What are the main features of Hadoop? – SVR Technologies

What are the principle key highlights of Hadoop?

Top 8 highlights of Hadoop are:

  • Financially savvy System
  • Substantial Cluster of Nodes
  • Parallel Processing
  • Conveyed Data
  • Programmed Failover Management
  • Information Locality Optimization
  • Heterogeneous Cluster
  • Adaptability


In this Hadoop Tutorial, we will talk about 10 best highlights of Hadoop. In the event that you are not comfortable with Apache Hadoop, so you can allude our Hadoop Introduction blog to get point by point learning of Apache Hadoop structure. In this blog, we are going to over most critical highlights of Big information Hadoop, for example, Hadoop Fault Tolerance, Distributed Processing in Hadoop, Scalability, Reliability, High Availability, Economic, Flexibility, Data area in Hadoop.

1) Cost Effective System

Hadoop structure is a savvy framework, that is, it doesn’t require any costly or specific equipment so as to be actualized. At the end of the day, it tends to be executed on any single equipment. These equipment parts are in fact alluded to as item equipment.

2) Large Cluster of Nodes

It bolsters an extensive bunch of hubs. This implies a Hadoop group can be comprised of a great many hubs. The principle favorable position of this element is that it offers an enormous registering power and a colossal stockpiling framework to the customers.

3) Parallel Processing

It underpins parallel preparing of information. Subsequently, the information can be handled all the while over every one of the hubs in the group. This spares a great deal of time.

4) Distributed Data

Hadoop structure deals with disseminating and part the information over every one of the hubs inside a group. It likewise reproduces the information over the whole group.

5) Automatic Failover Management

In the event that a specific machine inside the bunch bombs then the Hadoop organize replaces that specific machine with another machine. It additionally repeats the setup settings and information from the fizzled machine to the new machine. When this element has been appropriately designed on a group then the administrator require not stress over it.

6) Data Locality Optimization

In a conventional methodology at whatever point a program is executed the information is exchanged from the server farm into the machine where the program is getting executed. For example, accept the information executed in a program is situated in a server farm in the USA and the program that requires this information is in Singapore. Assume the information required is around 1 PB in size. Exchanging tremendous information of this size from USA to Singapore would devour a great deal of transfer speed and time. Hadoop disposes of this issue by exchanging the code which is a couple of megabytes in size. It exchanges this code situated in Singapore to the server farm in USA. At that point it orders and executes the code locally on that information. This procedure spares a great deal of time and transfer speed. It is a standout amongst the most critical highlights of Hadoop.

7) Heterogeneous Cluster

It bolsters heterogeneous bunch. It is additionally a standout amongst the most vital highlights offered by the Hadoop system. A heterogeneous group alludes to a bunch where every hub can be from an alternate seller. Each of these can be running an alternate form and an alternate kind of working framework. For instance, consider a bunch is comprised of four hubs. The primary hub is an IBM machine running on RHEL (Red Hat Enterprise Linux), the second hub is an Intel machine running on UBUNTU Linux, the third hub is an AMD machine running on Fedora Linux, and the last hub is a HP machine running on CENTOS Linux.

8) Scalability

It alludes to the capacity to include or expel the hubs just as including or evacuating the equipment parts to, or, from the group. This is managed without affecting or cutting down the group task. Singular equipment segments like RAM or hard-drive can likewise be included or expelled from a bunch.

Hadoop Introduction

Hadoop is an open source programming structure that underpins dispersed capacity and handling of enormous measure of informational index. It is most dominant huge information device in the market in light of its highlights. Highlights like Fault resilience, Reliability, High Availability and so forth.

Hadoop gives

HDFS – World most dependable stockpiling layer

MapReduce – Distributed preparing layer

YARN – Resource the executives layer

3. Critical Features of Big information Hadoop

There are such a large number of highlights that Apache Hadoop gives. How about we examine these highlights of Hadoop in detail.

3.1. Open source

It is an open source Java-based programming system. Open source implies it is uninhibitedly accessible and even we can change its source code according to your necessities.

3.2. Adaptation to non-critical failure

Hadoop control blames by the procedure of reproduction creation. At the point when customer stores a document in HDFS, Hadoop structure separate the record into squares. At that point customer conveys information obstructs crosswise over various machines present in HDFS group. What’s more, at that point make the reproduction of each square is on different machines present in the group. HDFS, as a matter of course, makes 3 duplicates of a square on different machines present in the group. In the event that any machine in the group goes down or flops because of horrible conditions. At that point likewise, the client can without much of a stretch access that information from different machines.

3.3. Appropriated Processing

Hadoop stores gigantic measure of information in a conveyed way in HDFS. Process the information in parallel on a group of hubs.

3.4. Adaptability

Hadoop is an open-source stage. This makes it amazingly versatile stage. Thus, new hubs can be effectively included with no downtime. Hadoop gives even versatility so new hub included the fly model to the framework. In Apache hadoop, applications keep running on more than a large number of hub.

3.5. Dependability

Information is dependably put away on the group of machines notwithstanding machine disappointment because of replication of information. In this way, in the event that any of the hubs bombs, likewise we can store information dependably.

3.6. High Availability

Because of different duplicates of information, information is exceptionally accessible and available in spite of equipment disappointment. Along these lines, any machine goes down information can be recovered from the other way. Learn Hadoop High Availability include in detail.

3.7. Financial

Hadoop isn’t over the top expensive as it keeps running on the group of ware equipment. As we are utilizing minimal effort ware equipment, we don’t have to spend a colossal measure of cash for scaling out your Hadoop group.

3.8. Adaptability

Hadoop is truly adaptable as far as capacity to manage a wide range of information. It manages organized, semi-organized or unstructured.

3.9. Simple to utilize

No need of customer to manage disseminated figuring, the system deals with every one of the things. So it is anything but difficult to utilize.

3.10. Information territory

It alludes to the capacity to move the calculation near where genuine information lives on the hub. Rather than moving information to calculation. This limits arrange clog and expands the over throughput of the framework. Get familiar with Data Locality.

4. End

All in all, we can say, Hadoop is exceedingly blame tolerant. It dependably stores immense measure of information notwithstanding equipment disappointment. It gives High versatility and high accessibility. Hadoop is cost productive as it keeps running on a group of ware equipment. Hadoop take a shot at Data territory as moving calculation is less expensive than moving information. Every one of these highlights of Big information Hadoop make it amazing for the Big information handling.

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