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, …
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.
Recent Reviews
Product Demos
Installation of Apache Hadoop 2.x or Cloudera CDH5 on Ubuntu | Hadoop Practical Demo
YouTube
Big Data Complete Course and Hadoop Demo Step by Step | Big Data Tutorial for Beginners | Scaler
YouTube
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop Tutorial | Simplilearn
YouTube
Product Details
- About
- Tech Details
- FAQs
What is Hadoop?
Hadoop Video
What is Hadoop?
Hadoop Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
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).
Comparisons
Compare with
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!
- Business Problems Solved
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
(1-16 of 16)Companies can't remove reviews or game the system. Here's why
April 11, 2022
Hadoop: A Robust Big Data Platform
Hadoop is being used to solve big data modeling problems in our firm. The corporate analytics team uses Hadoop to perform functions like data manipulation, information retrieval, data mapping, and statistical modeling. The business problem which it solves is the limitation of CSV/Excel files to handle more than a million rows. Hadoop allows you to process big data and also has connectivity with platforms like R Studio where you can deploy mathematical models.
- Capability to collaborate with R Studio. Most of the statistical algorithms can be deployed.
- Handling Big Data issues like storage, information retrieval, data manipulation, etc.
- Redundant tasks like data wrangling, data processing, and cleaning are more efficient in Hadoop as the processing times are faster.
- Hadoop requires intensive computational platforms like a minimum of 8GB memory and i5 processor. Sometimes the hardware does become a hindrance.
- If we can connect Hadoop to Salesforce, it would be a tremendous functionality as most CRM data comes from that channel.
- It will be good to have some Geo Coding features if someone wants to opt for spatial data analysis using latitudes and longitudes.
August 17, 2021
Great enterprise tool for handling large data
Apache Hadoop is one of the most effective and efficient software which has been storing and processing an extremely colossal amount of data in my company for a long time now. The software Hadoop is primarily used for data collection of large amounts, storage as well as for analytics. From my experience, I have to say that Hadoop is extremely useful and has a reliable plus valid purpose.
- The various modules sometimes are pretty challenging to learn but at the same time, it has made Hadoop easy to implement and perform.
- Hadoop comprises a thoughtful file system which is called as Hadoop Distributed File System that beautifully processes all components and programs.
- Hadoop is also very easy to install so this is also a great aspect of Hadoop as sometimes the installation process is so tricky that the user loses interest.
- Customer support is quick.
- As much as I really appreciate Hadoop there are certain cons attached to it as well. I personally think that Hadoop should work attentively towards their interactive querying platforms which in my opinion is quite slow as compared to other players available in the market.
- Apart from that, a con that I have noticed is that there are many modules that exist in Hadoop so due to the higher number of modules it becomes difficult and time-consuming to learn and ace all of them.
July 21, 2021
Good tool for unstructured data
Apache Hadoop is an open-source software library that is designed for the collection, storage, and analysis of large amounts of data sets. Apache Hadoop’s architecture comprises components that include a distributed file system. This is mostly used for massive data collection, analytics, and storage. Also, having consistent data can be integrated across other platforms and have one single source of truth.
- Apache Hadoop has made managing large amounts of data quite easy.
- The system contains a file system known as HDFS (Hadoop Distributed File System) which processes components and programs.
- The parallel processing tool of this software is also a good aspect of Apache Hadoop.
- It keeps interesting and reliable features and functions.
- Apache Hadoop also has a store of very big data files in machines with high levels of availability.
- I personally feel that Apache Hadoop is slower as compared to other interactive querying platforms. Queries can take up to hours sometimes which can be frustrating and discouraging sometimes.
- Also, there are so many modules of Apache Hadoop so it takes so much more time to learn all of them. Other than that, optimization is somewhat a challenge in Apache Hadoop.
May 16, 2018
Hadoop Review
It is massively being used in our organization for data storage, data backup, and machine learning analytics. Managing vast amounts of data has become quite easy since the arrival of the Hadoop environment. Our department is on verge of moving towards Spark instead of MapReduce, but for now, Hadoop is being used extensively for MapReduce purposes.
- Hadoop Distributed Systems is reliable.
- High scalability
- Open Sources, Low Cost, Large Communities
- Compatibility with Windows Systems
- Security needs more focus
- Hadoop lack in real time processing
January 04, 2018
Hadoop is pretty Badass
Apache Hadoop is a cost effective solution for storing and managing vast amounts of data efficiently. It is dependable and works even when various clusters fail. The Hadoop Distributed File System (HDFS) also goes a long way in helping in storing data. MapReduce and Tez, with the help of Hive of course, processes large amounts of data in a lesser time frame than expected. This helps our data warehouse to be updated with lesser resources rather than reading, processing and updating data in a relational data base.
- It is cost effective.
- It is highly scalable.
- Failure tolerant.
- Hadoop does not fit all needs.
- Converting data into a single format takes time.
- Need to take additional security measures to secure data.
October 24, 2017
Hadoop for Justifying Business Decisions with Hard Data
Hadoop has been an amazing development in the world of Big Data. 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 datapoints.
