Hadoop HDFS Reviews & Product Details

It is a most powerful and scalable framework for big data processing and storage. Its distributed nature and MapReduce model enable us perform any business logic with faster and effective performance. Review collected by and hosted on G2.com.

However, The configurations can take time when you start setting up in your system Additionally, Hadoop's MapReduce model may not be the most efficient approach for certain types of data processing tasks, particularly those requiring real-time or interactive analysis. But most of the time it can provide you the best results. Review collected by and hosted on G2.com.

I appreciate the high data availability of data and data processing speed. There is no latency compared to other file systems; you don't need to worry about node failures. I am one of the early users of HDFS, and the features that it has grown are amazing making the system wholistically more robust Review collected by and hosted on G2.com.

Lack of real-time analysis. We need extensive processing and data cleaning before we can do any analytics. It would have been great if there was some intelligence that would allow at least a high level of analytics Review collected by and hosted on G2.com.

Hadoop HDFS was developed using a file system design that holds a large amount of data and provides easy access. Hadoop stores huge data files across multiple machines. This is the best aspect of Hadoop HDFS. Review collected by and hosted on G2.com.

There is nothing to dislike. I would recommend Hadoop HDFS to enterprises. Hadoop HDFS is the best solution for enterprises. It is an enterprise-grade solution. There is nothing to dislike. Review collected by and hosted on G2.com.

It scales well with large datasets and provides high throughput for storing large files. It is open source, and the community is quite vibrant for helping in resolving issues. Plugins are available in multiple languages to use HDFS. Review collected by and hosted on G2.com.

It's not fully HA even with 3-node HA deployment. If the two namenode servers go down, the HDFS cluster goes down. The throughput decreases if there are a lot of small files. And the data is not evenly stored across all the datanodes; not sure if it is related to our deployment configurations. Review collected by and hosted on G2.com.

The best part about Hadoop HDFS is used to replicate data and its architecture is easy to learn and implement. It helps in processing larger files in a short period of time and it helps us to improve the performance. Review collected by and hosted on G2.com.

Managing or developing complex applications will be challenging. The main thing is it supports only batch processing, not real-time data processing, and is not suitable for minimal data. Review collected by and hosted on G2.com.

HDFS ensures high fault tolerance through data replication across multiple nodes in the cluster, typically maintaining three copies of each data block. This replication strategy guarantees data availability in case of node failures, as HDFS automatically redirects requests to alternative replicas, thus preserving data integrity and reliability. Review collected by and hosted on G2.com.

HDFS is primarily designed for batch processing and may not be the best choice for low-latency or real-time processing needs. Its data replication and block-based storage model introduce inherent delays in data access and processing, which diminish its suitability for real-time analytics or interactive queries. Review collected by and hosted on G2.com.

It completely changes our processing speed as it is a distributed file system that helps to do parallel processing, solving our problem of processing terabytes of data with horizontal scaling. Review collected by and hosted on G2.com.

It is a very costly setup as it needs nodes, which are essentially CPUs that require a lot of money to set up the cluster. Also, the small file problem means we can't store small files due to the meta information handling problem. Review collected by and hosted on G2.com.

HDFS has been around for a while now. There are plethora of documentation and community support available. Review collected by and hosted on G2.com.

Interacting with Kerberos authenticated HDFS can be esoteric and hard to understand initially. Review collected by and hosted on G2.com.

Fault tolerance facility, when data node failed it can use the other as replication factor is 3. Review collected by and hosted on G2.com.

Support only batch processing engine, it cannot produce realtime output. Review collected by and hosted on G2.com.

Hadoop is a widely used open-source framework for storing and processing extensive data sets because of its scalability, cost efficiency, Fault tollerence etc. Review collected by and hosted on G2.com.

Hadoop has some disadvantages like its Complexity, Steep learning curve, high hardware requirement. Review collected by and hosted on G2.com.