--- title: Hadoop HDFS Reviews meta_title: 'Hadoop HDFS Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 141 reviews by the users' company size, role or industry to find out how Hadoop HDFS works for a business like yours. aggregate_rating: rating_value: 4.4 review_count: 141 scale: '5' date_modified: '2026-07-17' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # Hadoop HDFS Reviews **Vendor:** The Apache Software Foundation **Category:** [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) **Average Rating:** 4.4/5.0 **Total Reviews:** 141 ## About Hadoop HDFS The Hadoop Distributed File System (HDFS) is a scalable and fault-tolerant file system designed to manage large datasets across clusters of commodity hardware. As a core component of the Apache Hadoop ecosystem, HDFS enables efficient storage and retrieval of vast amounts of data, making it ideal for big data applications. Key Features and Functionality: - Fault Tolerance: HDFS replicates data blocks across multiple nodes, ensuring data availability and resilience against hardware failures. - High Throughput: Optimized for streaming data access, HDFS provides high aggregate data bandwidth, facilitating rapid data processing. - Scalability: Capable of scaling horizontally by adding more nodes, HDFS can accommodate petabytes of data, supporting the growth of data-intensive applications. - Data Locality: By processing data on the nodes where it is stored, HDFS minimizes network congestion and enhances processing speed. - Portability: Designed to be compatible across various hardware and operating systems, HDFS offers flexibility in deployment environments. Primary Value and Problem Solved: HDFS addresses the challenges of storing and processing massive datasets by providing a reliable, scalable, and cost-effective solution. Its architecture ensures data integrity and availability, even in the face of hardware failures, while its design allows for efficient data processing by leveraging data locality. This makes HDFS particularly valuable for organizations dealing with big data, enabling them to derive insights and value from their data assets effectively. ## Hadoop HDFS Pros & Cons **What users like:** - Users commend HDFS for its **excellent data processing** capabilities, ensuring reliable storage and robust fault tolerance. (1 reviews) - Users value the **data security** of Hadoop HDFS, appreciating its fault tolerance for large file storage across machines. (1 reviews) - Users value the **reliable data storage** of Hadoop HDFS, appreciating its fault tolerance and stability in big data processing. (1 reviews) - Users value the **storage of large files across multiple machines** with solid fault tolerance and stability of HDFS. (1 reviews) **What users dislike:** - Users find the **increased costs** associated with HDFS due to hardware and maintenance burdens quite challenging. (1 reviews) - Users face significant **maintenance issues** with HDFS, requiring dedicated teams for security, upgrades, and overall management. (1 reviews) - Users face significant **performance issues** with HDFS, struggling with scaling, management, and inefficiency in handling small files. (1 reviews) - Users highlight **poor performance** issues with HDFS, struggling with scaling and management challenges in modern environments. (1 reviews) - Users highlight **security issues** with Hadoop HDFS, requiring a dedicated team for upgrades and maintenance. (1 reviews) ## Hadoop HDFS Reviews ### 1. Used HDFS to store crawled polish internet **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Education Management | Mid-Market (51-1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** Well, I like the basic idea - it is distributed filesystem used to store and transform large datasets. Science, we faced the problem of storing and processing multi-terabyte datasets it is only natural to use HDFS **What do you dislike about Hadoop HDFS?** Well, it is trivial to fool hdfs security and it was completely ineffective. You have to relly on additional tools, such as Kerberos and it adds complexity to your company. **Recommendations to others considering Hadoop HDFS:** Hire a preson whose only responsibility is to manage this zoo. You will never use HDFS in isolation (look security concerns) and at some point the cost of managing all Apache big data projects will be a significant burden on your talent pool. It is also wise to take advantage of existing bundles (Cloudera, Hortonworks). Otherwise the risk of not going beyond experimentation phase is quite significant. **What problems is Hadoop HDFS solving and how is that benefiting you?