Cloudera Distribution for Hadoop Reviews

We share company data leaks based on cloud data on their clusters.

We had a data warehouse before all the data. We can process a lot more data structures.

The solution has data managers. You can manage all services from one place in an integrated manner. You don't have to manage the other services separately by Spark, etc.

We switched to Airflow because Cloudera is outdated. It's not widely used. It would be good if we had the Spark 3.5. Spark is quite old. Cloudera is now offering an alternate solution as a replacement for AWS. AWS works badly with small files.

The solution is not fit for on-premise distributions. It should be containerized so we can deploy it as containers within Kubernetes. We had one upgrade from CDH to CDP, which lasted for a long time. And I would expect with containerized deployment, it would be upgraded much more quickly than we had the experience.

I have been using Cloudera Distribution for Hadoop for two years. We are using the V7.1.7 of the solution.

The product is stable.

I rate the solution’s stability an eight out of ten.

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.

We had back and forth with Cloudera support. We had to schedule the meetings and workshops, which took over a month. We had several sessions of writing. They called an engineer to fix the issues.

Neutral

We had a vendor. They have some issues with the installation, especially with the upgrade. When we upgraded from CDH to CDP on-premise, they had problems with the user interface and user authorization.

Deployment depends on the projects. It takes maybe a month for migration to CDP.

There was a local third-party vendor involved in the installation process.

The solution's license price increased five times because of CDH. We set the licensing levels like data engineering, an enterprise data hub, data science, and data engineering, and then when they moved to CDP, none of this was possible anymore. It's way more expensive now.

I rate the product's pricing a two out of ten, where one is cheap, and ten is expensive.

Overall, I rate the solution 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.

There are multiple use cases of Cloudera. It is a big data platform where we collect all the data and connect other sources to get data from multiple sources. Cloudera has a Data Lake.

Data Lake is mature. Their integration across the Hadoop ecosystem and the interoperability across other distributions is mature. The technical skill set for Cloudera is available. They enhance their roadmap quarterly and provide new features to enhance current functionalities and capabilities. They are capitalizing on their product and have a clear roadmap.

Pricing could be improved.

I have been using Cloudera Distribution for Hadoop for six years.

The product is stable.

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.

Customer support is supportive and proactive. They engage you for patching and upgrades.

I have used HPE Ezmeral Data Fabric. The difference is compatibility. HPE lacks compatibility with Informatica, while Cloudera is compatible.

The initial setup is easy.

The value is huge. We have about 30 use cases.

The product comes with an annual subscription, which is expensive. They are bundling technologies together. You have to pay an extra cost if you need the technology out of the base license.

Day-to-day maintenance is simple. We have two technical staff to take care of the solution.

Overall, I rate the solution a nine 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.

It is a good enterprise platform. It is easier and more stable. Additionally, it has the best proxy, security, and support features compared to open-source products.

The areas of improvement depend on the scale of the project. For banking customers, security features and an essential budget for commercial licenses would be the top priority. Data regulation could be the most crucial for a project with extensive data or an extra use case.

We have been using Cloudera Distribution for Hadoop for a few years.

I rate the product’s stability a ten out of ten.

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 product has a support subscription for one year. We use technical support only for complex use cases. We work with their team as we have direct and quick access to contact them. It helps us better understand the technical and business-related queries of the customers.

The on-cloud version is easy to set up. Although, it is complicated to process a large amount of data for on-premises or hybrid setup. It is not a ready-to-use solution for telecom or finance technology. It requires the deployment of robust technology relying on network infrastructure.

The product’s price depends from project to project. It is more expensive than open-source solutions and could be cheaper. However, in some cases, it is less costly than open-source.

It is the best solution in the world at the moment. I advise others to go for it if you have an enterprise customer. I rate it a ten out of ten.