In my previous article I have given the details about the Hadoop framework with its multiple types and explained about those types. In this article I would like to throw light on the Usages of Hadoop as well as where exactly you can use Hadoop. In real industry also many times architects face the question whether to use Hadoop or not. The article will give answer of all the questions and also the Usages of Hadoop with examples. The article contains following topics :
- What is Hadoop?
- What are Usages of Hadoop with Examples
- Where not to use Hadoop
What is Hadoop?
Hadoop is a software tool that uses a network of many computers to handle problems requiring a significant quantity of computation and data. Because the data can be structured or unstructured, it offers more flexibility for gathering, processing, analyzing, and managing data. It has an open-source distributed framework for the big data application’s distributed storage, management, and processing in scalable server clusters.
Big Data, or unstructured, structured, and semi-structured data volumes of exceptionally high sizes, can be processed, handled, and combined using Hadoop. Big Data has a solution in Hadoop. Hadoop seeks to take advantage of the opportunities offered by big data while overcoming any obstacles. It is a Java-based open-source programming framework that controls how big data sets are processed in a distributed or clustered setting.
What are Usages of Hadoop with Examples
In this section we will see the multiple Usages of Hadoop with examples.
1.Hadoop For Processing Big Data
Hadoop is for you if your data is extremely large—we’re talking at least terabytes or petabytes of data. There are many alternative solutions available for other smaller data sets (think megabytes) with significantly cheaper implementation and maintenance c sts (e.g., various RDBMs and NoSQL database systems). Your data collection may not be particularly huge right now, but when it grows as
a result of numerous events, this could change. In this situation, careful planning might be necessary, especially if you want to have access to all the raw data at all times for adaptable data processing.
2.For parallel data processing
The normal Hadoop methodology does not function well for any graph-based data processing (i.e., a complicated network of data dependent on other data). Nevertheless, the related Apache Tez framework does permit the use of a graph-based strategy rather than the more linear MapReduce workflow for data
3.For Storage of Diverse Set of Data
Hadoop can store and interpret any file data, no matter how big or tiny, whether they are binary files like pictures or plain text files, or even numerous iterations of a single data format across time. How you handle and examine your Hadoop data can be changed at any time. Instead of using slow and/or complicated standard data migrations, this adaptable technique enables new advancements
while processing enormous amounts of data. Data lakes are the name given to these kinds of adaptable data repositories.
When not to use HADOOP?
The important part is where to use Hadoop but the most important points to know where not to use Hadoop . Its important for designing architecture of any application where not to use specific technology. Here are few pointers which will give you idea where not to use Hadoop ,
- Hadoop processes lengthy tasks over significant amounts of data in batches (not all at once!). Compared to a relational database query on a few tables, these jobs will take much longer to process. Hadoop jobs sometimes take hours or even days to complete, especially when processing extremely big data sets.
- The optimum algorithm for your data processing needs may not necessarily be MapReduce. Every MapReduce operation ought to be independent of every other activity. The MapReduce programming approach might not be the ideal choice if the operation has to know a lot of data from jobs that have already been processed (shared state).
- Hadoop should not be used for a relational database because of its very slow response times.
What are Advantages of Hadoop?
- Because it can store and distribute very big data sets across hundreds of cheap computers that work in parallel, Hadoop is a highly scalable storage platform.
- Hadoop is specifically made to manage the enormous amount of data in the petabyte range.
- The cost savings are astounding: Hadoop provides computational and storage capabilities for hundreds of pounds per terabyte, as opposed to thousands or tens of thousands of pounds per terabyte.
- Hadoop can handle both semi-structured and un-structured data.
- Log processing, data warehousing, consumer strategy analysis, and fraud detection are just a few of the many uses for Hadoop.
What are Disadvantages of Hadoop?
- Hadoop is complex and difficult to manage.
- It is not applicable for real-time and small applications.
- Hadoop’s programming model is extremely constrained.
- Hadoop lacks network- or storage-level encryption.
- Hadoop has an inherent redundancy that duplicates data, necessitating extra storage space.
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