Big Data Analytics Services: Where Do I Begin?

To select the best big data analytic services, it's important to know the analytical and transactional data requirements of your system and select one accordingly.

To simplify, analytical data comes from processing large data from the past while transactional data comes from processing data that’s in the present. While big data continues to grow, not every activity that involves the use of the data is made equal.

Sometimes, utilizing this data is like running a small, but important errand inside a corner store. At other times, it can be going through the stroll of a warehouse and checking the inventory.

The objectives and the technology needed to handle all of the transactional data, as opposed to tools needed for data processing is different. In order to get the best data analytics tool for the job, it’s best to know the right data solution.

We’ll show you how.

Selecting the Right Data Solution

Big data analytics has emerged for interactive workloads and complex, retrospective analysis of larger datasets. IBM and Mongo DB, both large players within the big data analysis space, have some key insights into the difference of the two.

Here’s a quick overview:

Based on IBM, NoSQL systems such as key-value stores and document databases are popular solutions for scalable and fast operational databases. With the right NoSQL database, the transactions are processed faster.

Also, the system is processing thousands of small transactions during hours of peak activity. Thus, transactions per second are viewed as a better indicator of performance than the response time.

MapReduce and Massively Parallel Processing databases - which includes variants like Hadoop - are important solutions in the analytical space. In fact, there are emerging solutions that are designed to meet an enterprise's needs by analyzing data across NoSQL and SQL, presenting MapReduce in one single analytics platform.

How Big Data Analytics Services Helped Businesses

• Disease Management: Analytics have helped with testing the causes of diseases and analyzing various diseases. Also, it improves the accuracy in finding new medical solutions.
• Securing Data: Everyday, the industry faces a new cybersecurity challenge. Big data analytics helps physicians and healthcare organizations to protect their patients’ data. It does this by identifying and responding to breaches, and protecting your medical devices from attacks.
• Supporting Clinical Decisions: The right clinical decisions increases the quality of enhancing healthcare services outcomes. With big data, it aids this process by giving the right information to the right patients with customized health care. And, it enables physicians, doctors, and patients to find out any specific medical information.
• Advancing Treatment Process: Through the use of big data, doctors can explore previous diseases and come up with medical solutions to treat their patients. It allows physicians to treat symptoms instead of using the traditional disease-based management process.

Conclusion

In the end, it's up to you to decide what big data analytics services are best for your business. With analytical data, you can find patterns based off of the previous history and make future predictions. But, operational data helps when you need thousands of transactions processed at once. Pick which one is best for your project and watch the results you'll get.

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