Hadoop Reviews & Ratings 2023

Name: What is Hadoop?
Uploaded: 2012-07-14T22:23:27.000Z
Duration: 3 min 7 s
Description: What is Hadoop?

Apache 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.

Hadoop: A Robust Big Data Platform

9 out of 10

April 11, 2022

Hadoop is being used to solve big data modeling problems in our firm. The corporate analytics team uses Hadoop to perform functions like …

Great enterprise tool for handling large data

9 out of 10

August 17, 2021

Apache Hadoop is one of the most effective and efficient software which has been storing and processing an extremely colossal amount of …

Good tool for unstructured data

9 out of 10

July 21, 2021

Apache Hadoop is an open-source software library that is designed for the collection, storage, and analysis of large amounts of data sets. …

Good solution for storing and processing large data

7 out of 10

May 20, 2021

We use Apache Hadoop to store and process large amounts of data (petabytes per day) across thousands of data pipelines. Hadoop works …

Apache Hadoop Can Save on the Headaches

7 out of 10

January 16, 2021

[Apache Hadoop] is being handled as it is (mostly) intended. For large, unstructured data management from our data flows to include …

Hadoop -- Great Value for What You Pay

7 out of 10

September 21, 2020

It's used organization-wide for older data that's not used as frequently. We use Teradata to warehouse our more recent data, but for data …

Fault Tolerance and High Availablility Made Easy with Hadoop

10 out of 10

September 20, 2020

We are using it within my department to process large sets of data that can't be processed in a timely fashion on a single computer or …

Hadoop vs. Alternatives

8 out of 10

June 05, 2019

It is being used at our Fortune 500 clients. It is great for storage, but it is not well understood by the business. The challenge is that …

Hadoop Review

7 out of 10

May 16, 2018

It is massively being used in our organization for data storage, data backup, and machine learning analytics. Managing vast amounts of …

Great Option for Unstructured Data

10 out of 10

March 28, 2018

Used for Massive data collection, storage, and analytics
Used for MapReduce processes, Hive tables, Spark job input, and for backing up data

Hadoop is pretty Badass

9 out of 10

January 04, 2018

Apache Hadoop is a cost effective solution for storing and managing vast amounts of data efficiently. It is dependable and works even when …

Hadoop: Highly available, scalable and cost effective for big data storage and processing.

8 out of 10

December 13, 2017

Currently, there are two directorates using Hadoop for processing a vast amount of data from various data sources in my organization. …

Hadoop for Justifying Business Decisions with Hard Data

10 out of 10

October 24, 2017

Hadoop has been an amazing development in the world of Big Data. Where relational databases fall short with regard to tuning and …

Hadoop review 2346

9 out of 10

September 22, 2017

Hadoop is used to build a data lake where all enterprise data for my entire company can be stored. With data centralization and …

Hadoop for Big Data

10 out of 10

August 24, 2017

[It was used] As a proof of concept to analyze a huge amount of data. We were building a product to analyze huge data and eventually sell …

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Reviewer Pros & Cons

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Capability to collaborate with R Studio. Most of the statistical algorithms can be deployed.
If we can connect Hadoop to Salesforce, it would be a tremendous functionality as most CRM data comes from that channel.

Kunal Sonalkar

Data Research Analyst

Southwest Florida Water Management District (Higher Education, 5001-10,000 employees)

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Frequently Asked Questions

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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May 25, 2016

Hadoop is the Perfect Enterprise tool for Big Data

Tom Thomas

Student Lab Instructor (SLI) for Computer Science II

Rochester Institute of Technology (Higher Education, 1001-5000 employees)

Score 10 out of 10

Vetted Review

Verified User

Use Cases and Deployment Scope

The company I worked at used Hadoop clusters for processing huge datasets. They had several nodes for both production and per-production nodes. It allowed distributed processing of data across several clusters with an easy to use software model. It is used by the Systems and IT department at my company.

Pros and Cons

HDFS provides a very robust and fast data storage system.
Hadoop works well with generic "commodity" hardware negating the need for expensive enterprise grade hardware.
It is mostly unaffected by system and hardware failures of nodes and is self-sustained.

