Big Data and Hadoop, a tandem that works
Posted: Tue Jan 21, 2025 5:49 am
The Big Data-Hadoop tandem has proven to be revolutionary. The yellow elephant's opensource potential for storing and processing large amounts of data has enormous potential.
The Big Data-Hadoop tandem has proven to be revolutionary . The enormous potential of the yellow elephant's open source to store and process large data at a low cost has undoubtedly marked a before and after in big data analytics.
Of course, this does not mean that it does not have weaknesses . In fact, it has quite a few shortcomings, some of which are temporary, such as the shortage of technicians, and others that can be remedied with new technologies that are still in development or by using add-ons that, for example, allow for real-time analysis.
In one way or another, virtually all of these are solvable problems, and in many cases, it is worth taking up the challenge, since Apache Hadoop allows the processing of large data sets in a distributed fusion database with high performance and reliability .
Its great capabilities for scaling at low cost can lead to drawbacks in the business environment, it is true, but on the other hand Hadoop has become almost synonymous with Big Data . With Hadoop, big data has a meaning, something that is not exclusive to this technology, but obtaining it, getting the most out of it, is within the reach of many.
New call to action
Hadoop extracts value from Big Data
For end users, this is achieved by running applications that run thousands of terabytes of data across thousands of nodes with the goal of extracting value. In short, it's about making data talk. This is how Hadoop brings Big Data down to earth .
In addition to allowing analysis and predictions, it is possible to re-analyze and make that prediction as many times as necessary through processing that analyzes and predicts, without limits, in a constant look forward as a comparative advantage . Only with Hadoop is this possible, although it is a different challenge to do so using the original open source or commercial tools.
The Big Data-Hadoop tandem has proven to be revolutionary . The enormous potential of the yellow elephant's open source to store and process large data at a low cost has undoubtedly marked a before and after in big data analytics.
Of course, this does not mean that it does not have weaknesses . In fact, it has quite a few shortcomings, some of which are temporary, such as the shortage of technicians, and others that can be remedied with new technologies that are still in development or by using add-ons that, for example, allow for real-time analysis.
In one way or another, virtually all of these are solvable problems, and in many cases, it is worth taking up the challenge, since Apache Hadoop allows the processing of large data sets in a distributed fusion database with high performance and reliability .
Its great capabilities for scaling at low cost can lead to drawbacks in the business environment, it is true, but on the other hand Hadoop has become almost synonymous with Big Data . With Hadoop, big data has a meaning, something that is not exclusive to this technology, but obtaining it, getting the most out of it, is within the reach of many.
New call to action
Hadoop extracts value from Big Data
For end users, this is achieved by running applications that run thousands of terabytes of data across thousands of nodes with the goal of extracting value. In short, it's about making data talk. This is how Hadoop brings Big Data down to earth .
In addition to allowing analysis and predictions, it is possible to re-analyze and make that prediction as many times as necessary through processing that analyzes and predicts, without limits, in a constant look forward as a comparative advantage . Only with Hadoop is this possible, although it is a different challenge to do so using the original open source or commercial tools.