People who want to buy the solution must hire or work with someone who understands the architecture as per the use case. It should be good for the long run. Once Hadoop is set up, we can change the configuration, but the architecture cannot be changed frequently. We must invest more in the architecture. Once properly built, we can build or develop anything on it. Architecture is important for Hadoop. If the product is set up well, we will not find difficulties later. Overall, I rate the solution an eight out of ten.

If you plan to use Apache Hadoop, purchase the license from Cloudera because they provide you with technical support.

I rate the overall solution an eight out of ten. 

My company is using both Apache Hadoop and Oracle Exadata.

I'm unsure which version of Apache Hadoop I'm using, but it could be the latest version.

Currently, the solution is deployed on-premises because here in Bangladesh, there's a limitation with transferring data outside of the country. As far as I know, there's no cloud solution internally in Bangladesh, so if you want to use a cloud solution here, you'll have to move your data outside Bangladesh, and this is why Apache Hadoop is still deployed on-premises.

More than fifty people use Apache Hadoop directly, particularly the IT and analytics expert teams. The solution is being used by developers, people in operations, and people who maintain security.

In my company, Apache Hadoop is not fully implemented yet. It's still in the implementation phase and at least for the next two to three years, there isn't any plan of discarding it.

I'm giving Apache Hadoop a rating of seven out of ten.

I don't have any recommendations currently for people who want to implement Apache Hadoop because I'm still in the learning phase and I don't have much knowledge yet. The IT team in my company is also struggling every time in terms of preparing everything and still needs help from external vendors because the team isn't an expert on Apache Hadoop yet. My company's expertise is in Oracle Exadata because usage of that product started in 2002 or 2003.

My company is a customer of Apache Hadoop.

We can't use Apache Hadoop for everything, like storage and data errors. But we can use some tools that are native to Hadoop, like Kafka.

For the current situation, I'd rate it a seven out of ten. 

However, five years ago, I would have rated it a nine out of ten. Back then, I was working with it fully. But now we're used to working with cloud systems. Creating servers is more difficult nowadays.

My position in the company falls under the research and development of new technologies and solutions. I investigate, research, download, and read information and reports as part of my job.

Our company has a big data business division, and we propose, develop, and implement things which are related to big data projects. We are using Cloud Hadoop open source versions, distributed versions, and commercial Hadoop distributed versions. We propose all these versions to our customers from any industry.

Our focus is on the public sector. Big data is our strong point in Korea. Our company is the leader in big data technology, including infrastructure and visualization. This is a solution we provide to our customers. We are also in partnership with IBM. Our main focus is on Apache Hadoop.

We provide Apache Hadoop to our customers. I work for a systems integrator and technical consulting company.

Overall, our satisfaction with this solution is so-so. We continuously investigate new technologies and other solutions.

The Hadoop open source version was implemented in 95% of our company's customer base. Our remaining customers had the local vendor's Hadoop platform package implemented for them.

Our company is in the big data business. Before the big data business back in 1976, we implemented BI (business intelligence), DW (data warehouse), EIS, and DSS (decision support system), so we are in partnership with IBM.

I don't have advice for people looking into implementing this solution because I'm not in the business unit. I'm in the research field. My role is to plan new technology and provide consultation to our customers for big data projects in the early stages.

My rating for Apache Hadoop from a technical standpoint is eight out of ten.

There was a scenario when the product was essential for my company's data analytics needs. Before my company makes any web solution available in production, we have prototypes and replicas of the application in lower environments. My company uses Apache Hadoop to ensure that the lower environments in which we operate are secure and accessible only by those people in our company with valid credentials.

I suggest that those planning to use the product first understand the tool's features and capabilities and then choose the right configuration to avoid misconfigurations.

The product's integration capabilities are good since I see that we have not faced any time outs or downtime in our company when using the tool.

My company uses the tool to have security and the right availability, which means availability to the right people at the right time. So I think our expectation was met. The value we got from the tool was what we wanted in our company.

My company started to use the tool expecting that it would offer security and ensure its availability to the right people at the right time. I believe that the tool was able to meet our company's expectations, so we got the value that we expected the product to deliver.

I rate the tool a seven out of ten.

I would recommend this solution for data professionals who have to work hands-on with big data.

For instance, if you work with smaller or more finite data sets, that is, data sets that do not keep updating themselves, I would most likely recommend R or even Excel, where you can do a lot of analysis. However, for data professionals who work with large amounts of data, I would strongly recommend Hadoop. It's a little more technical, but it does the job.

I would rate Apache Hadoop an eight out of ten. I would like to see some improvements, but I appreciate the utility it provides.

I would rate it a nine out of ten because of the complexity, but technically, it is okay.

Try open-source Hadoop first but be aware of greater implementation complexity. If open-source Hadoop is "too" complex, then consider a vendor packaged Hadoop solution like HortonWorks, Cloudera, etc.

We use Hadoop's open-source version and do not receive direct support from Apache. There are good resources on the web, though, so we have no problem getting help, but not directly from the company.

If you want to use big data on a larger scale, you should use Hadoop. But you could use alternatives if you're going to use big data to analyze data in the short term and don't need cybersecurity. You could use your cloud's features. For example, if you are on Google or Amazon Cloud, you could use in-built features instead of Apache Hadoop. If you are, like us, working with banks that don't want to use the cloud or some commercial clouds or have large-scale data, Hadoop is a good choice for you.