While its open source nature provides a lot of benefits, there are multiple stability issues that arise due to it.
Limited support for interactive analytics.

Likelihood to Recommend

Hadoop is a very powerful tool that can be used in almost any environment where huge scale processing of data across clusters is required. It provides multiple modules such as HDFS and MapReduce that will make managing and analyzing said data reliable and efficient. Hadoop is a new and constantly evolving tool, and hence it needs users to be on top of it all the time.

Return on Investment

Reduced costs of hardware due to support for generic hardware
Improved time and cost of data analysis

Other Software Used

Apache Pig, Apache Hive, Apache Sqoop

Products Replaced

Key Differentiators

Price
Product Features
Product Usability
Product Reputation
Vendor Reputation
Analyst Reports
Third-party Reviews

February 14, 2016

Hadoop an awesome tool for large scale batch processing.

Tushar Kulkarni

Employee in Research & Development

Student (Computer Hardware, 51-200 employees)

Score 10 out of 10

Vetted Review

Verified User

Use Cases and Deployment Scope

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.

Pros and Cons

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.

Likelihood to Recommend

Hadoop is really very useful when dealing with big data.

Alternatives Considered

Apache Spark and Apache Flink

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.

Other Software Used

Apache Spark

Products Replaced

Key Differentiators

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.

Evaluation Lessons Learned

I'll be looking at scalability, reliability. At the same time it will be good to have small learning curve.

Easy Tasks

Processing huge data sets with good performance
Distributed data handling with multiple nodes
Small Learning curve

Difficult Tasks

Using Hdoop is a heavy weight process
Installation is a little tricky for newbees
Not suitable for dynamic data sets

Mobile Interface Availability and Impressions

Yes, but I don't use it

Usability

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.

April 29, 2015

Hadoop for better economy and efficiency

Bhushan Lakhe

Senior Vice President

Ipsos (Information Technology and Services, 10,001+ employees)

Score 7 out of 10

Vetted Review

Verified User

Use Cases and Deployment Scope

Hadoop is used for storing and analyzing log data (logs from warehouse loads or other data processing) as well as storing and retrieving financial data from JD Edwards. It's also planned to be used for archival. Hadoop is used by several departments within our organization. Currently, we are paying a lot of money for hosting historical data and we plan to move that to Hadoop; reducing our storage costs. Also, we got a much better performance out of our Hadoop cluster for processing a large amount of financial data. So, in that senese, Hadoop addressed multiple business problems for us.

Pros and Cons

Hadoop stores and processes unstructured data such as web access logs or logs of data processing very well
Hadoop can be effectively used for archiving; providing a very economic, fast, flexible, scalable and reliable way to store data
Hadoop can be used to store and process a very large amount of data very fast

Security is a piece that's missing from Hadoop - you have to supplement security using Kerberos etc.
Hadoop is not easy to learn - there are various modules with little or no documentation
Hadoop being open-source, testing, quality control and version control are very difficult

Likelihood to Recommend

Hadoop is best suited for warehouse or OLAP processing. It's not suitable for OLTP or small transaction processing

Return on Investment

We had a large ROI due to improved performance and expedited reporting - our clients were happier and business improved
Our storage costs reduced
Our infrastructure costs reduced - we used old hardware for our Hadoop cluster

Alternatives Considered

not applicable - I have not evaluated any other products

Users and Roles

Various - IT, business users, vendors

Support Headcount Required

Hadoop Administrator, Java Developer, Hive deveoper

Business Processes Supported

Use of HDFS / Hive for storage / analysis of data processing logs
Use of HDFS / Hive for storage / analysis of historical financial data
Use of HDFS for Archival

Innovative Uses

Archival
Reporting
ETL

Future Planned Uses

Data transfer
Staging area
Historical reporting

Likelihood to Renew

Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free

Products Replaced

Yes

We replaced 5 Windows based servers by a 10 node CentOS based desktops. Saved a lot on hardware and Windows server licenses

Key Differentiators

Price
Product Features
Product Usability

Price. We saved a lot of money

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