** The main business problem was to crawl and store polish websites. We used CommonCrawl dataset as a source, and Akka framework for highly paralell data processing (it was a "trivially parallelizable" problem") and hdfs with Cassandra and Apache Spark for storage and processing. ### 2. Best distributed file system for large datasets **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** What makes a successful platform is scalability and reliability . These are two traits Hadoop does perfectly . When you work with large datasets Hadoop helps you process without giving you worry . HDFS is the best distributed file system for large datasets . It is complete with integrations with many parallel computing platforms . It is tool you need to scale with huge datasets and it doing magic for use . **What do you dislike about Hadoop HDFS?** As with any tool , Hadoop is not a silver bullet for all data related tasks . Cases where the dataset is small or dataset involves transaction , one call feel Hadoop not upto the task . But no complains here , as we have to realise that Hadoop is not built for small dataset or transactional data. **What problems is Hadoop HDFS solving and how is that benefiting you?** 1. We have a huge data pipeline . 2. It helps to bring up business reports . 3. We use for the data science analysis . To improve our model. ### 3. Serves an important purpose, though it's often misused or misunderstood **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** It is extremely flexible and able to handle the largest data sets while the Map/Reduce patterns makes it easy to reason about program behavior. **What do you dislike about Hadoop HDFS?** Although it's getting better, it would be nice to improve the Streaming API. **Recommendations to others considering Hadoop HDFS:** Others have mentioned a lower limit of 1 TB for data that I generally agree with, although I might say that you should try to stay within convention systems up to 3-5 TB if possible. Smart indexing and sharding can even take you past that. **What problems is Hadoop HDFS solving and how is that benefiting you?** The business problem was extracting and refining data from a large unstructured corpus. Hadoop allowed us to scale this process and be able to iterate using the entire dataset. ### 4. Relying on Hadoop since 2008 **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** HDFS - solves a big problem and does it well. It's likely one of the most scalable distributed file systems and most reliable one. Hadoop, in general has a large expert community around it and it's well maintained and supported. It's composable which allows other systems (e.g. HBase, Spark, etc.) to layer on top nicely without compromising too much. HDFS is performant in that it's transparent with regards to data physical location, allowing efficient data manipulation. **What do you dislike about Hadoop HDFS?** It's a bit convoluted and not as easy as it should be. No good separation of client / server configurations. HTTP APIs are clumsy. **Recommendations to others considering Hadoop HDFS:** HDFS is great. MapReduce is also part of Hadoop, but practically obsolete. **What problems is Hadoop HDFS solving and how is that benefiting you?** HDFS solves a huge business problem - that of cheap, but reliable big data storage. It allows us to both store the data and have it available for processing in a scalable manner. ### 5. I use hadoop for scaling machine learning solutions to fault detection problems. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** The hadoop platform is essentially an open industry standard in cloud computing, several essential tools for modern production quality machine learning applications support scaling via hadoop / spark. **What do you dislike about Hadoop HDFS?** The setup and configuration of hadoop and spark is it's greatest weakness. It often takes a non trivial amount of engineering time to setup and tune. Fortunately services such as AWS allow you to hit the ground running without as much setup. **Recommendations to others considering Hadoop HDFS:** Use AWS if possible, but setting up your own cluster isn't as scary as it appears. **What problems is Hadoop HDFS solving and how is that benefiting you?** We are producing fault detection and prognostics for industrial machines and vehicles. We use several non parametric statistics and a good deal of machine learning to get the job done. Hadoop has drastically lowered turn around time on results and even in development (after the initial setup and growing pains subsided). ### 6. My Experience of using HDFS **Rating:** 4.5/5.0 stars **Reviewed by:** Xiufeng L. | Postdoctoral Researcher, Research, Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** HDFS provides high scalability to manage large-scale data sets in a cluster. **What do you dislike about Hadoop HDFS?** It does not provide the user-friendly interface to interact HDFS, e.g. like the GUI to for a DBMS **What problems is Hadoop HDFS solving and how is that benefiting you?** I use HDFS to store research data at my project, a smart city project. The benefit is still the scalability, and the ability of managing large data sets. ### 7. Tuan's experience on Hadoop HDFS **Rating:** 4.0/5.0 stars **Reviewed by:** Tuan T. | Research Engineer, Research, Mid-Market (51-1000 emp.) **Reviewed Date:** March 12, 2016 **What do you like best about Hadoop HDFS?