Big Data and Cloud Computing

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Every year, digital environments accumulate more and more data. Big data systems take in these huge amounts of raw data, and then turn it into meaningful information. These systems can operate on-premises or in the cloud.

Read on to learn why big data systems work well in cloud computing environments, and what challenges you can expect when placing your big data in the cloud. Hint: cloud computing services place big data controls right at your fingertips.

 

What Is Big Data?

The term ‘big data’ has two meanings—one refers to the type of data, while the other refers to data tasks. As a data type, big data is defined as huge amounts of complex data that can’t be processed in traditional data processing systems. Big data tasks involve the capture, transfer, storage, backup and analysis of data. Data visualization is a common tool used to condense big data into simple visual aids.

The Five Vs of Big Data

To evaluate the data’s level of complexity, data professionals often use five metrics, commonly referred to as The Five Vs of Big Data:

  • Volume—represents the amount of data, which usually starts at tens of terabytes.
  • Velocity—represents the speed of the data, when it is received and/or transferred.
  • Variety—represents the type of data, which can be structured, unstructured, or both.
  • Veracity—represents the reliability of the data, which is measured by the consistency, accuracy, and trustworthiness of the data source.
  • Value—represents the quality of the data, which determines how big data tasks are prioritized according to the value of each volume.
     

The Benefits of Combining Big Data and Cloud Computing

1. Disaster recovery in the cloud

Data loss has emotional, financial and reputational repercussions. People experience grief when they lose their data, which can in turn impact business continuity. Employees and companies that are found responsible for the data loss, often suffer damage to their reputation. Firms that lose their data often suffer from financial and reputational loses, as they try to recreate the data and/or compensate affected parties.

Cloud computing services, such as AWS disaster recovery, provide organizations with an effective backup and recovery mechanism. AWS offers an automated disaster recovery service called CloudEndure. If you’re using Amazon Elastic Block Store (Amazon EBS), you can use EBS snapshots for automatically creating incremental backups of your most recent changes.

 

2. Affordable big data storage and analytics

Big data often requires huge amounts of storage and analysis. On-premise data centers are often expensive and time-consuming operations. Cloud computing services manage and maintain the cloud infrastructure, providing organizations with a variety of ready-made and on-demand services. The on-demand pricing model offered by cloud providers allows organizations to grow their big data operations at scale.

Today, most cloud service providers offer a variety of storage and analytics types. Organizations can combine a variety of cloud services in a multi-cloud strategy that addresses their disparate needs. For example, you can use a private cloud for big data analysis of pre-determined volumes. To reduce storage costs, you can keep the rest of your big data in a data warehouse or a data lake.

 

3. Accelerated and real-time big data processing

Raw data has no meaning. Data processing helps organizations collect and translate raw data into meaningful and useful information. It’s part of the analysis process, during which machine learning algorithms process data from sources, such as data lakes, databases and connected devices, and look for patterns, such as purchase and health habits and user behavior.

Cloud computing operations offer scalable and flexible solutions. You can use distributed processing frameworks like Apache Hadoop to deploy your data across multiple mediums. You can then use big data cloud modules for adjusting the capacity during large traffic spikes, and enabling real-time analysis for ad-hoc events.

 

4. Big data in the cloud is agile

On-premise data centers designed for big data are somewhat like mammoths. They’re big. They’re heavy. And they’re complicated. It takes a lot of time and thinking power to design an on-premise data center for big data. What happens if you need to make changes after your data center is running? That could easily turn into a complex and expensive operation. One that not every company can afford.

Due to its integral connection to people, data is inherently volatile. There are peaks, during which a topic is trending or an advertising campaign gets people talking about the brand. There are lows and ebbs when people generate less data. Big data systems need to be capable of dealing with the high tides as well as the low tides of data. Cloud computing platforms are built for scalability, providing professionals with on-demand services that are easy and simple to use.

 

Challenges to Big Data in a Cloud Environment

Cloud computing environments are typically provided as a service—Infrastructure as a Service (IaaS) offers virtualized computing resources, Platform as a Service (PaaS) offers application development resources, Software as a Service (SaaS) offers software, and Database as a Service (DBaaS) offers data resources. You can also find database-specific managed services, such as MySQL and Postgres as a Service.

When you sign up for any cloud computing service, you introduce a third-party entity into your business and digital ecosystem. When you delegate a task to a third-party service, you forfeit a certain level of control. In cloud computing, that means you’re giving up control over many aspects of the digital environment, including your security perimeter.

The ability to delegate responsibilities to cloud providers is a major benefit for many professionals. However, there are some drawbacks, because you’re essentially relying on your cloud provider to secure your cloud while ensuring compliance. Before choosing a cloud environment for your big data, make sure it meets the required compliance for the data you’re processing and analyzing.

If you’re analyzing transactional or eCommerce data that contains credit cardholder information, you’ll need to comply with the Payment Card Industry Data Security Standard (PCI DSS), which regulates the security of cardholders’ data. If you’re processing healthcare information, you’ll need to comply with the Health Insurance Portability and Accountability Act of 1996 (HIPAA), which protects personal health and medical information.

 

Conclusion

Cloud computing simplifies big data processes. In a field bowed down by torrents of data, simplification is crucial for enabling progress. Technological advancement relies on continual forward movement. Cloud computing is gearing up to solve the biggest challenges of big data, providing professionals with affordable means to turn useless bytes into meaningful information.

 


Author Bio

Gilad David Maayan author image

Gilad David Maayan is a technology writer who has worked with over 150 technology companies including SAP, Imperva, Samsung NEXT, NetApp and Ixia, producing technical and thought leadership content that elucidates technical solutions for developers and IT leadership. Today he heads Agile SEO, the leading marketing agency in the technology industry.

LinkedIn: https://www.linkedin.com/in/giladdavidmaayan/