Big Data Analysis in Cloud Computing: A Detailed Overview

Big Data Analysis in Cloud Computing

Software as a Service (SaaS) has become increasingly popular over the past decade. Since 2015, the global SaaS industry has grown to $1617 billion from $31.4 billion. As this industry keeps growing, it’s crucial to stay up-to-date with cloud infrastructure and best practices regarding data storage, such as Big Data and Cloud Computing.

If you’re a part of the IT industry, you will most likely be familiar with both concepts. In fact, you might have worked with either or both of them, too, since they go hand-in-hand. For those still trying to learn about it, here’s everything you need to know about big data analysis in cloud computing.

Table of Content

  1. What is Big Data Analytics?
  2. What is Cloud Computing?
  3. What’s the Difference Between Cloud Computing and Big Data Analysis?
  4. Why is Big Data Analytics Important?
  5. How Does Big Data Analytics Work?
  6. Key Big Data Analysis Technologies and Tools
  7. Collection and Storage
  8. Processing
  9. Scrubbing
  10. Analysis
  11. Challenges and Benefits of Big Data Analytics
  12. Big Data and Cloud Computing: Why Both Work Well Together?
  13. Key Takeaways

What is Big Data Analytics?

Big data analytics refers to all the tools, methods, and applications used to collect and process insights from a high-velocity, high-volume, or varied set of data. These data sets can be from various sources like email, web, social media, mobile, and networked smart devices. The data used in big data analytics is usually generated at high speed that varies in form, such as unstructured (such as audio files and images), semi-structured (such as webpages and XML files), or structured (such as Excel sheets and databases tables).

Since the traditional form of data analysis software couldn’t support such an extensive complexity scale, big data analytics was specially designed to assist with such complex projects.

What is Cloud Computing?

Compared to Big Data Analytics, Cloud Computing can be explained as the processing of literally anything as long as it’s on the cloud, even Big Data Analytics itself. The cloud is a set of high-powered servers many providers offer, such as RedSwitches. Cloud computing can be useful if your business regularly views or uses large data sets since it’s quicker and versatile.

What’s the Difference Between Cloud Computing and Big Data Analysis?

When looking at the two at large, they might seem somewhat similar. However, when closely examined, you’ll realize that the relationship between cloud computing and big data analytics is synergistic.

The main difference between the two is that “Big Data” refers to the data collected in the form of large sets, whereas “Cloud Computing” refers to the mechanism that allows businesses to take this data in and perform various operations on it by storing it remotely on the “cloud.”

A quicker way to quickly summarize how the two are similar or different is the table below:

S. No Cloud Computing Big Data Analytics
1 It’s used to store large sets or volumes of data remotely It helps process large volumes of data for decision making
2 It’s a computer concept/paradigm Its data analytics of voluminous and complex data
3 It focuses on providing universal access to the data of an organization It focuses on deriving and sharing insights regarding the data gathered
4 Common advantages include: reliability, cost savings, centralized and on-demand Common advantages include: logical and accurate correlations for fine resolutions
5 It offers various services, such as IaaS, PaaS, and SaaS It offers various solutions, such as Hadoop, Sqoop, Ambari, Hive, MapReduce, and Oozie
6 It uses an extensive network range of cloud servers over the internet It’s used either on the cloud or within the company’s data center
7 It’s a platform to access large data sets minus its computation It’s a process of structuring, cleaning, and interpreting the gathered data.

Why is Big Data Analytics Important?

Data is so important that it’s woven into our everyday lives. Every day, around 2.5 quintillion bytes of data are collected, pushing the Big Data Analytics for the healthcare industry to roughly $79.23 billion by 2028. However, Big Data Analytics is not just limited to one industry. With the proper use of this enormous volume of data, there’s something for every industry.

Big Data Analytics can help organizations transform the way they think, work, and provide value to customers. Big Data Analytics comes with various applications and tools that allow organizations to optimize operations, gain insights from the 2.5 quintillion bytes worth of raw data, and predict future outcomes.

This power of better decision-making through insights is why big data is essential. Especially when Big Data and Cloud Computing are paired, it could be how healthcare providers discover a new and improved option for clinical care or how retailers fix the bottlenecks in the supply chain.

How Does Big Data Analytics Work?

As mentioned, Big Data Analytics allows organizations to glean insights and predict future outcomes through the analysis of data sets. However, effective results and accurate prediction depend greatly on how successfully the data was analyzed in the first place.

The correct procedure is first to store the large sets of data, then organize and clean them using a series of applications with the help of a step-by-step preparation process as follows:

  • Collect

The data collected for Big Data Analytics comes in structured, semi-structured, and unstructured forms since it’s collected using various sources across mobile, web, and the cloud. It must then be stored in a repository, which is a data warehouse or data lake, for it to be processed.

  • Process

The processing phase consists of verifying, sorting, and filtering the stored data, which helps prepare it for further use while improving the performance of queries.

  • Scrub

Once the data is successfully processed, it’s ready to get scrubbed. Any redundancies, conflicts, invalidations, incomplete fields, or formatting errors found are either corrected or cleaned for accurate results.

  • Analyze

The final step is to analyze the scrubbed data using Big Data Analytics tools and technologies, such as AI, data mining, machine learning, predictive analytics, and statistical analysis. These tools are essential to define and predict the behaviors and patterns in the data.

Key Big Data Analysis Technologies and Tools

Big Data Analytics is often referred to as a single solution or a system. In contrast, it comprises multiple tools and technologies that work cohesively to store, move, scale, and analyze the gathered data.