** HDFS is the most native file system in Hadoop, supported in all Hadoop-based framework and programming APIs. It supports replications and formatting checking nicely **What do you dislike about Hadoop HDFS?** just like local file systems, HDFS requires you to write many verbose code to handle the opening / closure, the readers **Recommendations to others considering Hadoop HDFS:** HDFS is the first native IO protocol you must master, before thinking of other more advanced data management stacks **What problems is Hadoop HDFS solving and how is that benefiting you?** We use HDFS to handle our data in all European projects that I'm involved: Sending and receiving financial data, web archives, social media stream crawls, etc. ### 8. Extensive experience with HDFS in various environments, on premise, cloud, AWS etc **Rating:** 2.5/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** Is this a review of the HDFS file system? If so, the performance is great compared to say S3 or other ways to access file systems on Hadoop. Its also tried and tested. However, for the survey to make more sense, I will answer on Hadoop in general too. **What do you dislike about Hadoop HDFS?** Inflexible, data needs to be copied to HDFS from other places, one cannot do real-time access from HDFS. This survey is not well-written if its primarily HDFS that you need feedback on. **Recommendations to others considering Hadoop HDFS:** Need a better filesystem. We should be able to import data from other sources faster. There should also be real time (in memory) capabilities built-around HDFS. **What problems is Hadoop HDFS solving and how is that benefiting you?** Analytics, business intelligence. Typically, I want fast results for jobs and ended up using Impala on top of HDFS. ### 9. Hadoop and it's future **Rating:** 3.5/5.0 stars **Reviewed by:** Chirag M. | Technical Lead, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Database in the form of large datasets is the need for today.hadoop does that with integrity , robustness, and it's plugin development helps a lot as well **What do you dislike about Hadoop HDFS?** Nothing as such but it should provide support for APIs for most popular languages **What problems is Hadoop HDFS solving and how is that benefiting you?** I work in banking and storage domain so database need is must ### 10. Very friendly solution for those using distributed databases **Rating:** 4.0/5.0 stars **Reviewed by:** Giridhur S. | Senior Analyst, Small-Business (50 or fewer emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** Very easy to set up. Lots of example code and tutorials available online to use. Java is a well known language so, very accessible for beginners **What do you dislike about Hadoop HDFS?** Rewriting functions into MapReduce form is not always easy. Needs some practice. Also interfacing with other languages is slightly difficult. **Recommendations to others considering Hadoop HDFS:** Umm, i really cant think of a better alternative to this. **What problems is Hadoop HDFS solving and how is that benefiting you?** I used Hadoop to implement a similarity search algorithm across photos and videos, to handle the large amount of data and make it scalable, Hadoop was my first choise ### 11. Highly Scalable Distributed Data Infrastructure **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) **Reviewed Date:** March 10, 2016 **What do you like best about Hadoop HDFS?** It is resilient and rack aware. Choose HDFS for >10TB data infrastructure where data lives in forms such as Thrift, Protobuf, JSON, etc. (for diverse datasets). It works very well with technology like Mesos and Aurora. Works with many solutions (Spark, HBase, Hadoop, Scalding, Cascading, Storm, etc.). **What do you dislike about Hadoop HDFS?** It's hard to set up. Turnkey solutions for this make it easier, as does contracting out setup. Master election does require tuning and domain knowledge so prefer off the shelf solutions over trying to roll your own deployment. **Recommendations to others considering Hadoop HDFS:** Stress test your configuration and test edge cases. Losing data is a tough process. **What problems is Hadoop HDFS solving and how is that benefiting you?** HDFS solved the business problems of highly scalable data storage in a highly available and scalable environments. Dump data into HDFS without worrying about ETL. ### 12. use hdfs in frequent work **Rating:** 4.0/5.0 stars **Reviewed by:** ruichang z. | Data Mining Engineer, Internet, Mid-Market (51-1000 emp.) **Reviewed Date:** March 15, 2016 **What do you like best about Hadoop HDFS?** hdfs is a distributed file system, I can use it friendly, as its command is similar to linux file command. **What do you dislike about Hadoop HDFS?** I think reponse time is large, and also I find maybe a bug, that is if I want to move to middle of a last typed hdfs command, and modify some, it would erase some behind chars. **Recommendations to others considering Hadoop HDFS:** is open source and command used, and good software for big data coders **What problems is Hadoop HDFS solving and how is that benefiting you?