Cloudera Distribution for Hadoop Reviews

We are a solution provider and this is one of the systems that we implement for our clients.

Our clients for this product are in the financial industry and they use it to perform cost analysis tasks.

The most valuable feature is Kubernetes.

The price of this solution could be lowered.

We have been using the Cloudera Distribution for Hadoop for five years.

It is a stable solution.

The Cloudera Distribution for Hadoop can be scaled. Our customers are enterprise-level companies and they have about 100 users for this solution.

We offer technical support for this solution to our customers.

We did not use another solution prior to this one.

The initial setup is straightforward.

The pricing is expensive.

Cloudera really has no competition.

I would rate this solution a nine out of ten.

Our primary use case for this solution is to host a big amount of data in our platform, processing, analysis and all of this stuff on the platform.

Cloudera is always developing new tools and supports a wide range of tools. We also really like the Cloudera community. You can have any question and will have your answer within a few hours. Cloudera is better than other competitors because they acquired Hortonworks.

We're processing a huge amount of data on our system. Without the big data environment, we cannot store all of this data live. We have billions of records and terabytes of storage to be used. It's not an option actually for us to have a big data environment. Cloudera is trying to adopt new technologies.

I think the idea of open source tools now is dominating. So Cloudera has to decide how to deal with open-source tools. I subscribe to Cloudera to get an enterprise version but I have found that I can get some of its features from other vendors that would be at a lower cost than Cloudera. They should lower the price. 

We have been using Cloudera for a year. 

It's stable. I have no issue regarding the stability.

It's scalable. You can add more nodes and you can expand your cluster easily.

After we open a ticket, the issue can be resolved very quickly, they have a management portal. I don't contact them directly, but I haven't heard anybody having any problems with it. 

The initial setup is complicated. We needed the vendor to install it themselves. The deployment took around three weeks. Three people were involved because they just follow up and supervise the deployment, but they're not deploying anything. The vendor does it. 

In terms of the advice, I would say to focus on what tools are available on the market. In terms of open-source, most companies are delivering open source technologies and providing support to these tools. Now I have the option to purchase a license for whatever platform for $1. I can deliver it with another small company at a lower cost. If I was the decision-maker, I'd invest in open-source tools. Cloudera and all of these companies are trying to adapt to these big data technologies and open source tools. Cloudera is trying to put it inside their platform so that we can have a compatible solution.

I would rate it an eight out of ten. 

We are dealing with data from the telecom industry. We were using an Oracle system but our volume has increased. We now have a lot of real-time data that needs to be transformed so that it can be made available and used.

The most valuable feature is Impala, the querying engine, which is very fast. We have been able to work with one terabyte of data in less than 20 minutes. The speed makes it easy for us to process all of the data that comes in, in time.

The support is very good.

All of the data has automatic triple replication in order to secure integrity.

There is a maximum of a one-gigabyte block size, which is an area of storage that can be improved upon.

When we are upgrading CDH, there are many things that need to be upgraded and it would be helpful if it were bundled. As it is now, we have to upgrade many different things separately.

I have been working with the Cloudera Distribution for Hadoop for around two years.

It is a stable solution.

The scalability is good and it works on commodity hardware. One of the problems we have right now is that there is a lot of data and we're moving it from our Oracle solution. This means that there is a double cost, in terms of storage, during our transition to working with big data.

We are using a data lake that is a store for all of the data in our organization. There are more than25 projects, with between 25 and 30 people in each one, for a total of almost 1,000 people. All of them are dependent on this solution.

Most of our users are technicians who have problems to solve using the data available to them. A couple of them are data scientists and the remainder are upper management, who do the analysis.

The technical support is very good. Whenever we open a ticket, we get support right away.

We did use another solution prior to this one but it could not keep up with our increase in data.

This suitability of this solution depends on the size of the data that you are going to be working with. If you have going to be working with a huge dataset that contains many gigabytes of data then this is a good solution. For smaller datasets, you should also consider other technologies.

My advice for anybody who is implementing this solution is to take some time to learn it. Beyond that, be sure to contact support if you have any problems because they are very helpful.

I would rate this solution a seven out of ten.

We primarily use it only for big data support for analytical applications.

The feature that we've used quite intensively is Spark, in how it specifically can speed up some of the data to assist with processing.

The one thing that we struggled with predominately was support. Because it was relatively new, support was always a big issue and I think it's still a bit of an ongoing concern with the team currently managing it.

In the next release, I think it would be helpful if there was easier integration into all the other existing data back corners. It will be a big plus as it's a favorite capability. We had to go with a third-party application in order to achieve that.

The stability is problematic. We did encounter quite a lot of issues with the cluster going down quite frequently.

In terms of scalability, if you have enough hardware you can scale out. Scalability doesn't have any issues. Currently, only about 10 people in total are using the solution. So we have about four business users and then four technical people. It's only limited to two environments.

I think there's a lot of room for improvement on the technical support side. Mostly because we don't have a lot of local skills in South Africa that could have supported the solution. It was an issue.

This is our first solution. We tested a bunch of other technologies, but that was our first one and we're still using it.

The initial implementation was straightforward from an application side. There weren't any hiccups. In terms of deployment time, it's going to be difficult to say, because most of it was related to hardware problems. Software took about two months to deploy. We required four people for deployment.

The pricing is very competitive. It's not bad.

We considered working with a few other companies, including IBM Bluemix.

I would recommend the solution given that they've proven the business case and that they've proven the technology. We have found that if you don't use or address the right business code you end up buying a technology that doesn't necessarily solve your business problems.

I would rate the solution seven out of ten. The main reason for not rating it higher is that I think that the overall support is not great and we've found some limitations. It wasn't mature when we started. It's getting there. It's getting better. The main reason for the score of seven is mainly the support as well as the limited functionality.

