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Hadoop

Hadoop

Overview

What is Hadoop?

Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.

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Recent Reviews

TrustRadius Insights

Hadoop has been widely adopted by organizations for various use cases. One of its key use cases is in storing and analyzing log data, …
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Hadoop Review

7 out of 10
May 16, 2018
Incentivized
It is massively being used in our organization for data storage, data backup, and machine learning analytics. Managing vast amounts of …
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Product Demos

Installation of Apache Hadoop 2.x or Cloudera CDH5 on Ubuntu | Hadoop Practical Demo

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Big Data Complete Course and Hadoop Demo Step by Step | Big Data Tutorial for Beginners | Scaler

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Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop Tutorial | Simplilearn

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Product Details

What is Hadoop?

Hadoop Video

What is Hadoop?

Hadoop Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.

Reviewers rate Data Sources highest, with a score of 8.7.

The most common users of Hadoop are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(270)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

Hadoop has been widely adopted by organizations for various use cases. One of its key use cases is in storing and analyzing log data, financial data from systems like JD Edwards, and retail catalog and session data for an omnichannel experience. Users have found that Hadoop's distributed processing capabilities allow for efficient and cost-effective storage and analysis of large amounts of data. It has been particularly helpful in reducing storage costs and improving performance when dealing with massive data sets. Furthermore, Hadoop enables the creation of a consistent data store that can be integrated across platforms, making it easier for different departments within organizations to collect, store, and analyze data. Users have also leveraged Hadoop to gain insights into business data, analyze patterns, and solve big data modeling problems. The user-friendly nature of Hadoop has made it accessible to users who are not necessarily experts in big data technologies. Additionally, Hadoop is utilized for ETL processing, data streaming, transformation, and querying data using Hive. Its ability to serve as a large volume ETL platform and crunching engine for analytical and statistical models has attracted users who were previously reliant on MySQL data warehouses. They have observed faster query performance with Hadoop compared to traditional solutions. Another significant use case for Hadoop is secure storage without high costs. Hadoop efficiently stores and processes large amounts of data, addressing the problem of secure storage without breaking the bank. Moreover, Hadoop enables parallel processing on large datasets, making it a popular choice for data storage, backup, and machine learning analytics. Organizations have found that it helps maintain and process huge amounts of data efficiently while providing high availability, scalability, and cost efficiency. Hadoop's versatility extends beyond commercial applications—it is also used in research computing clusters to complete tasks faster using the MapReduce framework. Finally, the Systems and IT department relies on Hadoop to create data pipelines and consult on potential projects involving Hadoop. Overall, the use cases of Hadoop span across industries and departments, providing valuable solutions for data collection, storage, and analysis.

Attribute Ratings

Reviews

(1-2 of 2)
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Tushar Kulkarni | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
I have been working with Hadoop since last year. It is very user friendly. Hadoop was used by the data center management team. It allows distributed processing of huge amount of data sets across clusters of computers using simple programming models.
  • It is robust in the sense that any big data applications will continue to run even when individual servers fail.
  • Enormous data can be easily sorted.
  • It can be improved in terms of security.
  • Since it is open source, stability issues must be improved.
Hadoop is really very useful when dealing with big data.
Apache Spark has an in memory processing model, making it powerful for lightning fast data processing. Apache Spark also exposes Scala and Python in APIs which is one of the most commonly used programming languages in data analytic and data processing domains.
No
  • Product Features
  • Product Usability
I used hadoop and found it really useful while working with bigger data sets. I used Hadoop for my project to get insight of different patterns from given data set. It was easy and user friendly.
I'll be looking at scalability, reliability. At the same time it will be good to have small learning curve.
  • Processing huge data sets with good performance
  • Distributed data handling with multiple nodes
  • Small Learning curve
  • Using Hdoop is a heavy weight process
  • Installation is a little tricky for newbees
  • Not suitable for dynamic data sets
Yes, but I don't use it
I found it really useful during my academic projects. Data handling for large data sets was easy with Hadoop. It used to work really fast for bigger data sets. I found it reliable.
Gaurav Kasliwal | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Incentivized
I have been using Hadoop for 2 years and I really find it very useful, especially working with bigger datasets. I have used Hadoop and Mahout for my project to analyze and learn different patterns from Yelp Dataset. It was really very easy and user friendly to use.

  • Scalability. Hadoop is really useful when you are dealing with a bigger system and you want to make your system scalable.
  • Reliable. Very reliable.
  • Fast, Fast Fast!!! Hadoop really works very fast, even with bigger datasets.
  • Development tools are not that easy to use.
  • Learning curve can be reduced. As of now, some skill is a must to use Hadoop.
  • Security. In today's world, security is of prime importance. Hadoop could be made more secure to use.
Hadoop is really useful for larger datasets. It is not very useful when you are dealing with a smaller dataset.
Fast and scalable. More reliable as compared to the other products I have used.
  • Big data analysis
  • Clustering
  • Machine learning problem solving
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