** recommendation algorithm for iqiyi ### 13. A Hadoop certified developer, used Hadoop file system as well **Rating:** 3.5/5.0 stars **Reviewed by:** Ankur S. | Small-Business (50 or fewer emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Scalable for super large datasets. But you really need to have huge data to need it. Most datasets do not really need hadoop. **What do you dislike about Hadoop HDFS?** It is slow. Even smaller datasets get spread out and need to be loaded by individual machines. **Recommendations to others considering Hadoop HDFS:** Ensure you really have huge data requirements, otherwise there are other in memory solutions. **What problems is Hadoop HDFS solving and how is that benefiting you?** The analysis of large datasets, but you cannot really do realtime analytics. It is all about the batch processing. ### 14. BigB of BigData **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Online Media | Mid-Market (51-1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** Working on PetaBytes of Data, it just works !!! No need of having powerful machines works on commodity hardware. **What do you dislike about Hadoop HDFS?** Not something that I dislike , but a limitation that nits not meant for small files. **Recommendations to others considering Hadoop HDFS:** I don't see any alternatives for it. I would recommend to go with Cloudera Manager rather plain vanilla install because installation and management would be easy. **What problems is Hadoop HDFS solving and how is that benefiting you?** Apache Log Analyzing , Analytics , ### 15. Use hdfs for store data warehouse data **Rating:** 4.0/5.0 stars **Reviewed by:** Jianfeng Z. | Member of Technical Staff, Computer Software, Mid-Market (51-1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** Good scalability and fault tolerance. Easy access method: shell and java api **What do you dislike about Hadoop HDFS?** Sometimes, weird issue would happen. Most of time it can be resolved from the logs, but sometimes have to restar the cluster **What problems is Hadoop HDFS solving and how is that benefiting you?** use hdfs to host the data warehouse data originally in MPP. Very cheap compared to MPP and with fair performance. ### 16. Industry-grade scalability but a developer experience with room for improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Mid-Market (51-1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** Technology is free and open source. HDFS is an industry standard for big data processing. Hadoop is Java-based, making onboarding for a majority of developers easy. **What do you dislike about Hadoop HDFS?** The Hadoop application programming interface is terse; simple workflows sometimes need to be laboriously converted into its map-reduce paradigm. Other interfaces (e.g. Spark) mitigate this problem. **Recommendations to others considering Hadoop HDFS:** Be aware that Hadoop YARN is aimed at becoming the next-generation of MapReduce. **What problems is Hadoop HDFS solving and how is that benefiting you?** I am a software engineer at a leading research institution. We are using Hadoop to train predictive models on large distributed data sets. ### 17. I only use Hadoop for one off analytic projects which I can not perform on my laptop **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in International Affairs | Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** I like it because it eliminates the bottleneck of insufficient computing power either on my laptop or company server. **What do you dislike about Hadoop HDFS?** Frankly, I haven't had any problems with Hadoop nor dislike any part of it. That being said, I am not a heavy user. **What problems is Hadoop HDFS solving and how is that benefiting you?** I had a project which requires me to process NASA satellite images which were massive (1TB per file) and was not possible to process on my laptop, especially when the analysis is conducted with R. This is where Hadoop came in and helped out. ### 18. bigdata processing for batch data **Rating:** 5.0/5.0 stars **Reviewed by:** tousif k. | Senior Software Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** January 10, 2016 **What do you like best about Hadoop HDFS?** distributed map reduce jobs to process batch data. **What do you dislike about Hadoop HDFS?** requires admin to monitor and keep the cluster up and running **Recommendations to others considering Hadoop HDFS:** if you have huge data processing and in batch mode than go for hadoop map reduce **What problems is Hadoop HDFS solving and how is that benefiting you?** batch data processing and reporting ### 19. Hadoop review **Rating:** 4.0/5.0 stars **Reviewed by:** Niko G. | Researcher, Telecommunications, Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** User doesn't have to think about the low-level functionalities. **What do you dislike about Hadoop HDFS?