We make recommendations to clients for using different models of this solution to handle data intelligently.

It gives us the opportunity to offer more options to our clients and create better solution models.

We find CDSW useful and plan to use it as a one-stop application for model build and training. Currently, we use Zeppelin notebook and we want to gravitate to a single application for notebooks.

The Data Science Workbench doesn't support multiple languages. It needs to support multiple programming languages. We were trying to use Scalar and Python for some solutions we wanted to deploy, but they didn't work properly. As a result, we had to come up with other workaround solutions. If the Data Science Workbench supported multiple programming languages our workflow would be easier and the solutions could be better.

Another aspect we would like to see improved is better opportunities for integration. For example, we would like to use H2O machine learning, which is an open-source product, and Cloudera doesn't support H2O.

If they could support H2O and also deploy multi-language support on the Cloudera Data Science that would be great. But the biggest thing that would help right now is H2O support.

Finally, one other improvement I would suggest is integrating data privacy software into  Cloudera. It is not quite complete in this aspect.

From a stability point of view, we know that there is a new product coming out called Unity — or that is the proposed name of the product that merges Cloudera and Hortonworks. We know that this means that some changes will be happening within the environment. We don't believe that they will be radical changes that will affect existing software that we have. It should just be added functionality of Hortonworks integration. But we know at the same time that Cloudera support will be available if we need it.

While we have not yet done a lot to scale the solution, we think that is going to be quite scalable because it's working on a distributed architecture. 

We will probably start with 10 or 15 users once we roll the solution out into production, which will probably be at the end of this week. Afterward, the user base will be growing quite large by double digits in percentage. But that is just to start with. Over a few years, we plan to start thinking about rolling out our experiences to our international businesses as well. This would be a substantial increase in user base.

At the moment and for what we have been able to experience, technical support seems to be fine. I would rate it at between seven to eight out of ten.

We did not consider other solutions.

The initial setup was difficult and we didn't like it. That is only because we implemented it with other software solutions outside Cloudera and needed to do the integrations. 

We are still battling with working out problems with some integrations after eight weeks. It's up and running, but we're optimizing, so that is why I'm saying it's probably medium to complex. But that was the situation for us and our particular needs. It may not be as complex for other businesses at all.

We have been working through the implementation with our own team.

We did consider other opportunities. Although we are quite comfortable with our current solution we may look at Hortonworks again, but that is not yet confirmed. We believe, from what we have read and what has been advertised, that Hortonworks and Cloudera are going to eventually merge and become one product. According to some sources, it has already happened.

We're simply trying to get the best of both worlds.

I would say that the product as it currently is should rate at an eight out of ten. The reason that score is not higher is because of the workarounds that we have to do when it comes to certain models that do not support using multiple programming languages. For example, in a single notebook, it is inflexible if you want to use other program languages. 

As far as other advice for people considering this solution, I would say take a good look at your business need before you decide on this technology and which solution to choose. Make sure that you are not already able to solve for your particular, identified needs using your existing technology before even considering a change.

You want to be sure you're applying the technology to the right business case because of actual need and not just change for change's sake.

We primarily use the solution for external storage.

The search function is the most valuable aspect of the solution.

The user infrastructure and user interface needs to be improved, as well as the performance. The GUI needs to be better.

We did not previously use a different solution.

The initial setup was complex, due to the user interface. We were doing a POC, so we're still doing the deployment.

I would rate this solution seven out of 10. There's tons of room for improvement.

I've been working on the software installation from the beginning, and we have a client for global supply change, so we get information from Telefonica's sales and distributions. Getting all that information into this system allows us to process it, get KPIs, and create outgoing information for business intelligence tools. 

In the cloud provider enterprise we get all the information from the gamers, like delays, response, and information from the games. It allows us to see if gamers are having trouble, high latency or any other kind of issue. They test that and get information about the issues in order to solve them.

I like the combination of all the tools that allow me to provide solutions and enable me to solve the use cases I'm working on. You need tools or components to foresee everything, and they are all in our emails. Sometimes you try several of them, and sometimes one will work better than the other. So you have to test the tools to see what works for you. 

We experienced many issues when we started working with Hadoop 3.0 in the Cloudera 6.0 version, so there are a lot of things that need to improve. I believe they are working on that. 

It's been quite easy to install. We only had to follow the instructions and there weren't many problems. That's important for us.

I will rate this solution a nine out of ten because nothing is ever perfect. You will always face problems, but I'm quite happy with Cloudera. 

I'm part of the IT team at my company, and our primary use case of this solution is building infrastructure for advanced analytics, where we copy data from our data warehouse that is now our relational database. We copy it to the Cloudera Distribution for Hadoop and then analyze it with Python and machine learning. 

The features I find most valuable is that the solution is that it is easy to install and to work with. It starts with the installation and from there on the management is very simple and centralized.

I would like to see an improvement in how the solution helps me to handle the whole cluster. For example, when I'm going down to a specific tool, like Kafka, for example, the Cloudera manager doesn't really help me. Then I have to use Google with other Kafka knowledge and tools. 

It is a very stable solution.

Not many people are currently using this solution at my organization, but I do believe it is scalable. I don't, however, have experience with upgrading or adding users. 

My problem is that I started using Cloudera Express without technical support and then I purchased the Enterprise edition through another company. So now I don't really have access to Cloudera support, even though I hardly need to use it. 

The initial setup was simple, but we had trouble implementing the cables in the Hadoop solution.

I had a bad experience connecting the Cloudera Distribution for Hadoop cluster to my other resources in the company, like the active directory or firewall. I would like to see the outside environment to be easier to handle. I will rate this eight out of ten because the solution doesn't cover everything. It is a very complicated solution because it contains a lot of internal tools.