--- 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. HDFS: Reliable, But Definitely Showing Its Age **Rating:** 3.0/5.0 stars **Reviewed by:** Abhishek K. | Technical Lead, Information Technology and Services, Enterprise (> 1000 emp.) **Reviewed Date:** August 01, 2025 **What do you like best about Hadoop HDFS?** HDFS still does one thing really well store large files across multiple machines with solid fault tolerance. It’s great for batch workloads and works like a charm when paired with Spark, Hive, or traditional Hadoop jobs. Once you’ve set it up right, it’s stable and does its job quietly in the background. For old school, on prem big data pipelines, it’s a dependable workhorse. **What do you dislike about Hadoop HDFS?** Let’s be real, HDFS is not keeping up with the times. In today’s world of cloud-native, serverless, auto-scaling storage, HDFS feels like using a Nokia in an iPhone world. Scaling means more hardware, more headaches. Managing NameNode/SecondaryNameNode is like babysitting one wrong move and your cluster throws a tantrum. It handles large files well, but feed it too many small files and it chokes. It also lacks the flexibility and cost efficiency of cloud storage, no managed service feel, and don’t even ask about object-level access. Security, upgrades, and maintenance? A whole job in itself. You’ll end up needing a dedicated team just to keep things smooth. **What problems is Hadoop HDFS solving and how is that benefiting you?** Storing large batch data in older pipelines Running PySpark jobs on top of Hadoop clusters Transitional layer before moving data to cloud-based systems like GCS or S3 ### 2. My experience with Hadoop **Rating:** 5.0/5.0 stars **Reviewed by:** Varad V. | AI/ML Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** January 24, 2024 **What do you like best about Hadoop HDFS?** The most that I like about hadoop is that it is extremely easy to use and implement. I work with big data and storing that is most convenient with hadoop. Transforming and modelling data is easy with hadoop. The scalability feature of it helps me store large amount of data quickly whenever needed. Due to parallel processing, it is quite fast. **What do you dislike about Hadoop HDFS?** The only bad thing about Hadoop is that is doesn't allow real time processing of data like some other distributed file and storage systems. **What problems is Hadoop HDFS solving and how is that benefiting you?** Hadoop helps me store large amounts of data for analysing purpose efficiently. That makes it useful for predictive analysis, data modelling and transformation that I have to do from time to time. I personally have to work only with historical data and not real time data, so Hadoop HDFS is my go to data storage system. ### 3. Compatibility for large/high volume **Rating:** 4.5/5.0 stars **Reviewed by:** Mohammad Mateen M. | Software Developer, Small-Business (50 or fewer emp.) **Reviewed Date:** March 06, 2024 **What do you like best about Hadoop HDFS?** It helps in managing large amount very easily.as someone who uses Mssql server exploring and working with Hadoop Hdfs was very good and it gives a new exploration to your knowledge so very good and efficient overall. **What do you dislike about Hadoop HDFS?** So far the process has been convenient, of course as a beginner if you miss something that could cause problem like understanding hdfs could be difficult but if taken care once then it is obvious that process goes smoothly. **What problems is Hadoop HDFS solving and how is that benefiting you?** So as we can create HDFS clusters, it has helped in the storing and analysing large volume of data more efficiently and more efficient means more time saving ### 4. HDFS for big data storage **Rating:** 5.0/5.0 stars **Reviewed by:** Nitish K. | Big Data Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** October 17, 2023 **What do you like best about Hadoop HDFS?** Hadoop's storing of large amount of data into a clusters that makes data as fault tolerant, secure and faster processing and scalability **What do you dislike about Hadoop HDFS?** There is nothing i disliked about HDFS but it's not easy to access, one must learn and install about hadoop to use hdfs, it could be better if there is a special user interface to store data using hdfs directly **What problems is Hadoop HDFS solving and how is that benefiting you?** HDFS helped with storing of 100's of GB's of data into hadoop HDFS cluster ### 5. I was a total good experience to go through with the huge chunk of data and dealing with it. **Rating:** 4.5/5.0 stars **Reviewed by:** Himani K. | Senior Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** June 07, 2023 **What do you like best about Hadoop HDFS?** It helps to deal with huge amount of data in a very smooth way, and it also got many tools which make it more and more productive to deal with, it has gave a whole new and good approach to Deal with the company's data. **What do you dislike about Hadoop HDFS?** You can say it got many tools for each and every new approach in the Hadoop but it also sometimes get hectic to work with data, it need different tool to apply in every part of the analysis of the data. **What problems is Hadoop HDFS solving and how is that benefiting you?** It is slowing one biggest problem we used to face in the technology world which is dealing with the huge amount of data how to store it when you don't have that much space to store that but Hadoop solved it. ### 6. Great tool to handle large data **Rating:** 4.0/5.0 stars **Reviewed by:** Ajeet M. | Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** June 14, 2023 **What do you like best about Hadoop HDFS?** Large volumes of data can be handled easily with Hadoop HDFS, which can scale as your needs change. Even in the face of failures, it guarantees that data is consistently accessible and trustworthy. Hadoop HDFS is a flexible solution that works well with other tools in the Hadoop ecosystem due to its low-cost architecture and capacity to process many sorts of data. **What do you dislike about Hadoop HDFS?** There are issues with Hadoop HDFS to think about. For multiple little files, it causes delays and lags a bit. There are limits to real-time tasks. Hadoop HDFS configuration and management can be challenging, and data replication increases the need for storage. **What problems is Hadoop HDFS solving and how is that benefiting you?