Cloudera Distribution for Hadoop Reviews

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.

I primarily use CDH for data storage and regular dashboard reports.

The most valuable feature is that I can use CDH for almost all use cases across all industries, including the financial sector, public sector, private retailers, and so on.

Cloudera's prices are too high and are not competitive with other solutions. They could also improve the Data Science Workbench and add some more features, like wizard activities.

CDH is stable.

CDH is scalable, but it's expensive to do it.

Cloudera's support is extremely bad and cannot be relied on.

I wouldn't recommend CDH to others because of its high cost.

I would rate CDH as eight out of ten.

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. 

Faster runtime for batch jobs.

Improved Business Intelligence reporting from daily to every two hours satisfying the business stakeholders who would favour transactional systems to draft reports because it had the latest data. 

The issue that arises using transactional systems with multiple version of truths across the enterprise. With faster turn-around time business stakeholders are now adopting the BI systems designed to give a cohesive view of the performance metrics important to them.

Full Support for all Spark SQL features, support for SparkR, compatibility with Hive for DataFrame saved tables.

Cloudera CDH5.5.x does not support SparkR. SparkR, the integration of R models in API would be a great addition since this will enable fast near real-time analytical integration of R models with data feed.

The functionality in SparkSQL to save a DataFrame as a table in HIVE produces a table not compatible with HIVE. There is a workaround for this in creating the HIVE table first and then doing inserts.

Cloudera CDH5.5.x is a great product, but the adoption of additional features not currently supported will make the product even better but by no means subtract from its desirability.

Do thorough research and ensure your use-cases or scale does not conflict with the system requirements and that those features that would make a difference are supported.

Mostly HUE, Impala, Sqoop, and Hive. The impala-shell command is number one.

We are working on research for genomic data looking for specific genes and variances. Even Hive was not good enough to process it correctly, only with Impala are we getting results quicker.

Sometimes the heavy queries do not finish at all. It would be good to see the progress of heavy script in the impala shell or get some way to access it.

We started to use Cloudera about one-and-a-half years ago.

We are having some issues with stability and are speaking to Cloudera support.

It's acceptable.

It's acceptable.

We were trying AWS Impala as well, but Cloudera won as it had more functionality with HUE, Sqoop, and Solr as built-in functions.

We have struggled a bit in installing and configuring Cloudera Manager on the AWS cluster. For now, it is good.

We did the implementation only using our team and resources. It was a hard start, but an easy landing.

Cloudera is good for mid to big company, but small ones can use AWS Impala/HUE. Go to training, or you are going to spend many hours to find short answers. The Cloudera solution is big with good documentation, but you need to know what and where to read first.

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. 

The Cloudera Manager administrator webpage simplifies the administration tasks and helps to maintain a global overview of the cluster performance.

We are moving from an standard SQL environment (Oracle DataWarehouse) to a Big Data environment, and the Hadoop cluster will be the key of our new organization. It will allow to scale in an easy namer.

We found some difficulties when importing Hive tables from another Cluster.

I want to point the fact that we encounter many problems related to the cloud storage and how resources are managed. Our learning has been that, although it is quite simple to deploy single machines on the cloud, deploying clusters of machines is much more complex as many factors need to be considered: individual machines, connectivity across machines, storage.

I've used it for three months.

We found some issues but were related with the hardware provider. For the moment I have not detected any problem from the Cloudera software point of view.

Technical support is really efficient.

We chose this product as it is considered a market standard and due to its wide documentation on the web. I evaluated other options but the fact that now it is becoming an standard for many companies helped me to choose this option.

In the cloud environment where we deployed (Azure Resource Manager) there was a ready-to-deploy template which simplified a lot the initial set-up.

We implemented with an in-house team. Our initial idea was to stop the cluster during the weekends and when there was no usage. However, we found strong difficulties and we were not able to start programmatically the whole cluster, so finally we left the cluster working all the time.

This issues were mainly related with the cloud provider and how this provider manages the resources for the cluster machines.

From our point of view it is a long-time investment. We hope to get the ROI in the following years.

I am very comfortable with this product. The combination of Cloudera Manager administrator server, which allows the management of the Hadoop Cluster, and the Hue server, which simplifies the use make this product a current standard on the market. Perhaps it lacks a full integration of all its components.

The features we've found most valuable are--

• Fast processing of data
• Easy to manipulate using HiveQL

We were able to utilize data which was untapped previously. We've got great use cases now to drive business revenue.

It needs more standardized documentation on Hive.

I've used it for two and a half years.

It's great.

The level of technical support is great.

No previous solution was used, and senior management chose to bring it in.

I was not directly involved in deployment.

It was done by the vendor team, who were great.

It's good for Big Data analytics.