These technologies and tools can vary based on your organization’s infrastructure, but the most commonly used Big Data and Cloud Computing technologies include:

Collection and Storage

  • Hadoop

This was one of the very first frameworks to address the needs of Big Data Analytics. Apache Hadoop, an open-source ecosystem, allows organizations to store and process large data sets using a distributed computing environment. This tool can be scaled up or down based on the needs, making it exceptionally flexible and cost-efficient.

  • NoSQL Databases

Contrary to traditional databases, NoSQL databases don’t require the data types to adhere to a fixed structure or schema. This means it supports data of various models and forms, something extremely useful when working with large quantities of raw or semi-structured data.

  • Data Lakes and Warehouses

Once the data has been collected from the source, it is essential to store it in a central silo for further processing, such as data lakes or data warehouses. A data lake holds unstructured and raw data that is then prepared to be used across applications. A data warehouse is a system that extracts pre-defined and structured data from various processes and sources to prepare it for operational use.


  • Data Integration Software

Data integration software consolidates data from various platforms into a single unified hub, such as a data warehouse. It helps users gain centralized access to all the information needed for business intelligence reporting, data mining, and operational purpose.

  • In-Memory Data Processing

In-memory data processing employs RAM, or memory, to process data instead of conventional data processing, which requires a disc. As a result, processing and transfer rates are significantly accelerated, enabling enterprises to get insights in real time. Batch processing and real-time data stream processing are done in memory using Apache Spark processing frameworks.


Data Processing and Scrubbing Tools

Data cleansing programs correct errors, clear up duplicates, remove missing numbers, and rectify grammar issues to guarantee that your data is of the greatest caliber. Your data is then validated and standardized by these tools so that it is prepared for analysis.


  • Data Mining

Big Data Analytics acquires insight from the data with expert knowledge and discovers techniques like data mining, which draw underlying patterns from enormous data sets. Data mining uses specially designed algorithms to discover current data trends and find any significant links.

  • Predictive Analytics

Analytic models that anticipate patterns and behavior may be created using predictive analytics. This is made possible by machine learning and other statistical techniques that let you predict future results, enhance procedures, and satisfy user demands.

  • Real-Time Analytics

Real-time streaming solutions, like Azure Data Explorer, store, process, and analyze your cross-platform data in real-time, enabling businesses to acquire insights immediately. They do this by linking a number of scalable, end-to-end streaming pipelines.

Challenges and Benefits of Big Data Analytics

Many big industries today use various data analysis tools to make better decisions regarding operations, product strategy, sales, consumer care, and marketing. Big Data Analytics and Cloud Computing allow organizations to work with any large amount of data and derive meaningful insights.

Some of the other commonly considered benefits and challenges of Big Data Analytics include the following:

Drawback 1: Organized Data with Easy Accessibility

The greatest challenge associated with big data is wondering how to manage the massive volume inflow of information so that it properly flows throughout the applications. It’s pivotal to avoid silos, plan the infrastructure and keep the data integrated.

Drawback 2: Quality Control

Managing the integrity and quality of the data may be challenging and time-consuming, particularly when a large amount is coming in quickly. It’s essential to ensure that data collection, processing, and cleaning procedures are integrated, standardized, and optimized before beginning any analysis.

Drawback 3: Keeps Data Secure

Data protection is more crucial than ever with increased data breaches. The analytics system’s potential for security risks increases as it expands, including phony data, leaks, compliance problems, and software vulnerabilities. Data encryption, regular security audits, and due diligence all serve to allay some of these worries.

Drawback 4: Choosing the Right Tools

The abundance of accessible tools and technology might make the decision-making process difficult. This is why it’s crucial to educate oneself, keep up with current events, and, if possible, seek an expert’s advice when necessary.

Benefit 1: Time-Efficient

Big Data Analytics accelerates the process by which firms transform information into insight. Making educated decisions about products, operations, marketing, and other company objectives follows from these insights.

Benefit 2: Cost-Efficient

Large data sets need storage, which may be costly to maintain. Yet, with the development of more scalable storage solutions, businesses may now increase operational effectiveness while cutting expenses. This translates to larger profit margins and more effective processes.

Benefit 3: User Friendly

Big data’s sophisticated business intelligence capabilities use predictive analytics to anticipate behavior and analyze consumer patterns. Businesses may tailor items to their customers’ demands by knowing more about their desires.

Big Data and Cloud Computing: Why Both Work Well Together?

When combining Big Data and Cloud Computing, they create infinite possibilities that complement one another. If we just had access to Big Data, we would only have substantial data sets and enormous potential value just sitting there, useless. Using simply our computers to analyze this data would be possible and impractical.

However, when paired with Cloud Computing, we can use the state-of-the-art infrastructure to process raw data and gain valuable insights by investing only a fraction of the time and power. Moreover, what do you think fuels cloud application development? Big Data!

Without Big Data and Cloud Computing coexisting and working cohesively, there would be just large data sets with little or no potential.

Big Data Analysis in Cloud Computing

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Key Takeaways

Cloud Computing and Big Data Analytics are amazing tools that play a huge role in our fast-paced digital society. When these two are linked together, it gives people’s great ideas a chance at success, even with limited resources. However, this success greatly depends on choosing the right cloud computing partner with the scalability to meet your business needs, such as RedSwitches.

We at RedSwitches offer customized bare metal servers that can tackle traffic volumes and workloads of all sizes. When using RedSwitches for cloud computing solutions, you get your servers, an opportunity to host near your audience, and personalized support 24 hours a day.

Ready to get your hands on the finest Cloud Computing for Big Data Analytics? Get in touch with us today to get started.