** Setup can represent an obstacle to a developer without system/OS knowledge. **What problems is Hadoop HDFS solving and how is that benefiting you?** Processed CDR (phone call records) dataset. Due to large amounts of data the problem couldn't be solved using traditional database or in-memory processing without HDFS. ### 20. Software Engineer **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Internet | Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** Hadoop makes it easy to scale to handle large dataset. The API it provides is simple and intuitive to use. **What do you dislike about Hadoop HDFS?** API for other languages, e.g. C++, is not as complete as that provided to Java. **What problems is Hadoop HDFS solving and how is that benefiting you?** Data cleansing, filtering, transformation and aggregation. Hadoop makes it very easy to scale to large data set. This is especially important in the field of computational advertising as we need to handle terabytes of raw data, and need to get the result within reasonable amount of time. ### 21. Best scalable distributed system **Rating:** 4.0/5.0 stars **Reviewed by:** Sunil S. | Principal Architect - Big Data & Analytics, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** The biggest challenge of a high available and distributed file system is eliminated with HDFS. **What do you dislike about Hadoop HDFS?** It is slow but very effective and if you know what right file formats to use, the performance can be blazing fast **What problems is Hadoop HDFS solving and how is that benefiting you?** high available data store, distributed, data lake ### 22. Hadoop - Vulnerable By Nature **Rating:** 5.0/5.0 stars **Reviewed by:** Viresh H. | Software Developer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** August 06, 2015 **What do you like best about Hadoop HDFS?** Distribute data and computation.The computation local to data prevents the network overload. We can easy to handle partial failure. Here the entire nodes can fail and restart. it avoids crawling horrors of failure and tolerant synchronous distributed systems. Speculative execution to work around stragglers. **What do you dislike about Hadoop HDFS?** 1 ) Rough manner:- Hadoop Map-reduce and HDFS are rough in manner. Because the software under active development. 2) Programming model is very restrictive:- Lack of central data can be preventive. 3) Joins of multiple datasets are tricky and slow:- No indices! Often entire dataset gets copied in the process. **What problems is Hadoop HDFS solving and how is that benefiting you?** This is the one advantages of using Hadoop in contrast to other distributed systems is its flat scalability curve. Executing Hadoop on a limited amount of data on a small number of nodes may not demonstrate particularly stellar performance as the overhead involved in starting Hadoop programs is relatively high. ### 23. Use in personal projectes **Rating:** 4.0/5.0 stars **Reviewed by:** Roberto O. | Software Developer Engineering, Computer Software, Enterprise (> 1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** We can use it to perform several tasks from simple analysis to more advanced machine learning methods. I adapt some ML algorithms to take advantage from HDFS. **What do you dislike about Hadoop HDFS?** The learning curve and integration with other solutions. **What problems is Hadoop HDFS solving and how is that benefiting you?** Classification problems in massive log datasets ### 24. Very Nice Technology **Rating:** 4.0/5.0 stars **Reviewed by:** Cataldo M. | Assistant Professor (Ricercatore a Tempo Determinato), Research, Enterprise (> 1000 emp.) **Reviewed Date:** March 10, 2016 **What do you like best about Hadoop HDFS?** It guarantees great scalability and good performance. Take care of using it in the right way. However, the last version has improved several features. **What do you dislike about Hadoop HDFS?** Not all the tasks can be exploited in a fruitful way. **What problems is Hadoop HDFS solving and how is that benefiting you?** Distributed Storage of Files. Scalability issues. ### 25. Cost Effective and Reliable Data Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Anil G. | Senior Software Developer, Internet, Mid-Market (51-1000 emp.) **Reviewed Date:** July 20, 2015 **What do you like best about Hadoop HDFS?** Open Source Cost Effectiveness Highly Scalable Fault Tolerant Highly Available Active & Huge Community Support Most mature and widely used Distributed Platform. **What do you dislike about Hadoop HDFS?** Running HDFS needs a lot of daemons(at least 3 Zookeeper, 3 Journal Node, 2NN). MapReduce programming is not very easy to learn. Too many new Projects in Apache Hadoop are breaking the community focus from making limited ROBUST products.