Big data performance management
A Big Data data management system is a generic term that relates to what many organizations need to manage their business in this new era of Big Data.
We are referring to the following. When Hadoop and NoSQL technologies began to be popular, some of their early followers spoke of them as replacements for relational databases. More recently, however, it has become apparent that they can be complementary tools.To maximize the value of Big Data performance management it is ideal to have Hadoop and noSQL, but it is also possible to maintain some parts with relational database systems, provided there is good integration. Supporters of Hadoop and even large companies, analysts and vendors, agree that while relational databases are not ideal for handling large amounts of data, having all of these elements working together is ideal. So, when all these components are working in unison, what do you have?
The answer is simple. You have a Big Data data management system.
We have moved from managing data, to managing Big Data and a relational database management system, to a Big Data data management system that integrates Hadoop, noSQL and your relational data warehouse. Even possibly other data sources.
But do we know what each of these database technologies is and what their specific importance is in this context?
Different elements of a Big Data data management system
Within an information infrastructure, such as a data management system, different technologies coexist. Knowing them allows to better contextualize the operation and capabilities of one of the most important resources that the business has today. These are the following:
Also known as RDBMS in computer jargon (since it is actually a system in itself: Relational Database Management System), it is a fundamental element when structuring data from the Internet. This is achieved thanks to its architecture, considered a standard for database management for decades, and still necessary to be able to work with large volumes of data https://www.enteros.com/mysql-performance-management-tool/ however, as the size of the information contained in it grows, its performance decreases, as it lacks the scalability that would allow it to meet the high level of demand of today's big data work.
With this acronym we refer to what is "Not only SQL". This denomination defines a high performance and much more agile framework in terms of processing for databases. It would be impossible to consider the architecture of a current data management system without this component, since it is the infrastructure best suited to the demands of big data; Amazon or Google are fully aware of this and two clear examples of its practical application. The efficiency of non-SQL databases is partly related to their configuration. The fact is that noSQL databases are not structured, so they manage to deliver speed where relational databases could only provide consistency.
This is achieved due to its distributed character, which allows multiple processing nodes and, sometimes, also different servers, to store the unstructured data. Horizontal scalability is the attribute that makes the main difference with RDBMS.
Probably very few people today have not heard of the Hadoop ecosystem. That's because this element of a data management system is not a database, but a software ecosystem that enables massive parallel computing. Hadoop makes it possible for large volumes of data to be distributed across a network of servers. Within this framework, MapReduce is a critical component, since it takes care of taking data-intensive processes and distributing them to a Hadoop cluster. In this way, it is possible to carry out in a few minutes operations that, without this component, would have taken hours.
Surely now you see the needs of large data processing differently. By gaining an understanding of how a data management system works, you can optimize your investment in technology and choose the most appropriate software and tools to achieve your goals.