Hadoop HDFS Reviews & Product Details

Hadoop is very fast and flexible towards the data for implementing and improve the data quality Review collected by and hosted on G2.com.

As per my experience Hadoop has no disadvantages as per my experience Review collected by and hosted on G2.com.

Hadoop HDFS architecture is easy to learn and implement. HDFS has a bigger block size which increases the speed of data allocation (by reducing the latency). Hadoop HDFS does not only provide you the storage but also gives you an option to encrypt the data at rest as it supports KMS/KTS/RangerKMS encryption. Review collected by and hosted on G2.com.

Hadoop HDFS is only for big files hence if you are storing a lot of small files or your ETL source is then HDFS performance gets impacted. A large number of small files increases the name node memory overhead and finally leads to slower throughput as it causes GC slowness. Hence my recommendation is to only use HDFS when you know you have to store or process large files ( >128MB) Review collected by and hosted on G2.com.

Easier to handle the bulk of data, and it's innovative features. Review collected by and hosted on G2.com.

Syntax differences as compared to trivial Java coding. Review collected by and hosted on G2.com.

Simplicity, re-use abilities, manintain, security, atomicity, integrity Review collected by and hosted on G2.com.

Nothing. . . . .. . . . Review collected by and hosted on G2.com.

HDFS is very user friendly it was very fast for data storing and data storage vary fast io over all my experience for hdfs is very good Review collected by and hosted on G2.com.

Some time it will legging for some data process some external connections and all. Review collected by and hosted on G2.com.

Best part about Hadoop Hdfs is used to replicate data. It can be used to manage high data set(large data set). It is one of the main components of Apache Organization use it for high scalability. Large data from megapyte to pegabyte can be stored using this product and it also runs on low cost system. It helps tracking down the system faults and strengthen the system. It processes large amount of data quickly. It also helps in distributed storage Review collected by and hosted on G2.com.

Well I am a current user of Hadoop Hdfs and till now I haven't found any cons about this product. Review collected by and hosted on G2.com.

Hdfs is highly scalable as it supports horizontal scaling. HDFS also boasts encryption at rest which is helpful as our data is secured so we do not have to worry. It also has some integrations to mask the PII data to hide sensitive information Review collected by and hosted on G2.com.

Hdfs gets slow at times and the stack support folks takes a lot of time to find the root cause. Hadoop slowness also affects the throughput and ultimately affects business as reports get delayed Review collected by and hosted on G2.com.

Everything goes smooth either while copying files from Multilocation Review collected by and hosted on G2.com.

It has dependencies on Linux, which is why we need to learn Linux code while working on edge node Review collected by and hosted on G2.com.

It helps Big Data processing be easier. Earlier, processing large files would take a lot of time to process, but with HDFS, we gained a lot of performance optimization. Review collected by and hosted on G2.com.

There is a lot of I/O operations while processing Big Data. In this aspect, Spark is performing much better due to its in memory calculations. Review collected by and hosted on G2.com.

It is really easy to run shell-based as well as code-based applications through autosys. Also, you can pass output from one step to other steps in autosys. Review collected by and hosted on G2.com.

Overall as a scheduler I didn't find anything which is not supported in autosys. Review collected by and hosted on G2.com.