(there are more than 30 projects in Hadoop and its hard to keep track now) **Recommendations to others considering Hadoop HDFS:** I dont recommend using Hadoop if you have very small dataset(less than 1 TB). Adopting Hadoop/MapReduce has learning curve. So, i recommend doing POC's before finalizing on a solution. Have at least 10 machines in your production to take advantage of Distrbuted Systems. **What problems is Hadoop HDFS solving and how is that benefiting you?** We use Hadoop to store and process data. Its cost effectiveness and speed has led to many optimizations in our data processing. Hadoop is a De Facto Standard to process and store data. ### 26. Great Option for Unstructured Data **Rating:** 5.0/5.0 stars **Reviewed by:** Bharadwaj (Brad) C. | Director Of Engineering/Head of Reliability Engineering, Enterprise (> 1000 emp.) **Reviewed Date:** July 17, 2015 **What do you like best about Hadoop HDFS?** Hadoop is a very popular big data framework.Hadoop is based on MapReduce, which makes it useful for big datasets. Hadoop can be used for almost any requirement involving huge data and also when data is unstructured. The open source community has built tons of tools around it and evolved it into an ecosystem. **What do you dislike about Hadoop HDFS?** I don't see any dislikes on this; the only thing people get confused is, its the right thing for solving every problem. Well, its not. **Recommendations to others considering Hadoop HDFS:** If you say yes for most of the questions then Hadoop is recommended 1. Data size - does it have TBs-PetaBytes of data 2. How much time you can wait - Hadoop is not instant querying tool 3. What is the data growth expected 4. Can I manage with out any real time operations 5. How much percentage of your data is structured - the low the better **What problems is Hadoop HDFS solving and how is that benefiting you?** Massive data collection, storage and analytics. It is extremely cheap to get this up and running. It does not need fancy hardware and its open source. If you are thinking this is open source and looking for support there are enterprise hadoop flavors from Cloudera, Hortonworks, MapR. ### 27. The Hadoop ecosystem is currently the best choice for many big data projects. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Enterprise support from different vendors makes it easily to 'sell' inside an enterprise, a large ecosystem with tons of options. **What do you dislike about Hadoop HDFS?** Good big data engineers/data scients are hard to find. There's a lot of misunderstanding in management levels about what the technology can and can not deliver. **What problems is Hadoop HDFS solving and how is that benefiting you?** Worked on software to create 360 degree customer views and insights for industries with large customer bases (financial, retail, telecom, media). ### 28. ICT - CAE specialist **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Aviation & Aerospace | Enterprise (> 1000 emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Possibility to manage an huge amount of data from different data sources. The integration with languages like r or python for mathematical analyses. **What do you dislike about Hadoop HDFS?** It's not easy understand the best approach in order to solve your problem and the proper tool you need to use. **What problems is Hadoop HDFS solving and how is that benefiting you?** We need to manage an huge amount of sensors time histories and permit users to search and correlate them with other data sources ### 29. If you deal with lot of data set Hadoop is the way to go **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Networking | Enterprise (> 1000 emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Easy learning curve and a huge community backing. **What do you dislike about Hadoop HDFS?** Its helpful only if the data set that we're dealing with is huge. There are other competitors in the market which can easily be adaptable and even Paas solutions that case be used turn key. **What problems is Hadoop HDFS solving and how is that benefiting you?** Writing a business process that deals with collection of user data on the network and create a report. It's extremely useful and scalable. ### 30. Hadoop for a trail user **Rating:** 3.0/5.0 stars **Reviewed by:** Verified User in Internet | Mid-Market (51-1000 emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Distribute data and computation.The computation local to data prevents the network overload. **What do you dislike about Hadoop HDFS?** Programming model is very restrictive:- Lack of central data can be preventive. **Recommendations to others considering Hadoop HDFS:** Cassandra may be a better choice for data analytics tasks **What problems is Hadoop HDFS solving and how is that benefiting you?** Using hadoop for data accumulation and event generation related tasks ### 31. Hadoop Review **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Higher Education | Mid-Market (51-1000 emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Apache Hadoop is powerful and easy to use. **What do you dislike about Hadoop HDFS?