Big Data and Cloud Computing: How They Work Together

By Michael AustinOwner and Editor

Updated Oct 8, 2026Published Aug 27, 201811 min read

Note (October 2026): The daily data figure was wrong; the commonly cited estimate is about 2.5 quintillion bytes per day (as of 2012), not 5 quintillion. The statistics have been corrected and newer IDC forecasts added.

Big data and cloud computing work together because big data needs huge, flexible amounts of storage and computing power, and the cloud rents both on demand. Instead of buying their own server clusters, organizations store large data sets in the cloud and analyze them with managed services such as Azure Databricks, Amazon EMR or Google BigQuery, paying only for what they use.

Big data refers to data sets too large or complex for traditional data-processing software to handle. It comes from every sector, from healthcare records to the billions of transactions financial firms process, and the total volume keeps growing.

There is no single agreed definition of big data; it is usually described by its characteristics, often called the five Vs: volume, velocity, variety, veracity and value. Big data techniques are used by many kinds of organizations, including universities, banks and logistics companies.

Big Data & Cloud Computing Explained

It was cloud computing whose spread made these technologies far easier to access. Because cloud resources are elastic and billed by use, organizations no longer have to buy and run their own clusters to analyze large data sets, and smaller enterprises can rent the same analytics services that large companies use.

The volume of information generated online keeps rising as cloud computing grows. Social media websites also generate large amounts of data every hour from user and marketer activity. This data can be structured (rows and columns, as in a database) or unstructured (text, images, video), and unstructured data is especially difficult to handle with traditional tools.

Let’s go through few figures from big data that were widely quoted when this article was first published (newer figures follow the list):

• About 2.5 quintillion bytes (2.5 exabytes) of data were generated every day as of 2012, according to the figure cited in Wikipedia’s big data article.
• Early IDC forecasts put the world’s data at roughly 40 to 44 zettabytes by 2020 (Wikipedia cites IDC’s forecast of growth from 4.4 zettabytes in 2013 to 44 zettabytes in 2020).
• Many organizations in the United States were reported to hold around 100 terabytes of stored information each, a figure from the same infographic that has not been independently confirmed.

Managing this much data was a challenge for companies, and cloud computing helped by taking over much of the infrastructure work, while each organization remains responsible for how its own data is secured and governed.

Big data and cloud computing complement each other: big data needs large amounts of storage and computing power, often in short bursts, and the cloud supplies both on demand. The cloud gives businesses a scalable way to run big data analytics without building their own data centers.

Big data analytics can benefit businesses of every size, especially when information is easy to access and handle. The main advantages of combining cloud computing and big data for businesses are:

Higher Productivity

Organisations can use the valuable insights collected from big data analytics to run most business operations more effectively.

Keeping data in the cloud (public or private) saves teams time on hardware maintenance, which they can spend on higher-value work. That can raise team productivity, although the effect on revenue depends on how the insights are used.

Also, cloud computing solutions let staff open important files from anywhere, which makes remote work possible. This helped many businesses keep operating during the COVID-19 lockdowns. The same access also allows real-time collaboration with business partners.

Real-Time Data Analytics

Batch processing, where data is collected and analyzed in scheduled runs, is still widely used, but many workloads now add real-time (stream) processing that analyzes data as soon as it is generated.

One example is edge computing where data from IoT devices is processed close to where it is generated instead of first traveling to a distant data center. The faster data is analyzed, the sooner a business can act on it.

Faster Data Processing

In a traditional on-premises IT environment, processing huge amounts of data demands extra hardware, time and expense. In the cloud, a cluster can be started for one job and shut down afterward, so many large jobs finish much faster and cost less than they would on fixed in-house servers.

Big data platforms available in the cloud, such as Azure Databricks – a collaborative Apache Spark based cloud platform for big data analytics, and Azure HDInsight, a managed service for Apache Hadoop, Spark, Kafka and Hive clusters, can be used to process unstructured data from your business, social media or other sources. To build skills on these platforms, one option is the MS Azure Certification accessible easily over the internet.

Migrating data between cloud services is now largely automated, although large transfers still need planning. Some vendors also offer physical migration from a data center to the cloud: Microsoft’s Azure Data Box, for example, ships a rugged storage device with 120 TB or 525 TB of usable capacity that the customer fills and returns, which helps businesses with massive amounts of data stored on physical servers.

Scalable and Cost Effective Environment

Cloud computing cuts the upfront spending needed to set up storage and processing infrastructure. Companies can adopt a private cloud to meet the need for security where sensitive resources can be stored, and big data analytics can be performed on them.

Various companies choose a hybrid cloud, keeping sensitive data on private infrastructure while using public cloud capacity and analytics services on demand. This lets companies scale storage and computing up or down as demand changes.

Wrapping Up

Taken together, these advantages explain why big data and cloud computing are now usually deployed together to support complex business operations.

As data volumes keep growing, businesses that need timely insights are likely to keep relying on this pairing. In short, cloud computing supplies the flexible infrastructure, and big data analytics turns the information stored on it into decisions.

