Have you ever thought why companies adopt Hadoop as an answer to Big Data Problems?
In this part of the tutorial, we are going to study the essential features of Hadoop that make Hadoop so popular. The article count on various Hadoop features like open source, scalability, fault tolerance, high availability, etc. that make Hadoop the foremost popular big data tool.
- 1 Features of Hadoop
- 1.1 1. Hadoop is Open Source
- 1.2 2. Hadoop cluster is Highly Scalable
- 1.3 3. Hadoop provides Fault Tolerance
- 1.4 4. Hadoop provides High Availability
- 1.5 5. Hadoop is very Cost-Effective
- 1.6 6. Hadoop is Faster in Data Processing
- 1.7 7. Hadoop is based on Data Locality concept
- 1.8 8. Hadoop provides Feasibility
- 1.9 9. Hadoop is Easy to use
- 1.10 10. Hadoop ensures Data Reliability
- 2 Summary
Features of Hadoop
Apache Hadoop is that the hottest and powerful big data tool, Hadoop provides the world’s most reliable storage layer. In this section of the features of Hadoop, allow us to discuss various key features of Hadoop.
1. Hadoop is Open Source
Hadoop is an open-source project, which means its source code is available free of cost for inspection, modification, and analyses that allows enterprises to modify the code as per their requirements.
2. Hadoop cluster is Highly Scalable
Hadoop cluster is scalable means we will add any number of nodes (horizontal scalable) or increase the hardware capacity of nodes (vertical scalable) to realize high computation power. This provides horizontal also as vertical scalability to the Hadoop framework.
3. Hadoop provides Fault Tolerance
Fault tolerance is the most crucial feature of Hadoop. HDFS in Hadoop 2 uses a replication mechanism to provide fault tolerance.
It creates a copy of each block on the different machines depending on the replication factor (by default, it is 3). So if any machine in a cluster goes down, data will be accessed from other machines containing a copy of similar data.
Hadoop 3 has replaced this replication mechanism by erasure coding. Erasure coding provides the same level of fault tolerance with less space. With Erasure coding, the storage overhead is not more than 50%.
4. Hadoop provides High Availability
This feature of Hadoop ensures the high availability of the data, even in unfavorable conditions.
Due to the fault tolerance feature of Hadoop, if any of the DataNodes goes down, the data is available to the user from different DataNodes containing a copy of the same data.
Also, the high availability Hadoop cluster consists of 2 or more running NameNodes (active and passive) in a hot standby configuration. The active node is the NameNode, which is active. Passive node is the standby node that reads edit logs modification of active NameNode and applies them to its namespace.
If an active node fails, the passive node takes over the responsibility of the active node. Thus even if the NameNode goes down, files are available and accessible to users.
5. Hadoop is very Cost-Effective
Since the Hadoop cluster consists of nodes of commodity hardware that are inexpensive, thus provides a cost-effective solution for storing and processing big data. Being an open-source product, Hadoop doesn’t need any license.
6. Hadoop is Faster in Data Processing
Hadoop stores data within a distributed fashion, which allows data to be processed distributed on a cluster of nodes. Thus it provides lightning-fast processing capability to the Hadoop framework.
7. Hadoop is based on Data Locality concept
Hadoop is popularly known for its data nativity feature means moving computation logic to the data, rather than moving data to the computation logic. This feature of Hadoop reduces the bandwidth usage in a system.
8. Hadoop provides Feasibility
Unlike the traditional system, Hadoop can process unstructured data. Thus provide feasibility to the users to analyze data of any formats and size.
9. Hadoop is Easy to use
Hadoop is easy to use as the clients don’t have to worry about distributing computing. The processing is handled by the framework itself.
10. Hadoop ensures Data Reliability
In Hadoop due to the replication of data in the cluster, data is stored reliably on the cluster machines despite machine failures.
The framework itself provides a mechanism to ensure data reliability by Block Scanner, Volume Scanner, Disk Checker, and Directory Scanner. If your machine goes down or data gets corrupted, then also your data is stored reliably in the cluster and is accessible from the other machine containing a copy of data.
In short, we can say that Hadoop is an open-source framework. Hadoop is known for its fault tolerance and high availability feature. Hadoop clusters are scalable. The Hadoop framework is easy to use.
It ensures fast data processing due to distributed processing. Hadoop is cost-effective. Hadoop data locality feature reduces the bandwidth utilization of the system.