Cloudera Distribution for Hadoop Reviews

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 use it for machine learning.

The data science aspect of the solution is valuable.

The governance aspect of the solution should be improved. The pricing renewal notices can also be a bit challenging for us. It requires providing a substantial amount of notice for renewal, which has been a notable difficulty in our experience.

We have been using the solution for the past five years.

The solution is highly stable. I rate it a nine out of ten.

The solution is highly scalable. I rate it a perfect ten. We have 20 users for the solution in our company.

The technical support team is great.

Positive

We have worked with Hortonworks.

The initial setup was neither straightforward nor overly complex. It took us a few days to complete. I wasn't involved in the configuration; another team handled it. There may have been technical issues related to the clusters. We have a technical team with seven administrators.

The solution is fairly expensive.

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

We are using this solution for storing Big Data in one centralized location.

It has been helpful in allowing data storage in one centralized location with data lakes and all of the surrounding applications.

All of the data processes are being stored into the Big Data Lake.

It allows us to store huge amounts of data, which is an advantage.

They have BI (Business Intelligence) tools. There are many AI tools.

We are able to connect and analyze the data to get reports. The reports are very good.

The main advantage is the storage is less expensive.

The performance can be improved. We have experienced some performance issues. It is not as sophisticated as Oracle Sybase.

Currently, we are using many other tools such as Spark and Blade Job to improve the performance.

The setup could be simplified, it's complex.

The security needs to be improved.

I have been using this solution since 2015.

It's a stable solution.

Scalability is good. It's replicated and by default, with Big Data there is a replication factor.

Over the years we have grown, when we started we had 10 nodes now we have increased to a large number of nodes.

Technical support is good. I have been able to learn from them. As a developer, I am learning every day.

I would rate the technical support a ten out of ten.

Previously we were using Oracle Sybase SQL. We switched because now, we have introduced Big Data.

The initial setup was complex.

It's not as simple as Oracle Sybase.

It's a complex architecture because you have raw data and many engines.

When comparing with Oracle Sybase and SQL, it's cheaper. It's not expensive.

I am a part of security and software development. 

We are currently considering migrating to the cloud, and planning on using Microsoft Azure, mainly for the Big Data component.

I would rate this solution a five out of ten.

CDH has a wide variety of proprietary tools that we use, like Impala. So from that perspective, it's quite useful as opposed to something open-source. We get a lot of value from Cloudera's proprietary tools. 

Integration is one of the main things we struggle with because we're working with several other environments. For example, we've got an MPP environment outside the Hadoop environment. Many cloud-based platforms like Azure are fully integrated with technology that gives you MPP machine learning and data lakes all in one environment. We've got on-premises IBM solutions and Cloudera, so it isn't easy to integrate. It would be useful if Cloudera had more tools like SQL Engines that offer the traditional relational database. We have to do a lot of work preparing the data outside Cloudera before getting it into the platform. And ideally, we should get as much raw data as possible into the platform before we can do the engineering, so we have machine learning and model training.

I've been using CDH for about two years, or rather, I manage the team that uses it.

We haven't had any issues with Cloudera. It's a solid product. 

Cloudera is dependable, and it's completely scalable.

We have engaged the technical support based in the UK. My team hasn't worked with them directly, but the administration team has. To my knowledge, they're fairly responsive. 

I rate Cloudera Distribution for Hadoop eight out of 10.

It offers a pre-build distribution. Even the support is there for the solution, whatever the issue is that you face. And apart from that, they have a platinum best practice to manage tools and all these things.  

The company is struggling to keep up with the upgrades of various components, and they are not willing to invest more in Cloudera.

The company is still switching from traditional methods to cutting-edge technology. While the deployed product is generally functional, there are instances where it presents difficulties. For example, the high SPs do not allow for metadata patching once it is created in the panel. This restriction limits our ability to make changes to the metadata.

I am aware that some companies are using open-source alternatives, which offer more flexibility. So, product maturity with cutting-edge technology will take more time.  

The primary concern is the cost. If you have the budget and are willing to pay for it, then it's fine. However, if we don't want to spend more money, it's not the best option.

I've used it. 

Customer service and support were able to fix whatever the issue was. But they haven't used the solution, so they cannot do anything in whatever is implementation. Because they are using self-managed open-source technology. There's no order or feature development by now. So wherever there's a limitation from the product, like the tool side, the kind of environment, they are not able to do anything.

Positive

It is an expensive product.

For using Cloudera, it depends on what you want to use it for. If you're looking for something easy to manage and operate in the cloud environment, then Cloudera is a good option. 

You don't need to do much; you can just deploy it and go. From my perspective, it depends on your use case and how you see your data needs, as well as how you manage cloud data technologies and work with different departments, teams, and identity features. If Cloudera satisfies your requirements and you have no issues with it, then go for it.