What Is Big Data?

Big data is the term for data sets that are too large or complex to be dealt with by traditional data-processing software, according to the widely used definition summarized by Wikipedia. Big data is normally described by five characteristics:

• Volume: the quantity of data generated and stored, usually terabytes, petabytes or more.
• Velocity: the speed at which data is generated and has to be processed.
• Variety: the mix of data types, from structured database tables to text, images, audio and video.
• Veracity: how reliable and accurate the data is.
• Value: the useful information an organization can extract by analyzing the data.

What Is Cloud Computing?

Cloud computing is the delivery of computing resources such as servers, storage, databases and analytics over a network, on demand. The US National Institute of Standards and Technology (NIST), in Special Publication 800-145 (September 2011), lists five essential characteristics of cloud computing: on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service. Elasticity matters most for big data, because analytics jobs need large capacity for short periods.

NIST also defines three service models, and each one supports big data in a different way:

How Do Big Data and Cloud Computing Work Together?

Big data and cloud computing work together through a simple division of labor: the cloud provides elastic storage and computing, and big data tools use that capacity to collect, process and analyze information. A typical cloud big data workflow has four steps:

1. Ingest: data arrives from applications, websites, sensors and social media, either in batches or as a continuous stream.
2. Store: raw data is kept in low-cost cloud object storage or a data lake, and structured data in a cloud data warehouse.
3. Process: frameworks such as Apache Spark or Hadoop clean, join and transform the data on clusters that scale up for the job and scale down afterward.
4. Analyze and act: analysts query the results, build dashboards or train machine learning models, and the insights feed business decisions.

NIST’s deployment models also shape big data projects. NIST lists public, private, hybrid, community and multi-cloud deployments. Companies often keep regulated data in a private environment and send less sensitive workloads to the public cloud. For a closer look at how analytics changes decision-making, see this guide to how data science and business analytics shape businesses.

Which Cloud Platforms Are Used for Big Data?

The largest cloud providers each offer managed big data services. The descriptions below come from each provider’s own documentation, as of October 2026:

Readers comparing vendors can also see this overview of top cloud computing companies in the USA.

How Much Data Is Created Today?

The amount of data created worldwide is measured in zettabytes; one zettabyte is a trillion gigabytes. The figures in the list earlier in this article are old, and newer estimates are much larger:

• IDC’s Data Age 2025 study, produced with Seagate and reported by Network World in December 2018, forecast that global data would grow from 33 zettabytes in 2018 to 175 zettabytes in 2025.
• The same study predicted that 49 percent of the world’s data would be stored in public cloud environments by 2025, and that nearly 30 percent of data generated would be consumed in real time.
• IDC has since revised its forecasts upward. According to a 2026 summary of IDC data published by the UK IT firm Servnet, IDC’s June 2026 forecast puts the 2025 Global DataSphere at about 218.4 zettabytes and projects about 718.5 zettabytes by 2030. These are forecasts of data created, captured, replicated and consumed, not counts of unique information.

What Are the Challenges of Big Data in the Cloud?

Running big data in the cloud solves the capacity problem but brings its own risks. The most common challenges are:

• Security and privacy: misconfigured storage and weak access controls can expose large data sets. This guide to the risk of data breaches in the cloud explains how breaches happen, and this one covers choosing the best cloud security solutions.
• Cost control: pay-per-use pricing is cheap for short jobs but can grow quickly if clusters are left running or data is copied repeatedly between regions.
• Data transfer: moving very large data sets over a network can take a long time, which is why providers offer shipped devices such as Azure Data Box. Microsoft says Data Box is best suited to transfers larger than 40 TB where network connectivity is limited.

How to Start With Big Data in the Cloud

1. Define the business question the data should answer before choosing any tools.
2. Audit your data sources: where the data lives, how much there is and how sensitive it is.
3. Pick a deployment model: public, private or hybrid cloud, based on regulatory and security needs.
4. Start with a managed service such as a cloud data warehouse or Spark platform, which avoids building clusters by hand.
5. Set budgets and alerts in the provider’s billing tools so test workloads do not run up costs.

On the skills side, Microsoft’s architect credential is the Microsoft Certified: Azure Solutions Architect Expert. As of October 2026 it requires passing exam AZ-305 (Designing Microsoft Azure Infrastructure Solutions) and first earning the Azure Administrator Associate certification, according to Microsoft Learn. More on that path is in this article on building a career in cloud computing with Azure certifications, and for the analytics side, see how to build a career in big data.

Written byMichael AustinOwner and Editor

Michael Austin is the current owner of Justwebworld and manages the platform’s editorial direction, content strategy, and digital publishing operations. With a strong interest in online media and modern publishing, he focuses on creating informative, engaging, and easy-to-understand content across multiple categories. Under his leadership, Justwebworld continues to grow as a trusted multi-niche digital publication covering technology, business, lifestyle, automotive, sports, travel, and trending internet topics for readers worldwide.

More from Michael Austin →

Justwebworld has published guides since 2012. How we write and update articles · Report an error

Powered by