--- 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: '2026-08-09' 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. Hadoop **Rating:** 3.0/5.0 stars **Reviewed by:** Saurabh A. | ETL Lead, Enterprise (> 1000 emp.) **Reviewed Date:** October 03, 2019 **What do you like best about Hadoop HDFS?** High volume data processing power Data duplication across nodes Ease of data storage **What do you dislike about Hadoop HDFS?** System becomes slow since nodes are shared, so it becomes difficult to complete job execution **What problems is Hadoop HDFS solving and how is that benefiting you?** Data retention and ease of data analytics ### 3. 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. ### 4. 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 ## 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?filters%5Bnps_score%5D%5B%5D=3§ion=pricing&secure%5Bexpires_at%5D=2026-08-14+13%3A09%3A34+-0500&secure%5Bsession_id%5D=75fcb8c9-0230-4393-9bc1-200e9dd32cc4&secure%5Btoken%5D=cbbf7322634d0f507d058ed694e59b2862d88711fd3b146495e1a03252c1c624&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 - Real-Time Data - Real-Time Data - Third-Party Integrations - Third-Party Integrations **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications - Mobile Access - Metadata Management ## Top Hadoop HDFS Alternatives - [Databricks](https://www.g2.com/products/databricks/reviews) - 4.6/5.0 (1,337 reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - 4.5/5.0 (1,145 reviews) - [Cloudera](https://www.g2.com/products/cloudera/reviews) - 4.1/5.0 (131 reviews)