Overall, I would rate it a seven out of ten.

It is helpful to gather and process data.

We used to collect data in small cases, and with Cloudera Distribution for Hadoop, we used data on large scales. It helps to store and protect the data and is helpful for processing.

The Cloudera Distribution for Hadoop is valuable.

There are multiple bugs when we update.

We have been using this solution for three and a half years and using version 6.3. It is deployed on-premises.

It is a stable solution.

It is a scalable solution. It is the best solution for larger companies. There are about 3000 users, and there are medical teams for medical data with about 1000 users. We require about six people for maintenance and deployment.

Cloudera's support is helpful, and I rate the technical support a nine out of ten.

Positive

The initial setup was straightforward and not an issue.

We have seen a return on investment.

I rate this solution a nine out of ten, and it is a helpful solution.

In my previous organization, we used Cloudera Distribution for Hadoop
for compiling website logs and application logs. We used it for log analytics.

We're now able to store large volumes of data through Cloudera Distribution for Hadoop. We're able to push large volumes of data to the platform, and that used to be a challenge, especially when storing a terabyte of information. This is the area where Cloudera Distribution for Hadoop improved the organization.

The feature I found most valuable in Cloudera Distribution for Hadoop is the Cloudera Manager. It's a good component because it makes log management easy. It's really useful as a management and monitoring console.

The setup and administration were not easy with Cloudera Distribution for Hadoop. They could be improved.

The solution has a limited feature list, so having more features is something I'd like to see in the next release of Cloudera Distribution for Hadoop.

I've been using Cloudera Distribution for Hadoop for two years. I'm still using it.

Cloudera Distribution for Hadoop seems to be a stable product.

Cloudera Distribution for Hadoop is really easy to scale. We can add more servers to it, so it's scalable.

I don't have experience contacting the technical support team of Cloudera Distribution for Hadoop.

The initial setup for Cloudera Distribution for Hadoop was easy for us because we outsourced the work to the vendor. All the nitty-gritty was taken care of by them.

We implemented Cloudera Distribution for Hadoop through the vendor. Deployment was done by an integrator. It usually doesn't take a lot of time. It usually takes just a day to deploy the solution.

Our implementation strategy for Cloudera Distribution for Hadoop was more into outsourcing. For example, the hardware, including its management, was outsourced, so the admin, data management, support, etc., were also outsourced. We were looking into having the application done in-house, with the team. We were looking at a one-year implementation plan to move more and more governance and data sets into Cloudera Distribution for Hadoop. Every quarter, we planned to have other features reintroduced into the platform.

Two people did the installation and two people did the deployment. It was deployed in a single location, and we initially had ten users of Cloudera Distribution for Hadoop.

It's tricky to derive the ROI from Cloudera Distribution for Hadoop, because in analytics, it's a little difficult to determine that this is the investment, and we're increasing the footprints and the revenue. It's very difficult to evaluate.

Cloudera Distribution for Hadoop is expensive. There are a lot of costs involved. For example: apart from the standard licensing fees, there are support costs involved, and support could be for three years, five years, etc., so support is a pretty large part of the contract.

We didn't evaluate other options before choosing Cloudera Distribution for Hadoop.

I'm using Cloudera Distribution for Hadoop.

The advice I would give to others looking into implementing or using Cloudera Distribution for Hadoop is for them to opt for a cloud variant, particularly something scalable for Azure, because of the ease of deployment and ease of setup. Procuring Cloudera Distribution for Hadoop is also a challenge unless the customer goes for its cloud version.

I would rate Cloudera Distribution for Hadoop six out of ten because of its limited features. If they can enhance their feature list, that would improve their score.

This product is a framework for edge AI, it comes with multiple ecosystems as a project. I'm a senior data architect manager and we are consultants. We offer Cloudera to our customers but we don't have a partnership with them. 

The best feature is the layer shared experience. If you have a cluster available on-prem or in the cloud, you can manage that security layer using the shared SDX and it provides flexibility. New features are constantly being added. 

The only thing that needs improvement is the cost, it's a very expensive solution and one of the main reasons companies are not attracted to the product. 

This product has been around for a long time so it's very mature and stable.

The scalability is very good. 

The initial setup has become easier although you need a dedicated admin to maintain and manage the solution because it's a framework and not a single product. Deployment nowadays is much smoother with the PaaS offering in the public cloud, so you can carry out the deployment with an in-house team. The deployment only takes a day but a company is unlikely to go with the default so the solution needs fine-tuning which can take a couple of weeks. 

For enterprise organizations that can bear the cost, it's a good solution. A smaller company wouldn't be able to afford the licensing fees. You can get a free trial for 60 days. They'll never have a community version because they're the only ones in the market offering this kind of framework. 

I rate this solution nine out of 10.