Big data is all about lots and lots of data! The volume of data running through any business every day is so massive that traditional data management tools are unable to store that quantity of data. 

The data can be very complicated in that it encompasses structured, semi-structured, and unstructured data; growing exponentially over time.  

Big Data – Research Numbers

Here is what a research study by IDC has indicated how much data will be generated by the year 2025:

So, now that you have all this data, what do you do with it? The world is now starting to tap into the value of unanalyzed big data.

Also, Forrester reports that between 60% and 73% of all data within a business goes unused by analytics. This number is problematic at many levels and has encouraged more data engineering and data analytics.

The Importance of Deriving Value from Big Data

So, what is the importance of big data analytics? The value of big data is enormous and when tapped into efficiently, can help transform a business.  

Big data analytics helps derive insights from big data but it is not a straightforward process. However, companies have started deploying teams to strategize big data analytics – hiring big data engineers, big data analysts, etc.

With the help of the analyzed data, businesses can discover new revenue opportunities. This can be in several areas such as finding effective marketing strategies, better customer service and gaining an advantage over business competitors in general. The result of a big data analytics process helps answer a lot of questions about the overall health of your business. And based on that, smarter actions and strategies can be taken and developed, resulting in greater efficiency, a healthier bottom line, and happier customers.

Information management systems that store Big Data

Several Information management systems handle big data. These systems can not only manage to store the data but also have high processing power. Thus, it can be used for predictive analytics, data mining, machine learning, etc.

 Some examples are,

• Teradata database- ideal for data warehousing needs
• Cassandra – the NoSQL database
• Oracle Big Data – a Hadoop-based data lake
• Hbase – the non-relational database data store for Hadoop, etc.

These databases and data warehouses store and help manage the vast amount of structured and unstructured data that make it possible to prepare the data for analytics. 

More Big Data Technologies

The big data technologies help in handling and managing the big data sets and also help identify the trends and patterns within them. Some of the popular tools are:

• CDH – Cloudera Distribution for Hadoop

This data storage framework is the foundation file system that Hadoop depends on to store big data on the cluster nodes. This data management platform can collect, process, and manage by administering, discovering, and modeling the data. It helps you perform end-to-end big data workflows.

• MongoDB –

This tool is a NoSQL database and is open source as well, supporting various operating systems. Special features include data aggregation, ad-hoc queries generation, indexing, data replication, load balancing, etc.

• Apache Storm

This distributed real-time stream processing computation framework is an open-source tool, built on the concepts of spouts and bolts. It processes real-time, unbounded data streams in a micro-batch processing model.

Cloud – the Best Platform for Big data

Big data and cloud support each other. The rapid growth of data is encouraging enterprises to rely on Cloud. Cloud can be deployed at the following levels – public cloud, private cloud, and hybrid cloud.  IDC predicts that by 2025, 49% of the world’s stored data will reside in the public cloud. Hence, there is a critical need to move up to the cloud infrastructure.

For the same reasons that the cloud is highly and easily scalable, provides flexibility, high performance, and security, also makes it ideal for big data analytics. Cloud hosting provides readily available infrastructure as well, at any time. 

Tools that are integrated within the cloud execution platforms, help your project perform tests in the cloud on cross platforms, browsers, devices, etc. Today, there is a high number of devices being used all over the world. Businesses are finding the cloud to be a viable option that enables fast, ubiquitous access to business data. Because the cloud is powerful in terms of connectivity features, and high performance is convenient to utilize, cloud provider data centers are now preferred to store, manage, maintain, and support consumer and business data.  

Cloud computing is economical and has low maintenance costs as well. Today, there is more reliance and trust in cloud services as compared to storing data locally. This is primarily because big data can be stored on the cloud either by keeping it on the big data supported database systems such as Amazon S3 among others. The cloud has enabled centralized access to the data, thus promoting big data analytics and AI. 

As companies rely on making the cloud the new enterprise data repository, it is now the core of advanced technologies. Some of the popular cloud providers include the following which provides IT projects and environments within the cloud, pooling and sharing scalable resources across the network:

• AWS
• Google Cloud
• IBM Cloud
• Microsoft Azure, etc.

Big Data Analytics – A business scenario

Here is an example of a big data analytics scenario. Let us assume you are the business owner of a popular decade-old, large retail store. Now, in order to increase sales and improve your business, what are the strategies that you would incorporate?

As the retail store is an established business, there would already be a lot of existing sales data that could be tapped into for analytics. Considering that it’s a popular store, the data would already be growing exponentially; providing an excellent opportunity to tap into big data technologies.

To do so, you could get big data experts to help you store, and derive value out of this data. With the help of big data analytics, your business team can upgrade the future sales portfolio in several aspects. For example, the big data analytics report may:

• Help extract insights – check for high demand during a particular time of the year, and plan for inventory accordingly. This will avoid understocking or overstocking items.
• Help understand patterns and trends – perhaps a pattern may be identified which indicates that when customers buy a particular product; oftentimes, many of them tend to buy another related product. In this case, the business team may plan to place those related products together in the store.
• Help identify profitable and non-profitable products based on buying trends – and build marketing strategies accordingly.

Similar to these, there are several big data application examples in the real world, in the domains of healthcare, education, e-commerce, finance, travel industry, etc.

 Big Data Testing

A big data QA tester needs to perform database testing, performance testing, functional testing, etc., on big data. Yes, the strategies for big data testing will be the same as for testing on smaller volumes of data. However, the technologies here will be more advanced. In similar ways, we are encouraged to use test automation tools to build an efficient test automation framework. Some of the automation testing tools that big data projects can rely on are TestingWhiz, TestProject, etc., which have connectors to big data technologies Hadoop, Teradata, NoSQL databases.

Automated big data functional testing tools should be capable of testing on any volume of data, a variety of data, and velocity of data. They should be able to be performed with higher efficiency compared to the traditional testing tools. The QA automation tools should be robust enough to test on databases that support big data; for example, Mongo DB, Microsoft SQL Azure, PostgreSQL, IBM DB2, etc. They ought to be able to verify the health of the databases used, checking for data integrity, etc.

Powerful automation tools also enable integration with external test management tools to help you manage the test activities. They also help to derive valuable insights and generate useful reports from them, optimizing the whole testing process. If they can be integrated with CI-CD DevOps tools like Jenkins, Bamboo, Azure, etc., it is a bonus. In this way, by integrating within the DevOps toolchain, you can trigger automation of build deployment and testing on a continuous CI-CD basis. And, in turn, help accelerate the project’s agile and regression release cycles. Also, this, it helps to make bug identification and test management easier. A QA engineer needs to upskill in these big data technologies.

Conclusion

Since big data analytics is associated with the analysis of hidden patterns, trends, correlations, and extraction of information from big data, they have been highly beneficial for all kinds of businesses, researchers, etc., in today’s fast-advancing world. There is a tremendous amount of cost reduction as well as faster, better, and smarter decision-making with the help of big data technologies.

With big data technologies evolving and improving, we should start tapping into big data analytics scenarios and use them to a business’ advantage. Organizations will need to start focusing on revamping their storage architectures to monetize data for analytics. Companies that want to be relevant and catch up with Industry 4.0 will need to understand the role that data plays in their organization. They’ll need to tap into their unanalyzed data and leverage the cloud to help support it. #KhabarLive #hydnews