I rate Apache Hadoop an eight out of ten because it could be more user-friendly and easier to install. Also, Hadoop has changed some features in the commercial version.

The best advice is not to start a project based on Apache Hadoop alone. It is based on technology, and needs a skilled team.

Overall, I rate the solution an eight out of ten.

Implement for defined use cases. Don't expect it to all just work very easily.

I would rate this platform a seven out of 10. On the one hand, it's the only place you can use certain functions, and on the other hand, it's not going to put any of the other ones out of business. It's really more of a complement. There is no fundamental battle between relational databases and Hadoop.

My advice to others is if you have a strong engineering team then this solution is excellent.

I rate Apache Hadoop an eight out of ten.

Try, try, and try again. Experiment with MapReduce and YARN. Fine tune your processes and you will see some insane processing power
results.

I would also recommend that you have at least a 12-node cluster: two master nodes, eight compute/data nodes, one hive node (SQL), 1 Ambari dedicated node.

For the master nodes, I would recommend 4-8 Core, 32-64 GB RAM, 8-10 TB HDD; the data nodes, 4-8 Core, 64 GB RAM, 16-20 TB RAID 10 HDD; hive node should be around 4 Core, 32-64 GB RAM, 5-6 TB RAID 0 HDD; and the Ambari dedicated server should be 2-4 Core, 8-12 GB RAM, 1-2 TB HDD storage.

It's good for what is meant to do, a lot of big data, but it's not as good for low latency applications.

If you have to perform quick queries on naive or analytics it can be frustrating.

It can be useful for what it was intended to be used for.

I would rate this solution a seven out of ten.

Our general suggestion to any customer is not to blindly look and compare different options. Rather, list the exact business needs - current and future - and then prepare a matrix to see product capabilities and evaluate costs and other compliance factors for that specific enterprise.

I would recommend this product to others. I would rate it as an eight out of ten. 

We're just a customer. We don't have a business relationship with Hadoop. 

My day-to-day job is data modeling and architecting.

Originally we used it as an open-source solution. We downloaded it, then we went for a commercial version of it.

In terms of advice, I'd tell other potential users that whether the solution is right for them depends on a few items. If the data volume is too big, it's IoT data, or the stream of data is too much, this solution can handle it and I would definitely recommend Apache Hadoop. 

Recently, in the last 18 months, I've been working with the Snowflake, it's a Data Lake project, and I am really impressed with that one. I got a certification so that we started using Snowflake set for our Data Lake environment.

I'd rate the solution eight out of ten.

First, understand your business requirement; second, evaluate the traditional RDBMS scalability and capability, and finally, if you have reached to the tip of an iceberg (RDBMS) then yes, you definitely need an island (Hadoop) for your business. Feasibility checks are important and efficient for any business before you can take any crucial step. I would also say “Don’t always flow with stream of a river because some time it will lead you to a waterfall, so always research and analyze before you take a ride.”

I rate Hadoop seven out of 10. It's very good, but it could always be better. To anyone considering Hadoop, I recommend that you be mindful of what you're trying to achieve.

On a scale from one to ten, I would give Apache Hadoop a nine.

We use the on-premises deployment model. It's a requirement for the company we work with, which is a bank. Often customers demand we work with on-premises deployment models.

I'd rate the solution seven out of ten. In terms of the ability to build middleware and offer scalability, it would be 10 out of 10 from me. However,  if you take into account only the visualization, I'd only rate it at three or four out of ten.

I would give this product a rating of eight out of ten. It would not be a ten out of ten because of some problems we are having with the upgrade to the newer version. It would have been better for us if these problems were not holding us back. I think eight is good enough.

The solution is perfect for those dealing with a huge amount of data. Still, you need to check to make sure it meets your company's requirements. You need to understand them before actually choosing the technology you'll ultimately use.

Overall, I would rate the solution at a seven out of ten.

Usually, people need to study and prepare for a few use cases and compare multiple ecosystems before choosing one. When people think of using a big data solution, Hadoop comes to mind. For certain use cases, Hadoop is comparable with other technologies. For example, when building a sort of real-time data warehouse — an enterprise data hub —, people don't think about using Hadoop directly. People often use solutions like DROID for building.

At the end of the day, you need to compare technologies — existing technologies against their use cases. You need to study your use case and select the technology inside of Hadoop that will fit your use case. You may find another ecosystem that solves your problem, just keep in mind, Hadoop is not the only solution, there are a lot of solutions. It depends on the use case. 

Overall, on a scale from one to ten, I would give Hadoop a rating of eight.

We use the on-premises deployment model.

We're more inclined towards an operational data source to fill our customer's needs. Hadoop is good for analytics and some reporting requirements. 

It's a good solution for those needing something for the purposes of management reporting.

I'd rate the solution eight out of ten.

I would suggest using this product. We were able to use this for petabytes of data.

I've used the solution under cloud, hybrid and on-premises deployment models.

I'd recommend the solution, but it depends on the company's requirements. If you don't have huge amounts of data, you probably don't need Hadoop. If you need a completely private environment, and you have lots of big data, consider Hadoop. You don't even need to invest in the infrastructure as you can just use a cloud deployment.

I'd rate the solution seven out of ten. I'd rate it higher if it had a better user interface.

I rate it an eight out of 10. It's huge, complex, slow. But does what it is meant for.