methods. Additionally, current trends lean towards object storage as a favoured data management strategy thanks to its compatibility with modern software applications. If you haven’t yet applied object storage to your data center, you should seriously consider this move.

Open-source composable systems have been on the rise lately and continue to enter more industries.

To design a good system, it helps to break things down into modules that can be operated individually. This process is not new but it’s now easier than ever with open-source modular software. The popularity of Kubernetes is down to it being an open source system that automatically deploys, scales and manages containerized applications. Open-source is the way of the future. With open-source principles, many people can contribute to an application, with all having potential access to unique specializations in different industries. Chances are better now to switch to the principle of hardware layout to meet the needs of software systems. It may make more sense for your business if you decide to go this route.

The ability to run workloads anywhere in your data center?whether it be at the edge of your network, on-premises, or in cloud-based environments?makes business management more flexible. Using composable systems also ensure you have enough resources for the next generation of IT without having to preconfigure everything so that workloads are statically balanced – especially when you don. Containers and Kubernetes are becoming the norm for data centers. To succeed in today’s tech environment, you need these in all your data centers.

There are two broad categories of data storage: centralized databases and distributed databases. Centralized databases keep all the data in one place. Distributed databases store the data across many different servers, so if one server goes down it doesn’t affect the whole system.

“Hot” data is served from flash sticks, and the excess – from SSDs. For example, NVIDIA GPU technology separates memory into levels: registers, shared memory and global memory. Each level has its own traits. For example, registers are fast but have short access latency. Global memory, on the other hand, has a slower delay.

NVIDIA has a program and API that is optimized for their multi-level memory and architecture systems. By analogy, SSDs and HDDs can be used at different storage tiers. Today it is inefficient to use homogeneous storage for ultra-large volumes of payloads.

This matters because it’s not affordable to store everything on high-performance drives, especially when there aren’t enough of them. Alternately, if you only have high-capacity drives, performance will be lacking. That’s why the current trend of tiering is growing, because this scheme provides the most effective balance of cost and performance. Storage at all levels is the name of the game – new technologies have been introduced that are really pushing things forward. Storage class memory is a big step in this direction and will inevitably result in a whole new type of technology.

If you had an unlimited budget, your company’s data centers would equip only the newest, most expensive Intel 3D XPoint media. However, while reality dictates a hierarchical division, as data that is “hot” and accessed frequently is placed on expensive media at the top end of this hierarchy and those that are not accessed often are stored on affordable media at the bottom. Hot and cold data tiers are nothing new. With today’s data centers, automatic tiering occurs to keep all the ‘needle in a haystack’ data from clogging up the best disks. If your data center doesn’t use tiering of different types of drives for this purpose, then there’s a good chance you’re losing out.

Formative AI has been evolving and gathering data to make it more useful.

As we create more and more data, it becomes easier to derive new findings. Archived data can now be processed with advanced machine learning to receive additional information. As the use of artificial intelligence in business becomes more prevalent, it has also become clear that this is just the tip of the iceberg. Business leaders will need to store even more data and be prepared to train their models for better insight. They’ll also need a high-performance environment to house archives that are increasingly becoming longer-lasting. 

Machine learning has always had the potential to drastically change the world and we’re finally reaching a point where breakthroughs can be made. The only downfall is that machine learning needs more accurate information stored in HDDS for it to work optimally. The implications of machine learning are hard to predict but it’s important that companies prepare themselves by saving today so they can use the best training samples.

We can expect a big increase in data-driven AI and deep learning by 2025. That’s because 44% of all data that’s created at the center will be used for these types of analytics and artificial intelligence – more than was used in 2017. And more data from IoT devices will be transmitted to the edge of the corporate network, too. Data is becoming more centralized and decentralized simultaneously. It’s estimated that by 2025, 80% of the world’s data will be stored in the cloud. Meanwhile, we can already store 12.6 zettabytes of data on hard drives, optical drives, solid state drives and tape drives.

One of the best options for managing data in the current environment is DataOps, which allows for a level of interoperability between both data creators and consumers. This helps with not only organizing it, but also using AI/ML to find relevant relations. In addition, DataOps uses an ELT process to extract data from multiple sources and then transform it. AI is able to turn this raw data into useful information.