Hadoop

Hadoop

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Score 8.5 out of 100
Apache Hadoop

Overview

Recent Reviews

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 …
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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 …
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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 …
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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 …
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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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Product Details

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 Video

What is Hadoop?

Hadoop Integrations

  • Sematext Infrastructure Monitoring (formerly Sematext SPM)

Hadoop Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo

Comparisons

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Frequently Asked Questions

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.

What is Hadoop's best feature?

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

Who uses Hadoop?

The most common users of Hadoop are from Enterprises (1,001+ employees) and the Information Technology & Services industry.

Reviews

(1-25 of 37)
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Kunal Sonalkar | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Hadoop is very well suited for big data modeling problems in various industries like finance, insurance, healthcare, automobiles, CRM, etc. In every industry where you need data analysis in real time, Hadoop is a perfect fit in terms of storage, analysis, retrieval, and processing. It won't be a very good tool to perform ETL (Extract Transform Load) techniques though.
Chantel Moreno | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Apache Hadoop is majorly suited for companies that have large amounts of unstructured data flow like advertising and even web traffic so I feel that Hadoop is a great option when you have the extra bulk of data that is required to be stored and processed on a continuous basis. Moreover, I do recommend Hadoop but at the same time, I would also hope and suggest that the software of Hadoop gets supplemented with a faster and interactive database so that the overall querying service gets better.
Peter Suter | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Altogether, I want to say that Apache Hadoop is well-suited to a larger and unstructured data flow like an aggregation of web traffic or even advertising. I think Apache Hadoop is great when you literally have petabytes of data that need to be stored and processed on an ongoing basis. Also, I would recommend that the software should be supplemented with a faster and interactive database for a better querying service. Lastly, it's very cost-effective so it is good to give it a shot before coming to any conclusion.
Score 7 out of 10
Vetted Review
Verified User
Review Source
If you have petabytes of data that you need to store and process on a regular basis and don't mind having to wait minutes for your queries to run, Apache Hadoop is great for that use case. I would supplement it with another faster interactive database for interactive querying.
Score 7 out of 10
Vetted Review
Verified User
Review Source
Apache Hadoop (and its subsequent add-ons) are well-suited to larger, unstructured data flows, such as aggregation of web traffic or advertising. Geospatial algorithms and their outputs are well-suited for this kind of aggregation as structuring that data is challenging, but leaving it unstructured and performing queries as-needed is a better fit for most business models. With the advent of data science, I would expect Hadoop fits a LOT of their initial outputs quite well.
Blake Baron | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Need cheap enterprise-level storage for data that is necessary to keep but isn't regularly accessed? Hadoop is the option for you. If you regularly have analysts or apps accessing the data warehouse, look for something more premium such as Teradata. The good news is that general SQL knowledge transfers well to this warehouse.
Gene Baker | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
Hadoop is easy to use. It is a scalable and cost-effective solution for working with large data sets. Hadoop accepts data from a variety of disparate data sources, such as social media feeds, structured or unstructured data, XML, text files, images, etc. Hadoop is also highly available and fault-tolerant, supporting multiple standby NameNodes. The performance of Hadoop is also good because it stores data in a distributed fashion allowing for distributed processing and lower run times. And Hadoop is open-source, making the source code available for modification if necessary. Hadoop also supports multiple languages like C/C++, Python, and Groovy.
Mark McCully | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Hadoop is well-suited to enterprise-class data lakes, or large data repositories that require high-availability and super-fast access. Hadoop lends itself to administrators that are well versed in Linux as well. Hadoop is not well suited to situations that don't care about high-availability or don't have any Linux or Hadoop admin available either.
Score 8 out of 10
Vetted Review
Verified User
Review Source
Massive processing in a distributed environment with data that can be distributed. Research environments. Lab environments would also be a good use for Hadoop. Hadoop can also be used in support of Spark environments and used by Frameworks if deployed properly. The best scenario is with a Data Scientist that understands how to program appropriately.
May 16, 2018

Hadoop Review

Kartik Chavan | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Hadoop helps us tackle our problem of maintaining and processing a huge amount of data efficiently. High availability, scalability and cost efficiency are the main considerations for implementing Hadoop as one of the core solutions in our big-data infrastructure. Where relational databases fall short with regard to tuning and performance, Hadoop rises to the occasion and allows for massive customization leveraging the different tools and modules. We use Hadoop to input raw data and add layers of consolidation or analysis to make business decisions about disparate data points.

Bharadwaj (Brad) Chivukula | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
  • Less appropriate for small data sets
  • Works well for scenarios with bulk amount of data. They can surely go for Hadoop file system, having offline applications
  • It's not an instant querying software like SQL; so if your application can wait on the crunching of data, then use it
  • Not for real-time applications
January 04, 2018

Hadoop is pretty Badass

Score 9 out of 10
Vetted Review
Verified User
Review Source
When we have data coming in from various sources, using hadoop is a good call. Its a good central station to take a good look at your data and see what needs to be done.
Hadoop should not be used directly for Real time Analytics. HDFS should be used to store data and we could use Hive to query the files.
Hadoop needs to be understood thoroughly even before attempting to use it for data warehousing needs. So you may need to take stock of what Hadoop provides, and read up on its accompanying tools to see what fits your needs.
Johanes Siregar | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
Hadoop is well suited for internal projects in a secure environment without any external exposure. It also excels well in storing and processing large amounts of data. It is also suitable to be implemented as a data repository for data-intensive applications which require high data availability, a significant amount of memory and huge processing power. However, it is not appropriate to implement as a near real-time solution which needs a high response time with a high number of high transactions per seconds.
Score 10 out of 10
Vetted Review
Verified User
Review Source
Hadoop is well suited for organizations with a lot of data, trying to justify business decisions with data-driven KPIs and milestones. This tool is best utilized by engineers with data modeling experience and a high-level understanding of how the different data points can be used and correlated. It will be challenging for people with limited knowledge of the business and how data points are created.
Mark Gargiulo | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
Hadoop is not for the faint of heart and is not a technology per se but an ecosystem of disparate technologies sitting on top of HDFS. It is certainly powerful but if, like me, you were handed this with no prior knowledge or experience using or administering this ecosystem the learning curve can be significant and ongoing having said that I don't think currently there are many other opensource technologies that can provide the flexibility in the "big data" arena especially for ETL or machine learning.
Muhammad Fazalul Rahman | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
If the user is trying to complete a task quickly and efficiently, then Hadoop is the best option for them. However, it may happen that the deadline for the submission is close and the user has little or no knowledge of Hadoop. In this case, it is easier not to use hadoop since it takes time to learn.
Tom Thomas | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
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.
February 23, 2016

Hadoop quick review

Score 9 out of 10
Vetted Review
Verified User
Review Source
Data is growing and grows fast. A relationship database can't hold this requirement any more. Real-time applications and distributed design are required for highly scalability and fault tolerance.
Mrugen Deshmukh | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
1. How large are your data sets? If your answer is few gigabytes, Hadoop may be overkill for your needs.
2. Do you require real-time analytical processing? If yes, Hadoop's map reduce may not be a great asset in that scenario.
3. Do you want to want to process data in a batch processing fashion and scale for TeraBytes size clusters? Hadoop is definitely a great fit for your use case.