Cloud computing Big Data analytics is at the center of attraction in current technological developments addressing the large amounts of data produced every day by different sources.
What is Big Data?
Big Data refers to data volumes and accumulations of massive complex datasets that are difficult to process with traditional data processing applications. Challenges may include data capture, storage, search, analysis, data sharing, visualization, and transfer.
Characteristics of Big Data
To answer the question of what might qualify as ‘big data,’ industry analysts highlighted three features that must complement data getting considered as big data:
- Volume: Determines the size of data. Data is usually considered ‘big’ depending on the capacity of those analyzing the data and the tools available to them. For example, because of large numbers of users, it’s estimated that Facebook stores about 250 billion photos and over 2.5 trillion posts of its users.
- Velocity is the speed at which this data must be generated, processed, and analyzed. Consider this; Facebook users upload over 900 million photos per day, approximately 104 uploaded photos per second.
Social media and IoT are the most prominent data generators. With growing trends, Facebook needs to process, store and retrieve this information to its users in real-time.
There are two main types of data processing:
- Batch processing: This refers to blocks of data stored over some time. Batches of data usually take longer to process. Thus, Hadoop MapReduce stands out as the best framework for processing data in sets. Especially in situations where there is no need for real-time analytics. But large data volumes are essential to get more detailed insights.
- Stream processing: This is key to the real-time processing and analysis of data. Stream processing allows for data to get fed into analytics tools immediately and results generated instantly.
The best use cases for stream processing include fraud detection to flag anomalies s that signal in real-time. And online retailers, where real-time processing can help enable compilation histories of customer interactions to generate insight for additional purchases.
- Variety is simply the different data types generated using various sources. Big Data has three key categories:
- Structured Data: Transactional data, spreadsheets, relational databases.
- Semi-Structured: Extensible Markup Language – XML, web-server logs.
- Unstructured Data: Social media, audio files, images, video.
Over time, these three fundamental values have gotten complemented by two extra features:
- Veracity: Which highlights the quality and accuracy of data. Suppose it has something to offer. Reliability also rises regarding data extracted.
- Value: This is related to the social or economic value generated by data.
Cloud Computing Big Data Analytics
The many benefits of cloud computing such as elasticity, pay-as-you-go or pay-per-use model, low upfront investment make it a desirable choice for ample data storage, management, and analytics.
The Relationship Between Big Data & Cloud Computing
The amount of information collected has increased significantly along with the number of devices that can collect this information.
The concept of Big Data deals with storing, processing, and analyzing large amounts of data. Cloud computing provides the infrastructure that enables big data processes cost-effectively and efficiently.
Many business sectors, including healthcare and education, are racing to harness the power of Big Data. For example, Big Data is used to reduce costs of treatment in healthcare and to predict outbreaks of pandemics or prevent diseases.
Cloud Computing facilities allow easy processing of data by clients, where services can get accessed using the site’s user interface. Cloud computing facilitates easy access to services like database management systems, cloud-based virtual machines and containers, identity management systems, and machine learning capabilities. Amongst others.
Big Data gets generated through large, network-based computing systems in either standard or non-standard formatting. From there, you can effectively search, edit, and use the data to create insights.
Cloud infrastructure facilitates the real-time processing of Big Data. You get vast amounts of data from intensive systems and interpret it instantly. The cloud allows Big Data analytics to occur in a fraction of the time it used to.
Advantages of Big Data Analytics
Companies across various industries are leveraging Big Data to promote data-driven decision-making. Some benefits of Big Data Analytics include:
- Data accumulation from different sources. Including the internet, online stores sites, social media, databases, and other third-party sources.
- Identification of problems that enhance business decisions.
- Facilitation of service delivery to meet client expectations.
- Real-time responses to customer queries, and grievances.
- Cost optimization by helping companies leverage big data to predict product trends and take critical measures to reduce losses.
- Business efficiency is encouraged by accumulating large amounts of valuable customer data and generating feedback which can help develop personalized products and services.
- Innovation Insights can help tweak business strategies, develop new products and services, optimize delivery, and increase productivity.
Businesses have primarily leveraged big Data Analytics, but other sectors have also benefited. For example, in healthcare, many states are now using big data to predict and prevent epidemics, cure diseases, cut down costs, etc.
The data also establishes efficient treatment models. With Big Data, comprehensive reports get generated and converted into relevant insights to provide better care. In education, Big Data can enable teachers to measure, monitor, and respond in real-time to student’s understanding of the material.