** Hadoop can be slow, Spark offers multiple advantages. **Recommendations to others considering Hadoop HDFS:** Think about the speed you need and your goals with using the product. Spark may be a better bet. **What problems is Hadoop HDFS solving and how is that benefiting you?** Handling big data on customers with ease ### 32. distributed storage of social media data for language analysis **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Research | Small-Business (50 or fewer emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Some details of file storage are obscured from user. Built-in redundancy. **What do you dislike about Hadoop HDFS?** The learning curve can be steep for new users. **What problems is Hadoop HDFS solving and how is that benefiting you?** Need to store tables of language data. We are able to have redundancy and speed, and concurrency hasn't been an issue. ### 33. New user to Hadoop Still learning the ropes **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Small-Business (50 or fewer emp.) **Reviewed Date:** March 09, 2016 **What do you like best about Hadoop HDFS?** The high availability and how it interfaces with technologies like Flink and Spark. **What do you dislike about Hadoop HDFS?** The high barrier to entry for learning the tools and the software. Also, setting it up for development. **What problems is Hadoop HDFS solving and how is that benefiting you?** I am trying to create an Information Retrieval engine. ### 34. Business intelligence with hadoop **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Banking | Mid-Market (51-1000 emp.) **Reviewed Date:** March 09, 2016 **What do you like best about Hadoop HDFS?** I like that hadoop is easy to use and simple to build other software on top **What do you dislike about Hadoop HDFS?** I dislike the difficult to setup and implement some function **What problems is Hadoop HDFS solving and how is that benefiting you?** A system for business intelligence with marketing data e analytics ### 35. Hadoop is great if you know how **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Market Research | Small-Business (50 or fewer emp.) **Reviewed Date:** March 08, 2016 **What do you like best about Hadoop HDFS?** Map reduce is an amazing way to move large set of data around **What do you dislike about Hadoop HDFS?** Hadoop is very complicated compared to Spark which is evolving rapidly.Complex syntax and deployment **What problems is Hadoop HDFS solving and how is that benefiting you?** Moving a lot of data around ### 36. The big data framework **Rating:** 5.0/5.0 stars **Reviewed by:** Pradeepkumar K. | Big Data Engineer, Internet, Enterprise (> 1000 emp.) **Reviewed Date:** April 28, 2015 **What do you like best about Hadoop HDFS?** Hadoop is the most common term used in big data world. For whatever you do on big data Hadoop will be an underlying element, so Hadoop plays an important role in all data related activities. First of all Hadoop does not replace a regular RDBMS. It is best suitable for batch operations than real time. There are recently kafka, spark streaming which can be fit into hadoop stack and make real time analytics possible. Hadoop is best to process very huge datasets. Its cheaper! Once the cluster is setup adding or deleting nodes is simple. There are very good user & developers communities working on Hadoop. With the recent releases of Hadoop with Yarn, Highly available, Federation the product is getting better and better. The high benchmarks for Hadoop & Map Reduce operations are very promising to use it. All reporting and data ware house operations can be shifted to HDFS, Hive, Hadoop stack. Hadoop can do many things file wise - it splits, merges, archives, unarchives and what not. There are almost all kinds of file operations are compatible with Hadoop and HDFS There are several use cases like recommendation systems, social network analysis(graph data), sentiment analysis etc implemented successfully on Hadoop. With the Hive things are much simpler. Just point tables to data(delimited) on HDFS and use it like a database or DWH. Tools like Sqoop, Flume make easy conversion of streaming data and legacy RDBMS data onto HDFS **What do you dislike about Hadoop HDFS?** Frankly I do not see any disadvantages or drawbacks using Hadoop! Its simply great. But there are some use cases which are not suitable for Hadoop :) 1. For all transaction purposes 2. For small data or structured data **Recommendations to others considering Hadoop HDFS:** Check yourself on 1. Data size - does it have TBs-PetaBytes of data 2. How much time you can wait - Hadoop is not instant querying tool 3. What is the data growth expected 4. Can I manage with out any real time operations 5. How much percentage of your data is structured - the low the better So if you say yes for most of the questions then Hadoop is recommended **What problems is Hadoop HDFS solving and how is that benefiting you?** 1. Building data pipelines. 2. Generating reports on very huge datasets. 3. Performing operations and extracting results from huge datasets. 