Top 10 Features of Big Data Hadoop
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In this Hadoop Tutorial, we will discuss 10 best features of Hadoop. If you are not familiar with Apache Hadoop, so you can refer our Hadoop Introduction blog to get detailed knowledge of Apache Hadoop framework.
In this blog, we are going to over most important features of Big data Hadoop such as Hadoop Fault Tolerance, Distributed Processing in Hadoop, Scalability, Reliability, High Availability, Economic, Flexibility, Data locality in Hadoop.
2. Hadoop Introduction
Hadoop is an open source software framework that supports distributed storage and processing of huge amount of data set. It is most powerful big data tool in the market because of its features. Features like Fault tolerance, Reliability, High Availability etc.
- HDFS – World most reliable storage layer
- MapReduce – Distributed processing layer
- YARN – Resource management layer
3. Important Features of Big data Hadoop
There are so many features that Apache Hadoop provides. Let’s discuss these features of Hadoop in detail.
3.1. Open source
It is an open source Java-based programming framework. Open source means it is freely available and even we can change its source code as per your requirements.
3.2. Fault Tolerance
Hadoop control faults by the process of replica creation. When client stores a file in HDFS, Hadoop framework divide the file into blocks. Then client distributes data blocks across different machines present in HDFS cluster.
And, then create the replica of each block is on other machines present in the cluster. HDFS, by default, creates 3 copies of a block on other machines present in the cluster.
If any machine in the cluster goes down or fails due to unfavorable conditions. Then also, the user can easily access that data from other machines.
3.3. Distributed Processing
Hadoop stores huge amount of data in a distributed manner in HDFS. Process the data in parallel on a cluster of nodes.
Hadoop is an open-source platform. This makes it extremely scalable platform. So, new nodes can be easily added without any downtime. Hadoop provides horizontal scalability so new node added on the fly model to the system. In Apache hadoop, applications run on more than thousands of node.
Data is reliably stored on the cluster of machines despite machine failure due to replication of data. So, if any of the nodes fails, then also we can store data reliably.
3.6. High Availability
Due to multiple copies of data, data is highly available and accessible despite hardware failure. So, any machine goes down data can be retrieved from the other path. Learn Hadoop High Availability feature in detail.
Hadoop is not very expensive as it runs on the cluster of commodity hardware. As we are using low-cost commodity hardware, we don’t need to spend a huge amount of money for scaling out your Hadoop cluster.
Hadoop is very flexible in terms of ability to deal with all kinds of data. It deals with structured, semi-structured or unstructured.
3.9. Easy to use
No need of client to deal with distributed computing, the framework takes care of all the things. So it is easy to use.
3.10. Data locality
It refers to the ability to move the computation close to where actual data resides on the node. Instead of moving data to computation. This minimizes network congestion and increases the over throughput of the system. Learn more about Data Locality.
In conclusion, we can say, Hadoop is highly fault-tolerant. It reliably stores huge amount of data despite hardware failure. It provides High scalability and high availability.
Hadoop is cost efficient as it runs on a cluster of commodity hardware. Hadoop work on Data locality as moving computation is cheaper than moving data. All these features of Big data Hadoop make it powerful for the Big data processing.