Data Analysis
Database management

The 5 Rules of Big Data Analysis from Data Management Services

We need to understand the basic functionalities of data management services before we take a plunge into the world of big data analysis. The boundaries of data management are shifting. It is no longer restricted to the categories of storing, processing, creating or deleting data. Instead, the services are focusing their attention on the users who will be directly influenced by the data. One can expect better decisions from a better data management that will support more business processes and explore new opportunities for analysis. In addition to a shift in the style of operation, the world of data management is witnessing a defining change with the coming of latest concepts and technologies like ‘Big Data’.

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What is Big Data?

Big data and Hadoop technologies have made works easier for giant enterprises. A big data platform is capable of storing enormous amount of data which may encompass diverse subjects. Apart from this these techniques enable new styles of data analysis. Among multiple benefits, the 2 most conspicuous ones are cost control and a proper management of performance. To successfully take up the challenges of different processing, the professional data scientists recruited by management services must follow 5 rules of big data.


The 5 Rules

1. The first rule states that accessing the batch and real-time processes is a must. Three Vs that inspire Hadoop adoption are data volume which the database needs to handle, the source varieties for finding the correlation between signals and sources; audio, video or other data types which are non-relational. The velocity response required for the data analyses is under a millisecond. The real-time requirements should be reviewed in use cases and for the next rule, a set of new data become the resources.

2. Meeting fresh data streams is not a new thing in the world of big data. Scalable capture not only demands multiple servers but should also have the capability of handling enormous workload. In addition, there is the need of right tools for handling thousands of source mechanisms.

3. The rule three deals with management and merger of data to Hadoop. It is not realistic to just store the data. One must keep it in mind that the processing time should be minimised as much as is possible. The time to process Big Data is quite big. After all, real-time data analytics, aggregations, sampling etc operations performed on a web browser are those activities which should be finished within a few seconds.

4. The fourth rule states that real-time data transformation and ingestion should go hand in hand. De-normalization of data structures is a crucial step of the big data structures.

5. Analysis, enhancement and leverage, these are the three points which should be always kept in mind while dealing with big data resources.

As the providers for data management services recruit efficient professionals, big enterprises could focus its attention on other vital business aspects. Often the complexities of managing big data sources could steal the peace of mind so it is better to leave the matters to the professionals.

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