Big Data best practices: top 5 principles

Big Data is a growing field in IT, which is exponentially developing within organizations. With large chunks of data, specific methods and tools should be elaborated to split and aggregate it. Large datasets go through the specific lifecycle from ingestion to data visualization where finally, the data is cleaned, reduced, and processed for further use. Without a full understanding of different big data methods, the situation might get out of control, that is why one should make decisions rationally before the data is processed and visualized to avoid any inconsistencies.

The most common challenge arising within organizations is the problem that sometimes the data is gathered incorrectly because of the wrong methods used or when it is not smoothly processed during its usual lifecycle. It might happen when people handling big data made mistakes during the metrics process or they do not have enough experience at providing data veracity and ultimately, value. In this article, we would underline the most common big data practices, which play a vital role in keeping business afloat .

1. Identify your business goals before conducting analytics

2. Choose the best strategy and encourage team collaboration

Check the validity of your data on time before ingesting it into the system is essential to avoid any extra work, return to the initial process, and correct things over and over again . It is important to check the collected information and gain more insights during the project.

3. Begin from small projects and use Agile approach to ensure high quality

Start from a small pilot project and focus on the areas, which might go wrong. To avoid any problems, establish a method if any problem arises. One of the most common techniques is an Agile approach, which implies breaking project on phases and adopting new client’s changes during the process of development. In this case, data big analysts might test the data several times per week to ensure it is a right for further computing.

4. Select the appropriate technology tools based on the data scope and methods

Choosing a technology depends on the method you will apply. Therefore, in the case of real-time processing, you might go for Apache Spark, as it computes all data in RAM in an efficient way. If you deal with batch processing, you can enjoy the benefits of Hadoop, which is a highly scalable platform for processing data controlled by cheap servers.

5. Opt for cloud solutions and comply with GDPR for higher security

Data privacy is another aspect, which requires to pay more attention to those who have access to corporate data and which one should be strictly accessed by a particular group of people. One should define which data should be kept in the public cloud and which one — on-premises.

Summary

Originally published at https://agiliway.com on August 30, 2019.

Custom software development company offering a wide range of IT Consulting, Web and Mobile development, Quality Management, BI and BigData services.

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