- Hadoop can take loads of data quickly and performs well under load.
- Hadoop is customizable so that nearly any business objective can be justified with the right combination of data and reports.
- Hadoop has a lot of great resources, both informal like the community and formal like the supported modules and training.
- Hadoop is not a relational database, but it has the ability to add modules to run sql-like queries like Impala and Hive.
- Hadoop is open source and has many modules. It can be difficult without context to know which modules to leverage.
September 22, 2017
Hadoop review 2346
Hadoop is used to build a data lake where all enterprise data for my entire company can be stored. With data centralization and standardization we use it to build analytical solutions for our company. There are many other uses for the data - for example monitoring performance via KPIs, etc.
- Massive data processing
- Fault tolerance
- Speed to market
- Data visualization
- Data history
- Random access
August 24, 2017
Hadoop for Big Data
[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 that product to a utility.
- Highly Scalable Architecture
- Low cost
- Can be used in a Cloud Environment
- Can be run on commodity Hardware
- Open Source
- Its open source but there are companies like hortonworks, Cloudera etc., which give enterprise support
- Lots of scripting still needed
- Some tools in the hadoop eco system overlap
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.
- 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.
February 23, 2016
Hadoop quick review
We have Hadoop pre-prod and prod clusters. Production clusters are comprised of 200 nodes. And we have realtime clusters as well. All the data will be moved to Hadoop. We use Hadoop to do machine learning and data warehousing.
- Machine Learning Model, when SAS can not process 3 of years data. Hadoop is good tool to build the model.
- Data warehousing is also another good use case. Using Teradata is expensive.
- A lot of people are not from a programming background which makes Hue very important for end users when starting the Hadoop journey. Making Hue more user friendly and functional will be helpful for end users who don't much of a programming background.
February 14, 2016
Hadoop an awesome tool for large scale batch processing.
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.
December 01, 2015
Hadoop - Effective tool for large scale distributed processing.
I have used Hadoop for building business feeds for a telecom client. The major purpose for using Hadoop was to tackle the problem of gaining insights into the ever growing number of business data. We leveraged the map reduce programming model to churn more than 30 gigabytes of data per day into actionable and aggregated data which was further leveraged by campaign teams to design and shape marketing and by product teams to envision new customer experiences.
- Hadoop is an excellent framework for building distributed, fault tolerant data processing systems which leverage HDFS which is optimized for low latency storage and high throughput performance.
- Hadoop Map reduce is a powerful programming model and can be leveraged directly either via use of Java programming language or by data flow languages like Apache Pig.
- Hadoop has a reach eco system of companion tools which enable easy integration for ingesting large amounts of data efficiently from various sources. For example Apache Flume can act as data bus which can use HDFS as a sink and integrates effectively with disparate data sources.
- Hadoop can also be leveraged to build complex data processing and machine learning workflows, due to availability of Apache Mahout, which uses the map reduce model of Hadoop to run complex algorithms.
- Hadoop is a batch oriented processing framework, it lacks real time or stream processing.
- Hadoop's HDFS file system is not a POSIX compliant file system and does not work well with small files, especially smaller than the default block size.
- Hadoop cannot be used for running interactive jobs or analytics.
December 01, 2015
Fast and Reliable, Use Hadoop!
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.
November 11, 2015
Advantage Hadoopo
Score 10 out of 10
Vetted Review
Verified User
We are using it for Retail data ETL processing. This is going to be used in whole organization. It allows terabytes of data to be processed in faster manner with scalability.
- Processes big volume of data using parallelism in faster manner.
- No schema required. Hadoop can process any type of data.
- Hadoop is horizontally scalable.
- Hadoop is free.
- Development tools are not that friendly.
- Hard to find hadoop resources.
January 19, 2015
Hadoop >>>> Traditional proprietary Systems
Score 10 out of 10
Vetted Review
Verified User
My company's new cloud based architecture is Hadoop based . It is being used across several organizations in our company . Using Hadoop our company has been able to solve many big data problems faster with very high performance.
- Cost Effective
- Distributed and Fault Tolerant
- Easily Scalable
- Cluster management and debugging is kind of not user friendly ( Doesn't has many tools )
- More focus should be given to Hadoop Security
- Single Master Node
- More user adoption ( Even though it is increasing by each day )
May 09, 2014
User Review of Hadoop
Score 10 out of 10
Vetted Review
Verified User
Hadoop is part of the overall Data Strategy and is mainly used as a large volume ETL platform and crunching engine for proprietary analytical and statistical models. The biggest challenge for developers/users is moving from an RDBMS query approach for accessing data to a schema on read and list processing framework. The learning curve is steep upfront, but Hive and end user tools like Datameer can help to bridge the gap. Data governance and stewardship are of key importance given the fluid nature of how data is stored and accessed.
- Gives developers and data analysts flexibility for sourcing, storing and handling large volumes of data.
- Data redundancy and tunable MapReduce parameters to ensure jobs complete in the event of hardware failure.
- Adding capacity is seamless.
- Logs that are easier to read.