Big Data Analytics in Cloud Computing: An Overview

Cloud computing is now almost two decades old, and so is big data. Together these two technologies have the power to drive business growth and revenue. Both technologies became extremely popular, and businesses around the globe began using cloud computing for big data.

In this article, we explore the powerful harmony between these two transformative technologies and help you discover – if you haven’t already – how the scalability, flexibility, and cost-efficiency of cloud platforms help big data analysis and what it means for your business. If you want to learn more about cloud computing for big data, consider going for AWS Solution Architect Associate course. 

What is Big Data? 

Big data refers to the massive volumes of structured and unstructured business data generated from various sources at high velocity. It encompasses vast amounts of information that traditional databases and processing techniques need help to handle. By leveraging advanced platforms like cloud computing for big data analytics, businesses can derive meaningful insights from big data to drive innovation, optimize operations, and make informed decisions.

Features and Characteristics of Big Data 

Big Data is voluminous data that any business collects to gain business insights for decision-making. But how big must the data be to be called big data? Is sheer volume enough for it to be termed big data?

The answer to these questions lies in the five characteristics – the five Vs of big data. These are volume, value, variety, velocity, and veracity. Let us look at each one of them:

• Volume: Big data involves massive amounts of data surpassing traditional systems' capacity.
• Velocity: Big data is generated at high speeds, requiring real-time or near-real-time processing to derive timely insights.
• Variety: Big data encompasses diverse data types, including structured, unstructured, and semi-structured data from various sources.
• Veracity: Big data can be prone to inaccuracies, inconsistencies, and noise (irrelevant or meaningless data), requiring careful data cleansing and quality assurance.
• Value: Extracting value from big data through analytics enables organizations to gain valuable insights, uncover patterns, and make calculated bets.
• Variability: Big data can exhibit fluctuations and unpredictability in volume, velocity, and variety over time.
• Complexity: Big data often involves complex relationships and intricate data structures. It requires sophisticated analytics techniques to extract meaningful insights.

Big Data Analytics in Cloud Computing 

Big data analytics and cloud computing have transformed how organizations process and analyze large chunks of structured or unstructured data. Cloud platforms like Azure, Oracle, and MongoDB offer tools and frameworks for data analytics. Some of these are free, while others charge a fee. Here’s how big data analysis works:

Big Data Analytics Cycle

Typically, the big data analytics cycle involves extracting the data from the source, processing and storing it in standard formats, and presenting it for decision making a.k.a. data visualization. Cloud platforms enable businesses to scale computing resources on demand, making it easier to process large amounts of data in real time, offer faster insights, and enable informed decision-making.

Moving from ETL to ELT Paradigm

ETL and ELT are two data workflows typically used for big data transformation and analytics. ETL or Extract Transform Load refers to a framework where data is extracted from the source, transformed into the desired format, and then loaded into the data lake or data warehouse for storage and use. In the ELT paradigm, data is extracted and loaded. 

Transformation is done at the time of utilization of the data. By loading directly into the cloud, ELT takes advantage of the processing power of cloud computing for big data analytics. Since cloud resources are shared, ELT is cost-effective. Besides, transforming at the presentation time is efficient as data is transformed directly into the format required for visualization.

Pros of Big Data in the Cloud 

It is possible to process data on local servers. However, there are certain distinct advantages of performing big data analytics in cloud computing.

Scalability

Shared clouds offer the flexibility of scaling resources, including data storage on demand. This means you can expand your storage as your data volumes grow, eliminating the need to invest in hardware, software, and maintenance upfront.

Agility

Big data and cloud storage work well together for another reason. Apart from scalable storage, clouds also offer the flexibility to rapidly deploy custom applications into the cloud end and integrate it with cloud services. You can thus adapt quickly to evolving market conditions and leverage opportunities.

Cost

Another significant role of cloud computing in big data is enabling cost efficiency. Apart from the reduced investment in IT infrastructure and maintenance, you also save on energy, licensing costs, development, HR, and other related costs. These resources can be utilized in other initiatives contributing to business growth and revenue.

Accessibility

If you use cloud services like Amazon cloud computing for big data analytics, you configure it to access data securely from any location or device. This makes it easier for your teams and stakeholders to have information at their fingertips for informed decision-making.

Resilience

Most cloud computing platforms use distributed storage. This means that even if one cloud server malfunctions, all your data does not become inaccessible. Using failover and data recovery technology, cloud service providers can provide an almost seamless experience and minimize the possibility of data loss.

Cons of Big Data in the Cloud 

If you have ever used cloud computing for big data analytics, you’ll know that with all the benefits that cloud computing brings, there are still some challenges to managing the alliance between big data & cloud computing. Below is the look: 

Network Dependence

Big data and cloud computing rely heavily on stable network connections, which can be a significant constraint if businesses experience frequent network issues, outages, or slow internet speeds. This dependence may affect their ability to access and analyze data, leading to reduced efficiency, performance, and missed deadlines.