** Hadoop HDFS benefits me by solving business problems in the following ways: 1) It facilitates effective data storage of significant amounts. 2) It enables parallel processing, enabling sophisticated analytics and insightful information. 3) It uses strategies for fault tolerance to guarantee data availability and dependability. 4) Compared to specialised systems, it is more affordable and easily scales to address expanding data needs. ### 7. Hadoop HDFS Review **Rating:** 4.0/5.0 stars **Reviewed by:** Abhishek S. | Senior Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** June 06, 2023 **What do you like best about Hadoop HDFS?** One of the things I like best about Hadoop HDFS is its ability to handle massive amounts of data across multiple nodes. It is designed to distribute data and processing tasks, making it highly scalable and fault-tolerant. Another great feature is its fault recovery mechanism, which ensures data availability even in the event of node failures. Additionally, Hadoop HDFS provides a simple and efficient way to store and retrieve data, making it a popular choice for big data processing and analytics. **What do you dislike about Hadoop HDFS?** Firstly, HDFS can have relatively high latency for small file operations due to the overhead of storing metadata. Secondly, its reliance on Java may pose difficulties for developers accustomed to other programming languages. Thirdly, Hadoop HDFS lacks built-in support for fine-grained access control, requiring additional configuration for robust security measures. Additionally, the complexity of configuring and managing HDFS clusters can be a learning curve for newcomers. Lastly, Hadoop HDFS might not be the ideal choice for real-time data processing scenarios due to its batch-oriented nature. **What problems is Hadoop HDFS solving and how is that benefiting you?** Hadoop HDFS solves the problem of storing and processing massive amounts of data by providing a distributed file system. This allows for horizontal scalability and fault tolerance, enabling efficient handling of big data workloads. With HDFS, you can store and analyze vast datasets across multiple nodes, utilizing the power of parallel processing. The benefits include improved data availability, faster data processing, and the ability to scale your infrastructure as your data grows. Ultimately, Hadoop HDFS empowers organizations to extract valuable insights from their data, leading to better decision-making and competitive advantage ### 8. Brief Review of HDFS **Rating:** 4.0/5.0 stars **Reviewed by:** Karan S. | Senior Consultant Big Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 06, 2023 **What do you like best about Hadoop HDFS?** HDFS High Throughput perfectly designed to store, analyse, and stream huge files and data. With its emphasis on sequential read and write operations, large throughput rates are possible. Due to this, HDFS is a good choice for workloads that require a lot of data and batch processing. **What do you dislike about Hadoop HDFS?** Hadoop HDFS offers simple access control techniques, however in some business circumstances, these might not be enough. It may be necessary to perform additional configuration and management for advanced security features like fine-grained access control, encryption, and integration with external security systems in matter of Security and access control. **What problems is Hadoop HDFS solving and how is that benefiting you?** HDFS includes built-in fault tolerance methods to assure data availability. By replicating data across numerous data nodes, HDFS guards against data loss in case of hardware failures or other node problems. Data replication and recovery are automatically handled by the system, ensuring that access to data is maintained even in the event of failures which basically helped me to complete some my tickets. ### 9. Hadoop HDFS: A Scalable and Reliable Solution for Storing and Processing Big Data **Rating:** 4.5/5.0 stars **Reviewed by:** Archit M. | Market Risk Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** June 07, 2023 **What do you like best about Hadoop HDFS?** Hadoop HDFS can be easily scaled out to handle larger amounts of data. This is done by adding more nodes to the Hadoop cluster.It has also many features such as native support to large dataset, higher fault tolerance and it can also provide high throughput data access. **What do you dislike about Hadoop HDFS?** HDFS has limitations such as it is not suitable for large datasets, slow processing speed and no real time processing, higher latency. But overall it is a good experience. **What problems is Hadoop HDFS solving and how is that benefiting you?** Hadoop HDFS is solving a big problem of Big data by storing the data in distributed form in different machines. There are huge amount of data present and that data have to be store in a cost effective way and process it efficiently. And Hadooo is doing exactly the same ### 10. Hadoop HDSF Review **Rating:** 4.0/5.0 stars **Reviewed by:** Divyanshi S. | Cloud Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 07, 2023 **What do you like best about Hadoop HDFS?** Hadoop file system is way better than traditional file system in many ways. It has many features like native support of larger datasets, higher fault tolerance and it can also provide high throughput data access for applications having large scale data sets. **What do you dislike about Hadoop HDFS?** There are few limitations of HDFS like it is not suitable for small datasets, slow processing speed, no real time data processing, bit harder to use, latency is bit higher. But overall good experience. **What problems is Hadoop HDFS solving and how is that benefiting you?** Hadoop HDFS is solving a big problem of Big data by storing the data in distributed form in different machines. There are huge amount of data present and that data have to be store in a cost effective way and process it efficiently. And Hadooo is doing exactly the same. ## 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/hadoop-hdfs-review-4574671?section=pricing&secure%5Bexpires_at%5D=2026-07-18+11%3A15%3A01+-0500&secure%5Bsession_id%5D=094d38a2-96d9-4d23-bcce-df0ed4b5fbdb&secure%5Btoken%5D=466bff71197446cbb86ee37409e06a5531e40bf01292d14b755f67a4f91bbafc&format=llm_user) ## Hadoop HDFS Integrations - [Hive](https://www.g2.com/products/hive-hive-hive/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) ## 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 (1,319 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)