Hadoop HDFS Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Hadoop's storing of large amount of data into a clusters that makes data as fault tolerant, secure and faster processing and scalability Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.