What Is an Exabyte and 4 Technologies Enabling Huge-Scale Storage

An exabyte is a unit of digital information storage equal to approximately one quintillion bytes, or precisely 1,024 petabytes (PB). In binary terms, an exabyte is 2^60 bytes, which is 1,152,921,504,606,846,976 bytes. It’s a large data size, reflecting the enormous amount of information generated, processed, and stored by digital technologies.

An exabyte can store a vast amount of content—for example, hundreds of thousands of high-definition movies or the entire written works of humanity multiple times over. As digital content creation and consumption continue to grow, exabytes are increasingly used as a measure of data storage in various industries and technologies.

The World Economic Forum estimates that by 2025, 463 exabytes of data will be created daily around the world, and that the entire digital universe is over 44 zettabytes in size—a zettabyte is a 1,000 exabytes. 

How Does an Exabyte Compare to Other Data Sizes?

The following table shows the differences between the commonly used data sizes, from byte and kilobyte all the way to yottabyte. Note that the number of bytes in the table are calculated according to the binary system (learn more in Exabyte vs. Exbibyte below).

Why Do Companies Need Exabytes of Storage? 

As of the time of this writing, most companies do not yet need even a single exabyte of storage, while a petabyte (1/1000 of an Exabyte) is considered a very large amount of storage. But with the exponential growth in data, many companies will soon approach the need to store and process an exabyte or more.

There are several applications that may require exabyte-level data storage, now or in the future:

• Cloud services and data centers: As organizations and consumers continue to generate large amounts of information, they need cloud and data center infrastructure to store, process, and manage this data at scale. These technologies offer scalable resources to handle the growing demands of storing exabytes of data.
• Big data analytics and AI: These technologies process and analyze enormous volumes of data to identify patterns, predict outcomes, and inform decision-making. Given the complexity and size of the datasets involved, especially with unstructured data like images, videos, and social media interactions, the storage requirements can escalate to exabyte levels.
• The Internet of Things (IoT): This generates data from connected devices across various sectors, including healthcare, agriculture, smart cities, and industrial automation. Each device collects and transmits data in real time, contributing to the accumulation of exabyte-scale datasets. These datasets help in analyzing trends, optimizing operations, and making informed decisions. 
• Archiving and compliance: Regulatory mandates across various industries, including healthcare, finance, and telecommunications, require organizations to retain records for extended periods. This archival data includes emails, transaction logs, and customer information, growing over time. 

This is part of a series of articles about data lake

In this article:

• How Does an Exabyte Compare to Other Data Sizes?
• Why Do Companies Need Exabytes of Storage?
• Exabyte vs. Exbibyte: What Is the Difference?
• Key Technologies Enabling Exabyte Storage
• The Future of Exabyte-Scale Data

Exabyte vs. Exbibyte: What Is the Difference? 

An exabyte and an exbibyte measure digital information but use different numerical systems: 

• Exabyte is based on the decimal system and equals 1,000,000,000,000,000,000 bytes. In the decimal system, each storage unit is multiplied by 1,000 to derive the next unit (e.g. an exabyte is 1,000 petabytes).
• Exbibyte uses the binary system where each increment is a power of 2. It represents 1,152,921,504,606,846,976 bytes. In this system, each storage unit is multiplied by 1,024 to derive the next unit (e.g. an exbibyte is 1,024 pebibytes, a pebibyte is 1,024 gibibytes, etc). This means that an exbibyte is over 15% larger than an exabyte. 

Understanding this distinction is crucial for accurately assessing and managing data storage needs in environments where precise calculations are important. The choice between using exabytes or exbibytes often depends on context and industry standards. 

While the decimal-based system (exabytes) is prevalent in general computing and storage discussions due to its simplicity and alignment with SI units, the binary-based system (exbibytes) is more accurate for technical specifications and software development.  

Key Technologies Enabling Exabyte Storage

If and when your organization needs exabyte storage, here are some of the technologies that will make it happen:

1. Distributed File Systems

Distributed file systems enable the storage and access of data across multiple servers, providing the necessary infrastructure for handling exabyte-scale data. They offer redundancy, fault tolerance, and scalability by distributing data blocks across various nodes. This approach not only enhances reliability but also allows for parallel data processing, which significantly boosts performance. 

Examples of distributed file systems include Hadoop Distributed File System (HDFS) and Google File System (GFS), which are designed to support the high throughput and large data volumes characteristic of exabyte-level storage.

2. Object Storage Systems 

Object storage systems handle vast amounts of unstructured data. Unlike traditional file and block storage, object storage manages data as objects within a flat namespace, which allows for almost limitless scalability and enhanced data management capabilities. Each object includes the data itself, a globally unique identifier, and metadata, enabling efficient indexing and retrieval. 

Examples of technologies include Amazon S3, Microsoft Azure Blob Storage, and Cloudian’s on-premises object storage, all of which are able to scale to exabyte-scale datasets with high availability and durability.

3. Software-Defined Storage (SDS)

SDS offers a flexible approach to managing exabyte-scale data storage needs. Unlike traditional storage systems that are closely tied to specific hardware, SDS abstracts storage management from the underlying hardware. This abstraction allows for easier scaling and management of storage resources, allowing SDS solutions to dynamically allocate resources based on current needs.

Examples of SDS technologies include VMware vSAN, Red Hat Ceph Storage, and IBM Spectrum Scale, which provide scalable and efficient storage solutions capable of handling vast amounts of data.

4. High-Performance Computing (HPC) Storage

HPC storage systems are engineered to meet the rigorous demands of processing and analyzing vast datasets at high speeds, essential for scientific research, financial modeling, and complex simulations. They prioritize capacity and performance to handle the intensive workloads of HPC environments, integrating technologies such as parallel file systems to enable simultaneous access to data by thousands of processors.

Examples of HPC storage technologies include Lustre, IBM Spectrum Scale (GPFS), and DDN Storage, which are optimized for the high throughput and low latency requirements of HPC applications.

The Future of Exabyte-Scale Data

In the next decade, data generation and management will continue to see exponential growth, propelled by advances in technology and an increase in digital content consumption. The proliferation of IoT devices, further advancements in AI and machine learning, and the continuous expansion of the digital universe will drive the need for exabyte-scale data storage. 

Predictive analytics, real-time processing, and the integration of virtual reality into everyday applications will generate vast amounts of data, requiring new approaches to store, manage, and analyze these datasets efficiently. To accommodate this surge in data volume, future storage technologies will likely focus on enhancing scalability, durability, and accessibility. 

Innovations in distributed storage systems, improvements in object storage efficiency, and the adoption of software-defined storage are expected to play significant roles. Quantum computing may emerge as a game-changer for data storage and processing at exabyte scales. 

Exabyte-Scale On-Premises Storage with Cloudian AI Data Lake

Cloudian® HyperStore® AI Data Lake software is S3 API-compatible object storage software that is designed for limitless scalability. With a flat namespace and a fully peer-to-peer architecture, a Cloudian cluster scales in both size and performance as nodes are added. 

Learn more about Cloudian® HyperStore®.