Hadoop Features

Read what people say are the most valuable features of the solutions they use.
ShivanshSrivastava says in an Apache Spark review
Sr. Software Engineer at a tech vendor with 1-10 employees
The most valuable feature is the Fault Tolerance and easy binding with other processes like Machine Learning, graph analytics. The community is growing and hence executing ML in a distributed fashion is quite good. View full review »
Rosemary Walsh says in an Apache Spark review
Portfolio Manager, Enterprise Solutions Architect with 10,001+ employees
It supports streaming and micro-batch. View full review »
Sumanth Punyamurthula says in an Apache Spark review
Director - Data Management, Governance and Quality with 10,001+ employees
Powerful language. View full review »
Sumit Chaudhuri says in a Cloudera Distribution for Hadoop review
Lead Consultant - Product Development at a tech services company with 10,001+ employees
Keeping multi copies of the file and tools of map reduce like PIG, HIVE due to their flexibility it is easy to develop the application with less or almost no knowledge of Java and Sql. And capability to handle huge data size. View full review »
reviewer894894 says in an Apache Spark review
User
Machine learning, real time streaming, and data processing are fantastic, as well as the resilient or fault tolerant feature. View full review »
Lubos Musil says in a Hortonworks Data Platform review
Solution Architect with 10,001+ employees
I have no preferences towards any feature. View full review »
Abhijit Nayak says in an Apache Spark review
Manager | Data Science Enthusiast | Management Consultant at a consultancy with 5,001-10,000 employees
Distributed in memory processing. Some of the algorithms are resource heavy and executing this requires a lot of RAM and CPU. With Hadoop-related technologies, we can distribute the workload with multiple commodity hardware. View full review »
MSegel says in a MapR review
Founder at Chicago area Hadoop User Group (CHUG)
MapR’s strength comes from their file system. Because they start with the raw disk, they are able to expose the storage through various APIs and have the ability to lockdown and secure the file system better than the Apache derivatives, which store the file blocks above the Linux file system. Because of MapR’s POSIX compliant file system, they can support read/write files over the WORM storage of their competitors. In addition, they remodeled how to track and store the blocks. So the NameNode isn’t a single point of failure and you can store a magnitude of multiple orders of small files before you can cause a volume to have issues. Note: This is a cluster volume, not the entire cluster. Fill up the NameNode with lots of small files upon an Apache release, and you lose the entire cluster. View full review »
Subhasish Guha says in an Apache Spark review
Big Data and Cloud Solution Consultant at a financial services firm with 10,001+ employees
DataFrame: Spark SQL gives the leverage to create applications more easily and with less coding effort. View full review »
Matthew Cloney says in an Amazon EMR review
Data Science Engineer
The ability to resize the cluster is what really makes it stand out over other Hadoop and big data solutions. You can do it very easily and quickly. It is a managed service from AWS Amazon so it removes a lot of the headaches of configuring the different environments for all the nodes in the cluster, and frees you up to do other things. You can use it. You can set it up in minutes and it's very straightforward. View full review »
Balan R says in a Netezza Analytics review
Manager-Projects,Data Analyst & DBA at a tech services company with 10,001+ employees
Currently, we are using Netezza to the utmost. We first used it for our data warehouse. Then we moved on to doing analytics. Also, we are now doing some packages on it. Currently we are using it for multiple purposes, but, mostly it's used for reporting. It is a back end for our SSIS, MicroStrategy,, Tableau. All of these are connecting to get the data. To do so we are also using our analytics which is built on the data. View full review »
Rafelito Comendador says in a Netezza Analytics review
Project Manager
* Speed * storage * RAM all of which contribute to large capacity. View full review »
Sumit Pal says in an Apache Spark review
Architect at a healthcare company with 51-200 employees
ETL and streaming capabilities. View full review »
Russell Derouin says in a Netezza Analytics review
Senior Enterprise Architect at a retailer with 10,001+ employees
* Its ability to process and query large amounts of structured data. * Low administrative support in terms of query optimization and indexing support. Indexing and data partitioning is built into the firmware. * Data compression. It was relatively impressive. I think at some point we were getting 4:1 compression if not more. View full review »
Vishnu C.G says in an IBM InfoSphere BigInsights review
BigData Consultant at a tech services company with 10,001+ employees
InfoSphere Streams was the one core product from the platform in which we were using. We were building a real-time response system and we built it on InfoSphere Streams. View full review »
Reviewer742794 says in a Hortonworks Data Platform review
User at a comms service provider with 10,001+ employees
A few of them, namely: Hive/Tez, HBase, Ranger, Yarn and Ambari. Ambari helps managing the platform, Hive is very easy to use. Ranger for security; with Ranger we can manager user’s permissions/access controls very easily. View full review »
Vikash Pandey says in a Hortonworks Data Platform review
BigData(QA & RnD) with 51-200 employees
* Ambari Web UI: user-friendly * Views for Hive, Tez, Pig * Spark and Ranger View full review »
Jyothish Kalavoor says in a MapR review
Technical Architect at a tech services company with 10,001+ employees
MapR-DB is a NoSQl datastore on top of MaprFS. The data can be updated and random data can be picked very fast. MapR-DB stores data in MapR-FS and it does not have region server like in HBase. View full review »
BigDataConsult393 says in an Apache Spark review
Big Data Consultant at a tech services company with 501-1,000 employees
The good performance. The nice graphical management console. The long list of ML algorithms. View full review »

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