Hadoop

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 helped us in debugging issues and solving problems at a faster rate.

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 process of configuration in a clustered environment. Additionally, from my experience, I have noticed that Hadoop provides great scalability and redundancy. Also, it provides enterprise-level support from a variety of vendors. Lastly, I think that a great positive fact of Hadoop is its horizontal scaling.

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 of servers and machines that are needed to process, store, and analyze data sets. It also handles explosions in data with big data technology. Apache Hadoop is good at handling all node failures as well.

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 capacity to process data and the speed are great without having to do any settings tuning or optimization. It also doesn't require any on-site hosting, making it a great hands off solution.

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 more, of course.

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 to support SQL queries in hopes we could leverage the existing codebase. Azure was just more expensive but again was easier to setup. In the end, cost won out because even though the competition was easier to set up, it's not like Hadoop was that much harder to setup.

Hadoop Clusters are more reliable and allow faster access to data than what we have seen with Vertica. Hadoop also doesn't require fast storage or high-end computer resources, so it is much cheaper on the hardware side. Hadoop is cheaper than Vertica licensing as well.

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 even on IAAS cloud. The main challenge, however, is to manage user access and to maintain security.

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 for facilitating Big Data aggregation.

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.

Amazon Redshift is some what closer to Hadoop. But to analyze Petabytes of data Hadoop as better performance.

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 indefinitely. I'm not sure that any of the other "big data" technologies out there have as many certified components or work with such a diverse collection but as I said I am pretty new to this and so have only tertiary knowledge of competing products.

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 way.

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 provides very high bandwidth to support MapReduce workloads.

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 modules in Hadoop are designed with a fundamental assumption that hardware failures are commonplace and thus should be automatically handled in software by the framework, relieving the developers from handling every edge scenario that can occur in a large distributed system.
Hadoop can be deployed in a traditional onsite datacenter as well as in the cloud. The cloud allows organizations to deploy Hadoop without hardware to acquire or a specific setup expertise. Many vendors who currently have an offer for the cloud include Microsoft, Amazon and Google.

Hadoop has a master slave architecture and comes with more features than Splunk.

Fast and scalable. More reliable as compared to the other products I have used.

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 making business decisions in an organization. These data are often personal data, which are useful from a marketing viewpoint to understand the desires and demands of potential customers and in analyzing and predicting their buying tendencies.

I think Hadoop processes it very efficiently.