The scalability is fairly good, but we don't have much experience with it. Our users are mainly technical, automated users who are like second-service users. We have hundreds of service users who are taking and pushing forward the data from the data lake. Then we have 20 to 30 data scientists and advanced analytics people. We have a few users interested in Google analytics logs. In total, we'd have a maximum of 50 users. 

We push them towards the direction of using BI tools and we try to make it clear that it's sometimes important to go for raw data. We are not always able to push it to the data warehouse or to a data mark for them, but it's always risky from a production process perspective because then the solution will be sensitive to  source system changes.

It is a scalable solution. Scalability-wise, I rate the solution a nine out of ten. Scalability depends on the environment, but it can scale up in an on-premises environment. There are challenges with its scalability on the cloud.

My company deals with around seven customers who use the product.

The scalability of Cloudera Distribution for Hadoop is excellent. It is not a straightforward process to scale, but it is easy to deploy a new server and connect it. Once connected, the data can be distributed effectively.

The solution is scalable. You can add many whenever you need.

We have 100s of users using this solution. We have plans to increase the usage.

We have a team of four data engineers and seven data scientists. We all work on the system, but machine learning models are delivered to other systems in our company. We have 10-15 people using this solution in total. There are five data discovery engineers using Jupyter portals.

We scaled up from six data nodes to 12 data nodes. It took considerable time because we did it during the COVID. We had to wait for eternity to get the servers. When servers came, it took maybe three weeks.

I rate the solution’s scalability a seven out of ten.

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

We have 2000 to 3000 users in our organization.

Cloudera is dependable, and it's completely scalable.

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.

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.

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

We have ten customers using the product. They include data engineers, performance engineers, and environment engineers.

I rate its stability a ten out of ten.

The scalability is very good. 

It is a scalable solution. There were about 20 users of this solution in my company. 10 people were required for the deployment and maintenance of the solution, including developers. 

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.

This solution is scalable enough for us. 

We have created a product, using HDFS, and when our engineers install it for themselves or for customers, we use this solution. There are about 15 to 20 people using it at any point of time. 

The tool is scalable. I rate the scalability an eight out of ten. It is easy to scale the product. Almost 20 to 25 people use the tool in our organization. We maintain the solution ourselves. We have nine engineers in our maintenance team.

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

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. 

This solution is scalable. We have 40 users for different projects in our organization.

We will continue to use this solution.

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.

It is very stable, didn't face any performance issue.

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.

It's very easy to deploy and scale as large as you want. Once created on the CM management cluster, is difficult to scale up as needed, as you add more clusters to the same CM instance.

None as such

This is a scalable solution. We have clients that have a large installation of Cloudera.

No issues encountered.

No issues encountered.

No issues with the current version.

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

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

No issues encountered.