4. Data analytics. ### 37. I use it on a daily bases. **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Human Resources | Mid-Market (51-1000 emp.) **Reviewed Date:** March 14, 2016 **What do you like best about Hadoop HDFS?** The distributed nature of the data storage. **What do you dislike about Hadoop HDFS?** The amount of the administrative overhead required to maintain. **Recommendations to others considering Hadoop HDFS:** The de facto standard in storing data distributively. **What problems is Hadoop HDFS solving and how is that benefiting you?** Storing documents required to analyze. ### 38. Great big data management system **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Management Consulting | Enterprise (> 1000 emp.) **Reviewed Date:** January 05, 2016 **What do you like best about Hadoop HDFS?** Fantastic system for working with big data **What do you dislike about Hadoop HDFS?** No problems with the software as of yet. **Recommendations to others considering Hadoop HDFS:** Great big data software **What problems is Hadoop HDFS solving and how is that benefiting you?** Solving data management issues and allows us to work with large datasets ### 39. Awesome for large and unstructured data **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Enterprise (> 1000 emp.) **Reviewed Date:** April 28, 2015 **What do you like best about Hadoop HDFS?** Hadoop is a very popular big data framework. Hadoop is based on MapReduce, which makes it useful for big datasets. Hadoop can be used for almost any requirement involving huge data and also when data is unstructured. To use hadoop one needs to know how map reduce works and distributive computing features, preferably in java **What do you dislike about Hadoop HDFS?** Hadoop is not yet as good as legacy RDBMS products in terms of security. All algorithms cannot be implemented with MapReduce. Previously there was single point of failure issue but with Hadoop new releases federation and high availability that was no more an issue **Recommendations to others considering Hadoop HDFS:** Hadoop is well recommended for all ETL, Reporting usecases. **What problems is Hadoop HDFS solving and how is that benefiting you?** It does not need fancy hardware. Its open source - NO COST If you are thinking this is open source and looking for support there are enterprise hadoop flavors from Cloudera, Hortonworks, MapR. ### 40. A Great Tool if your Use Case Fits **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Enterprise (> 1000 emp.) **Reviewed Date:** May 14, 2014 **What do you like best about Hadoop HDFS?** The ability to capture all data in your own ecosystem. The ability to sift through that data and find business insights. In terms of hardware and software cost it is very cheap (especially with hosted cloud services). **What do you dislike about Hadoop HDFS?** As with all Hadoop tools lots of knobs to tweak. Takes a good bit of time optimize and finely tune your Hadoop install. **What problems is Hadoop HDFS solving and how is that benefiting you?** Massive data collection, storage and analytics. It is extremely cheap to get this up and running. ### 41. Hadoop **Rating:** 3.5/5.0 stars **Reviewed by:** Brittany D. | Member of Governor Youngkin's Commission on Human Trafficking Prevention and Survivor Support, Enterprise (> 1000 emp.) **Reviewed Date:** April 21, 2014 **What do you like best about Hadoop HDFS?** It is easy to use, and has a simple interface. **What do you dislike about Hadoop HDFS?** Depending on the structure and size of your tables it can take a long time for queries to run, and it doesn't give you much insight into the length of time it might take. **Recommendations to others considering Hadoop HDFS:** I find it outdated compared to other tools, but it is fine. It gets the job done. **What problems is Hadoop HDFS solving and how is that benefiting you?** Providing business intelligence answers using simple or complex queries that run against your databases. ## Hadoop HDFS Discussions - [What is Hadoop HDFS used for?](https://www.g2.com/discussions/what-is-hadoop-hdfs-used-for) - 1 comment, 1 upvote - [View Hadoop HDFS pricing details and edition comparison](https://www.g2.com/products/hadoop-hdfs/reviews?page=3§ion=pricing&secure%5Bexpires_at%5D=2026-08-04+00%3A02%3A47+-0500&secure%5Bsession_id%5D=586a7f3e-1984-4b27-b921-8f821e200d2e&secure%5Btoken%5D=2f559847f4063354ce727026ff6ceeb7760dc0d0ce3fecf2d82f5794ebafdcd4&format=llm_user) ## Hadoop HDFS Features **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Integrations** - Hadoop Integration - Spark Integration **Platform** - Machine Scaling - Data Preparation - Spark Integration **Processing** - Cloud Processing - Workload Processing **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## Top Hadoop HDFS Alternatives - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (1,328 reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - 4.5/5.0 (1,144 reviews) - [Cloudera](https://www.g2.com/products/cloudera/reviews) - 4.1/5.0 (131 reviews)