5 advantages of Hadoop architecture

Big Data's exponential growth is independent of Hadoop's existence , but without this open source software, it is difficult, if not impossible, to conceive of both storage and value extraction and processing of big data at low cost.
To analyze Big Data without Hadoop, that is, to take advantage of the strategic advantages that this implies for science and also for organizations in general, it would be necessary to find another technology that would allow it to be done efficiently. Or perhaps we should say better that we should create it, although it would surely be complicated if it could offer all its advantages. Join the Hadoop training in Bangalore to get profound knowledge in the web development tools.
Not surprisingly, the Hadoop architecture has features that are perfectly adapted to the needs of the Big Data universe, both for storage and to allow file sharing and the possibility of carrying out heterogeneous data analysis quickly , flexible, scalable, low cost and fault resistant.
The strengths of Hadoop architecturer
Hadoop's architecture enables effective analysis of unstructured big data, adding value that can help you make strategic decisions, improve production processes, save costs, follow up on customer feedback, or draw conclusions scientific, say.
Its scalable technology makes it possible, its speed (not in real time, at least not without help, such as the one provided by Spark), flexibility, among other strengths. If we have to point out its five main advantages , they would be the following:
1. Highly scalable technology: A Hadoop cluster can grow simply by adding new nodes. It is not necessary to make adjustments that modify the initial structure. Therefore, it allows us easy growth, without being tied to the initial characteristics of the design, making use of dozens of low-cost servers that, unlike the relational database, cannot scale. Thanks to MapReduce's distributed processing, files are easily divided into blocks.
2. Low-cost storage: Information is not stored by default, in rows and columns, as is the case with traditional databases, but Hadoop assigns categorized data through thousands of cheap computers, and this is a great saving. Only then does it become feasible. Otherwise, we would not be able to work with large volumes of data, since the cost would be extremely high, unaffordable for the vast majority of companies.
3. Flexibility: By increasing the number of system nodes we also gain in storage and processing capacity. In turn, it is possible to add or access new and different data sources (structured, semi-structured and unstructured), while there is the possibility of adapting accessory tools that work in the Hadoop environment and help in the design of processes, integration or improve other aspects.
4. Speed: Low cost, scalability and flexibility will be of little use to us if the result is not reasonably fast. Fortunately, Hadoop also allows for very fast processing and analysis.
5. Fault tolerant: Hadoop is a technology that makes it easy to store large volumes of information, which in turn allows you to retrieve data safely. If a computer goes down, another copy is always available, making data recovery possible in the event of a crash.