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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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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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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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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.
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April 11, 2022
Hadoop: A Robust Big Data Platform
- 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
- 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 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.
- Storing large amounts of data
- Processing large amounts of data via a familiar SQL interface
- Slower than other interactive querying engines. Queries take minutes at least and up to hours sometimes
- Tuning the settings to be able to run certain queries can require a lot of domain knowledge
January 16, 2021
Apache Hadoop Can Save on the Headaches
- Handles large amounts of unstructured data well, for business level purposes
- Is a good catchall because of this design, i.e. what does not fit into our vertical tables fits here.
- Decent for large ETL pipelines and logging free-for-alls because of this, also.
- Many, many modules and because of Apache open source, takes time to learn
- Integration is not always seamless between the disparate pieces nor are all the pieces required.
- Optimization can be challenging (see PSTL design)
September 21, 2020
Hadoop -- Great Value for What You Pay
- Accessible
- Inexpensive
- User friendly
- Much slower than more premium platforms
- Doesn't connect with other data warehouses
- Not mainstream -- somewhat more, "hacky" of a solution
September 20, 2020
Fault Tolerance and High Availablility Made Easy with Hadoop
- Map-reduce
- Parallel processing
- Handles node failures
- HDFS: distributed file system
- More connectors
- Query optimization
- Job scheduling
June 05, 2019
Hadoop vs. Alternatives
- Great for inexpensive storage, when originally introduced.
- Distributed processing
- Industry standard
- Network fabric needs to be more sophisticated.
- Need centralized storage.
- The three copy of data should have been in the original design, not years later.
- Consider deploying Spectrum Scale in these environments.
May 16, 2018
Hadoop Review
- 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
March 28, 2018
Great Option for Unstructured Data
- HDFS is reliable and solid, and in my experience with it, there are very few problems using it
- Enterprise support from different vendors makes it easier to 'sell' inside an enterprise
- It provides High Scalability and Redundancy
- Horizontal scaling and distributed architecture
- Less organizational support system. Bugs need to be fixed and outside help take a long time to push updates
- Not for small data sets
- Data security needs to be ramped up
- Failure in NameNode has no replication which takes a lot of time to recover
January 04, 2018
Hadoop is pretty Badass
- 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.
December 13, 2017
Hadoop: Highly available, scalable and cost effective for big data storage and processing.
- Scalability is one of the main reasons we decided to use Hadoop. Storage and processing power can be seamlessly increased by simply adding more nodes.
- Replication on Hadoop's distributed file system (HDFS) ensures robustness of data being stored which ensures high-availability of data.
- Using commodity hardware as a node in a Hadoop cluster can reduce cost and eliminates dependency on particular proprietary technology.
- User and access management are still challenging to implement in Hadoop, deploying a kerberized secured cluster is quite a challenge itself.
- Multiple application versioning on a single cluster would be a nice to have feature.
- Processing a large number of small files also becomes a problem on a very large cluster with hundreds of nodes.
October 24, 2017
Hadoop for Justifying Business Decisions with Hard Data
- 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
- Massive data processing
- Fault tolerance
- Speed to market
- Data visualization
- Data history
- Random access
August 24, 2017
Hadoop for Big Data
- 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
June 03, 2016
A newbie's look at Hadoop
- The distributed replicated HDFS filesystem allows for fault tolerance and the ability to use low cost JBOD arrays for data storage.
- Yarn with MapReduce2 gives us a job slot scheduler to fully utilize available compute resources while providing HA and resource management.
- The hadoop ecosystem allows for the use of many different technologies all using the same compute resources so that your spark, samza, camus, pig and oozie jobs can happily co-exist on the same infrastructure.
- Without Cloudera as a management interface the hadoop components are much harder to manage to ensure consistency across a cluster.
- The calculations of hardware resources to job slots/resource management can be quite an exercise in finding that "sweet spot" with your applications, a more transparent way of figuring this out would be welcome.
- A lot of the roles and management pieces are written in java, which from an administration perspective can have there own issues with garbage collection and memory management.
May 26, 2016
Experience with Hadoop by a novice user.
- Provides a reliable distributed storage to store and retrieve data. I am able to store data without having to worry that a node failing might cause the loss of data.
- Parallelizes the task with MapReduce and helps complete the task faster. The ease of use of MapReduce makes it possible to write code in a simple way to make it run on different slaves in the cluster.
- With the massive user base, it is not hard to find documentation or help relating to any problem in the area. Therefore, I rarely had any instances where I had to look for a solution for a really long time.
- I would have hoped for a simpler interface if possible, so that the initial effort that had to be spent would have been much less. I often see others who are starting to use hadoop are finding it hard to learn.
- I'm not sure if it is a problem with the organization and the modules they provide, but sometimes I wish there were more modules available to be used.
- 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
- 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 16, 2016
Apache Hadoop is the best open source product I used.
- Hadoop is open source and with a wide community already present, the usage is much easy for individuals, startups and MNCs alike.
- Hadoop works well for commodity hardware and that makes it easier to avoid pricey clusters.
- Hadoop takes parallel programming to next level and helps processing of multi terabytes (even petabytes) of data easier.
- While Hadoop MR parallelizes jobs involving Big Data, it is slow for smaller data sets
- OLAP (analytics)is easier, however, OLTP (transactions) is a problem in most cases.
- People using Hadoop have to keep in mind that small proof of concepts may not scale as expected.
February 14, 2016
Hadoop an awesome tool for large scale batch processing.
- 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 09, 2015
Hadoop - best data optimization for the Enterprise
- Hadoop is a very cost effective storage solution for businesses’ exploding data sets.
- Hadoop can store and distribute very large data sets across hundreds of servers that operate, therefore it is a highly scalable storage platform.
- Hadoop can process terabytes of data in minutes and faster as compared to other data processors.
- Hadoop File System can store all types of data, structured and unstructured, in nodes across many servers
- For now, Hadoop is doing great and is very productive.
December 04, 2015
A very generic Hadoop review
- No requirement for schema on write.
- Ability to scale to massive amounts of data.
- Open platform provides multiple options and customizations to fit your exact needs.
- The platform is still maturing and can be confusing to research and use. Basic tasks can still be manual and are not always user friendly.
December 01, 2015
Hadoop - Effective tool for large scale distributed processing.
- 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
Hadoop the solution to big data problems
- Processing huge data sets.
- Concurrent processing.
- Performance increases with distribution of data across multiple machines.
- Better handling of unstructured data.
- Data nodes and processing nodes
- Make Haadop lighweight.
- Installation is very difficult. Make it more user friendly.
- Introduce a feature that works with continuous integration.