An extraordinarily large unit of digital data, one Exabyte (EB) is equal to 1,000 Petabytes or one billion gigabytes (GB).

Prefix

SI prefixes.

As of 2018, the yottabyte (1 septillion bytes) was the largest approved standard size of storage by the System of Units (SI). For context, there are 1,000 terabytes in a petabyte, 1,000 petabytes in an exabyte, 1,000 exabytes in a zettabyte and 1,000 zettabytes in a yottabyte.

The International System of Units (SI) denotes “exa” as a multiplication by the sixth power of 1000 or (1018). In other words, 1 exabyte (EB) = 1018bytes = 1,0006bytes = 1000000000000000000 bytes = 1,000 petabytes = 1 million terabytes = 1 billion gigabytes.

Zettabytes are used to describe data storage of extremely large amounts of information and code, also commonly referred to by technology professionals as big data. Big data might include any large amount of structured or unstructured data that is collected daily at rapid speeds.

What is a Brontobyte? One brontobyte is equal to one quadrillion terabytes. And a brontobyte is smaller than a geopbyte. A thousand brontobytes equal to one geopbyte.

Computer Storage Units Smallest to Largest

• Bit is an eighth of a byte*
• Byte: 1 Byte.
• Kilobyte: 1 thousand or, 1,000 bytes.
• Megabyte: 1 million, or 1,000,000 bytes.
• Gigabyte: 1 billion, or 1,000,000,000 bytes.
• Terabyte: 1 trillion, or 1,000,000,000,0000 bytes.
• Petabye: 1 quadrillion, or 1,000,000,000,000,000 bytes.

A zettabyte is a digital unit of measurement. One zettabyte is equal to one sextillion bytes or 1021 (1,000,000,000,000,000,000,000) bytes, or, one zettabyte is equal to a trillion gigabytes.

The vast majority of the world’s data has been created in the last few years and this astonishing growth of data shows no sign of slowing down. In fact, IDC predicts the world’s data will grow to 175 zettabytes in 2025.

Big Data Growth Trends The amount of data created each year is growing faster than ever before. By 2020, every human on the planet will be creating 1.7 megabytes of information… each second! In only a year, the accumulated world data will grow to 44 zettabytes (that’s 44 trillion gigabytes)!

You can query external data sources, store big data in HDFS managed by SQL Server, or query data from multiple external data sources through the cluster. You can then use the data for AI, machine learning, and other analysis tasks.

The Cloud enables the expansion of Big Data and data analytics. Big Data and Cloud Data can be used in conjunction to store large amounts of data and provides scalable processing and improved real-time analysis of data.

HDFS is made for handling large files by dividing them into blocks, replicating them, and storing them in the different cluster nodes. Thus, its ability to be highly fault-tolerant and reliable. HDFS is designed to store large datasets in the range of gigabytes or terabytes, or even petabytes.

MapReduce facilitates concurrent processing by splitting petabytes of data into smaller chunks, and processing them in parallel on Hadoop commodity servers. In the end, it aggregates all the data from multiple servers to return a consolidated output back to the application.

The Google file system (GFS) is a distributed file system (DFS) for data-centric applications with robustness, scalability, and reliability [8]. GFS can be implemented in commodity servers to support large-scale file applications with high performance and high reliability.