Storage Costs

The true benefit of using a service like Amazon cloud computing for big data lies in its scalability. That is to say, it is cost-efficient when data volumes are high. The initial costs of implementing a cloud-based big data solution include your cloud subscription and the costs incurred in setting up your cloud storage and transferring your data to the cloud. These can be significant for low volumes of data.

Security

One of the primary concerns surrounding big data and cloud computing involves data privacy and security. Since data is stored on third-party servers, organizations need to ensure that their cloud service provider adheres to strict security practices and compliance standards. Despite advances in encryption and data protection measures, potential breaches or unauthorized access remain risks that need to be addressed.

Lack of Standardization

Another challenge is the lack of standardization across cloud platforms. For instance, if you want to switch providers, you may not be able to process your cloud computing big data using Hadoop. Or you may need to invest additional resources to adapt Hadoop to the new platform's structure, integration, and management requirements. Standardizing tools, file formats, and data workflows can be time-consuming, complex, and expensive.

Despite these challenges, businesses still use big data for cloud computing because of its scalability, flexibility, and cost-effectiveness.

Choose the Right Cloud Deployment Model 

Cloud technology is essentially deployed in one of three ways. That is to say, cloud technology has three deployment models. Depending on your business goals, you need to choose the right cloud deployment model for your business.

Private Cloud

Private cloud deployments offer you dedicated and secure cloud environments, allowing you greater control over your data This highly customizable model is ideal for businesses with strict compliance requirements or those dealing in sensitive data and want to minimize the risk of unauthorized access.

Private clouds may be on-premises, i.e., on the local server of the business or a dedicated server or partition on the cloud provider's server. A private cloud is the most expensive cloud deployment and also the most secure.

Public Cloud

Public clouds are managed by third-party providers and shared by multiple users, offering affordable and flexible solutions for big data processing, storage, and more. They are best suited for smaller businesses or projects with minimal security and compliance concerns, enabling rapid provisioning and scalability without significant investment in infrastructure. While data is secure in public clouds, too, the risk of exposure and data leaks is higher. Public clouds are the most cost-effective of the three cloud deployment models.

Hybrid Cloud

To provide the best of both worlds, some cloud providers offer what is known as the hybrid deployment model. Hybrid clouds combine the benefits of private and public cloud environments, allowing organizations to select where data is stored and processed based on security, compliance, or performance needs. This approach offers a highly adaptable solution that caters to specific business requirements while retaining the benefits of both public and private clouds.

Multi-cloud

Multi-cloud deployments utilize the resources of multiple cloud providers to reduce cost, vendor lock-in, and offer greater flexibility in infrastructure management. This strategy enables businesses to optimize resource utilization, performance, and cost-efficiency by selecting the best-fit services from different providers for their specific needs.

One thing to remember when selecting your cloud deployment model is the amount of effort you and your IT team will have to put in. For instance, if you opt for a private cloud, it might fall upon you to set up the infrastructure and write the required application. If you are unsure about this, you would be well advised to invest in a certification like Cloud Technology courses which will clarify cloud infrastructure and architecture. A lot also depends upon the cloud service provider you choose so here’s a quick review of some of the top providers that offer big data analytics in the cloud.

Review Big Data Services in the Cloud 

AWS

Using cloud computing for big data by Amazon Web Services (AWS) you can build efficient data pipelines on cloud platforms. AWS offers a comprehensive set of cloud computing solutions for big data analytics and processing. Key services include Amazon Redshift, a fast and scalable data warehouse, and Amazon EMR, which simplifies using Hadoop and Spark for big data workloads. AWS also provides services for data storage (Amazon S3), real-time analytics (Amazon Kinesis), and machine learning (Amazon SageMaker).

Microsoft Azure

While using cloud computing for big data on Amazon Web Services is a good choice, Microsoft Azure too offers some good big data analytics services for handling large volumes of data, including a spectrum of tools and platforms for data ingestion, storage, and processing. Key offerings include Azure Synapse Analytics, a fully managed data warehouse solution, and Azure HDInsight, a managed Hadoop and Spark service for big data processing.

Google Cloud

Google Cloud provides a robust ecosystem of big data tools, including BigQuery for scalable data warehousing, Cloud Dataflow for real-time data processing, and Cloud Dataproc for managed Hadoop and Spark services. It also offers advanced machine learning and AI solutions like Cloud AutoML and TensorFlow. 

Conclusion 

Although both big data and cloud computing are nearly two decades old, they are still popular technologies in use by businesses, not just for data analytics but for several other uses such as storage, processing, and accessibility. While many cloud service providers are in the market, a few have established themselves as industry leaders providing specialized services. 

The choice of service provider and deployment model depends on the need, data volumes, and other factors. We hope this guide has helped you understand your options. If not, consider taking a KnowledgeHut Cloud Computing certificate course that will give you clarity and drive career growth.