What You Need to Know About Big Data Management

 Jenn Livingston

What You Should Know about Big Data Management

As more and more companies adopt platforms for big data, there is growing concern that the development of applications may suffer from the inadequacy of good practices for the management of the data that runs those particular applications. Whenever there is a debate about the control of big data and how it is related to big data platforms, it becomes clear that there is a need for the development of new tools and processes for big data management such as Apache Kafka. This has been the case for companies which are combining Hadoop and the commodity hardware.

In this article, we have looked at the things that every business enterprise needs to know about the management of big data which will ensure trust and consistency in the results of your business analytics.

Companies can manage their big data by themselves

One of the key principles of big data is availability. This is being available in the sense that a company can access massive volumes of data sets in their original formats. The business managers and owners of today have a characteristic of being more adept than those in the last century. Corporate managers want data fed to them directly in its raw form rather than having it delivered to them via a data store’s operational chain, data marts or data warehouses. Company users are willing to have the data sources scanned and prepare their analysis and reports based on their own needs. There are two implications with the management of big data that come with the support of a business’ self-service for big data:

• Data preparation tools are going to be required by the business users to have the numerous data sets assembled and presented for analysis.

• Business users have to peruse the data to permit the discovery of data independently.

Quality lies in the beholder’s eyes

Data cleansing and standardization in traditional systems is applied before the data can be stored in the models that were previously defined. One of the outcomes with big data is that the provision of the data in its original format will mean that there are no standardizations or cleansing that is applied during the capture of data sets. While the mode of data use provides excellent freedom, applying any necessary data transformations becomes the responsibility of the user. Data sets can always be used for various purposes as long as there is no conflict between different user transformations. This means that the company has to put in place different methods to manage the multiple ways and alterations to ensure that there is no conflict. The management of big data must put in place systems to record user transformations, support interpretations for coherent data and ensure that they are consistent.

Understanding big data architecture improved business performance

Platforms of big data strongly rely on storage nodes and commodity processing for the parallel computation of data results by the use of distributed storage. You may be surprised by the poor response time of the system if you do not internalize the details of any SQL language for big data management. For instance, when using complex JOINs, it is a requirement that you broadcast chunks of data sets to all the nodes of computing. This will lead to the injection of large amounts of data into the network which goes a long way in improving performance. Understanding how queries are optimized using data execution models and how to organize data using the big data architecture will enable you to write applications at a much higher speed.

Big data management considerations

Big data management not only entails a new breed of processes and technologies, to enable greater data usability and accessibility, it also eliminates many of the traditional architecture and data modeling approaches. A big data management system in a company must embrace the preparation of data, data discovery, semantic management of metadata, data cleansing and standardization, engines for stream processing and enhanced accessibility to self-service data.

Domain: Business
Category: Management

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