What Is Big Data Storage?

Written by Coursera Staff • Updated on

Big data is key to business growth. In this article, you can learn more about big data storage solutions, big data storage options, and the benefits of using big data in businesses.

[Featured image] An employee holding a laptop visits her company's big data storage warehouse.

Businesses, organizations, governments, and institutions rely on data to help guide their decision-making and strategic planning. These groups collect millions of bytes that pile up daily in vast volumes. To combat the sheer amount of data, a new field of high-demand professionals has emerged responsible for sorting and storing all this information through big data storage. 

Read on to learn more about big data storage solutions, the different options, and the benefits of big data to businesses.

What is big data?

Big data refers to extremely large data sets, either structured or not, that professionals analyze to discover trends, patterns, or behaviors. It's unique in that it has what professionals describe as the three Vs—volume, velocity, and variety—in such large amounts that traditional data management systems struggle to store or analyze the data successfully. Therefore, scalable architecture must be available to manage, store, and analyze big data sets.

Read more: What Is Big Data? A Layperson's Guide

What is big data storage?

Big data storage is a scalable architecture that allows a business or organization to collect, manage, and analyze immense sets of data in real-time. The design of big data storage solutions is specifically tailored to address the speed, volume, and complexity of the data sets. Some examples of big data storage options are:

  • Data lakes: Data lakes exist as centralized storage solutions that process and secure data in its native format without size limitations, and they can enable different forms of smart analytics like machine learning and visualizations.

  • Data warehouses: Data warehouses aggregate data sets from different sources into a single storage unit for robust analysis, supporting data mining, artificial intelligence (AI), and more. Unlike a data lake, data warehouses have a three-tier structure for storing data.

  • Data pipelines: Data pipelines gather raw data and transport it into repositories, such as lakes or warehouses.

Data lakes, warehouses, and pipelines exist within several different storage options, including:

  • Cloud-based storage: A cloud-based storage system is where a business outsources the storage of its data to a vendor that operates a cloud storage system.

  • Colocation storage: Colocation storage is the process of a business renting space to store their servers rather than having it on-site.

  • On-premise storage: On-premise storage is where a business manages its own network and servers on-site. This can include hardware, such as servers, that houses the data at an organization’s premises. 

What is big data storage used for?

The main purpose of big data storage is to successfully store immense amounts of data for future analysis and use. Big data is crucial for businesses and organizations, from health care research to retailers and security, to make more efficient, informed, and effective decisions. Without big data storage, businesses wouldn’t have the time, money, or technology to store and manage big data sets successfully. 

Because big data is so valuable for processing and understanding patterns and trends, it needs correct storage to work. Big data storage makes the application of big data to business decisions possible.

How does big data storage work?

Big data storage employs a system of commodity servers and high-capacity disks capable of analyzing the data sets. For example, in a cloud storage scenario, the big data sets exist in a server hosted in an off-site location that one can access through the internet. Virtual machines provide the space for the data to live safely, and it’s possible to quickly create more virtual machines when the amount of data grows past the servers’ current capacity.

Who uses big data storage?

Professionals across multiple industries use big data storage to store, manage, and analyze their data. These industries include health care, finance, government, education, and retail. These industries benefit from big data storage because it provides a unique opportunity to analyze data at a large scale, offering insights that wouldn’t be possible otherwise, such as predictions for the future and customer behavior analysis. 

Pros and cons of using big data storage

The pros and cons of big data storage typically relate to the volume of data being handled. Here are some advantages of using big data storage:

  • Data-driven. The large-scale data analysis allows businesses to become data-driven using concrete data to help make decisions and better inform strategic planning.

  • Make safe and informed decisions. Big data storage keeps data safe and lets professionals apply analytical tools to the data sets, resulting in more informed decision-making, better customer service, more flexibility in strategic planning, and increased efficiency in operations.

  • Flexible. Cloud-based storage is flexible and allows businesses to scale their needed servers up or down without up-front investment.

On the other hand, here are some factors to consider when using big data storage:

  • Costly. It’s expensive for a business to purchase the necessary space to store big data sets, and the cost will only increase as more data becomes available. For example, if a business opts to manage its servers on-site, it may face the risk of needing to purchase more systems and the staff to run them.

How to get started in big data storage

If you’re interested in pursuing a career that involves big data storage, the first step is to research a category known as “big data jobs.” These jobs all include working with big data, and you might find one that matches your specific skills and interests. Big data jobs often involve managing and creating solutions for data storage and thinking up strategies to use data to drive profits.

For example, a big data and AI engineer is responsible for designing, building, and maintaining big data storage and other architecture to make big data sets available for analysis. They create architectural solutions for storing big data, and they often work with other professionals, such as data scientists. To become a big data and AI engineer, you’ll need a four-year degree in math, computer science, or information technology. Some big data engineers also gain certification to become more competitive. The average annual salary for a big data engineer in the US is $126,110 [1].

Getting started with Coursera

You can sharpen your big data storage skills and learn foundational knowledge to pursue a career in big data storage on Coursera. With courses like Google Cloud’s Developing Applications with Google Cloud Specialization or the University of Colorado Boulder’s Master of Science in Data Science degree, you can explore the opportunity to learn more about big data storage, its applications across multiple industries, and the different ways to apply this knowledge in a big data career.

Article sources

  1. Glassdoor. “How much does a Big Data Engineer make?, https://www.glassdoor.com/Salaries/big-data-engineer-salary-SRCH_KO0,17.htm” Accessed March 15, 2024.

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