Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.
Apache Spark can be considered as an alternative because of its similar capabilities around processing and storing big data. The reason we went with Hadoop was the literature available online and integration capability with platforms like R Studio. The popularity of Hadoop has …
Different departments of my organization have been getting the benefit from Apache Hadoop as it serves the purpose of saving lives when large amounts of data is unable to be converted and processed in a timely manner from a node or a simple computer. Hadoop also has an easier …
I feel that this is a highly reliable and scalable solution computing technology that is highly capable of processing large data sets across multiple servers and thousands of machines in a well-defined and distributed manner. Apache Hadoop can automatically scale up the number …
Spark is a good alternative to Hadoop that can have faster querying and processing performance and can offer more flexibility in terms of applications that it can support.
Google BigQuery has also been a great alternative and is especially great in terms of ease of use. The …
MariaDB - Better to be already in the cloud you will use it for. Issues have improved as it has matured over the year.s CockroachDB - Not nearly as performant (even out of the box) as Apache Hadoop. More configurations required just to make it work. In memory cacheing is an issue.
Hadoop utilizes a SQL structure, which is great. You pay less for the services, but it's definitely less of an enterprise-level option and more just a good place to store your seldom-used data. Teradata and AWS are a lot faster in returning queries than Hadoop, but you pay …
Vice President, Chief Architect, Development Manager and Software Engineer
Chose Apache Hadoop
Hands down, Hadoop is less expensive than the other platforms we considered. Cloudera was easier to set up but the expense ruled it out. MS-SQL didn't have the performance we saw with the Hadoop clusters and was more expensive. We considered MS-SQL mainly for its ability …
When comparing to the sophistication of IBM GPFS (Spectrum Scale) to Hadoop, it is clear that Spectrum Scale is a much better choice. That is maybe something you don't want to hear, but in all of our research, this has been the final decision of the client.
Hadoop offers a scalable, cost-effective and highly available solution for big data storage and processing. The use of a non-proprietary physical layer greatly reduces dependency on technology. It also offers elastic dimensioning capability when deployed on virtual machines or …
I haven't worked with other Big Data aggregation services like Hadoop. As far as I know, Hadoop is the leading choice in this field with good cause. There is a lot of community support, custom modules, paid consultants, free and paid training. All this makes it an ideal choice …
No SQL database were evaluated along with MPP platform. Hadoop performs very well compared to the other platforms. Also since lot of investment goes into Hadoop there is a good chance of getting what one needs from the developer community.
As I am new to the hadoop ecosystem I have not used or evaluated any other similar products at this time. This was handed to me from a previous much older installation that was very under utilized. Our new platform will be working the new cluster much harder with jobs that run …
Hadoop was a cheaper alternative to Amazon. Since I had to pay for every minute I use with Amazon, I had to make sure multiple times that the code was good enough before I purchased with Amazon. But since Hadoop was available on the cluster, I had the opportunity to code on the …
Hadoop being open source, is cheaper to use and do POCs for clients. Cloudera, Hortonworks and MapR also compete to contribute to open source Hadoop and keep their product conceptually similar to Hadoop.
Apache Spark has an in memory processing model, making it powerful for lightning fast data processing. Apache Spark also exposes Scala and Python in APIs which is one of the most commonly used programming languages in data analytic and data processing domains.
Not used any other product than Hadoop and I don't think our company will switch to any other product, as Hadoop is providing excellent results. Our company is growing rapidly, Hadoop helps to keep up our performance and meet customer expectations. We also use HDFS which …
Hadoop provides storage for large data sets and a powerful processing model to crunch and transform huge amounts of data. It does not assume the underlying hardware or infrastructure and enables the users to build data processing infrastructure from commodity hardware. All the …
Processing of big data has been the ultimate need for the me choosing Hadoop. Big data is massive and messy, and it’s coming at you uncontrolled. Data are gathered to be analyzed to discover patterns and correlations that could not be initially apparent, but might be useful in …