Cloudera DataFlow Reviews

Cloudera DataFlow is used as an ETL or ELT solution within Cloudera's data pipeline. Our organization heavily relies on it for data ingestion, transformation, and warehousing. It is also used daily for operational tasks, and it integrates well within Cloudera's ecosystem for high performance and throughput.

The most valuable features of Cloudera DataFlow include its native connectivity with a wide range of ecosystems within Cloudera such as Hive, Impala, and Spark. These connections ensure high performance and high throughput. Additionally, it allows for end-to-end scheduling of workflows without needing third-party applications.

Cloudera DataFlow's UI interface could be enhanced significantly. Memory handling can also be improved to be better than it is today.

I have been using Cloudera DataFlow for about five or six years.

Overall, I would rate the stability of Cloudera DataFlow as eight out of ten.

I would rate the scalability of Cloudera DataFlow as seven out of ten. It needs some enhancements to improve scalability.

Customer service and support are responsive and knowledgeable. I would rate them eight out of ten.

Positive

Previously, I used Informatica DataStage, IBM DataStage, Talend, and Ab Initio. We switched to Cloudera DataFlow because it is fully integrated with Cloudera and already available with Cloudera's license, thus avoiding the need for additional third-party licenses.

We have an operational team that handles maintenance of Cloudera DataFlow. This team consists of different engineers with similar skill sets in development and operation.

Cloudera is an expensive platform, and the licensing cost is high. I would rate its cost-effectiveness as nine out of ten. There are no extra expenses once the Cloudera license is acquired.

Before switching to Cloudera DataFlow, I evaluated Informatica, IBM DataStage, Talend, and Ab Initio.

Cloudera DataFlow is fully compatible with Cloudera's ecosystem and offers high efficiency through native connectors for various ecosystems. However, the learning curve is high, and there is a shortage of skilled professionals. My overall rating for Cloudera DataFlow is eight out of ten.

DataFlow's performance is okay.

DataFlow feels like using a closed environment - you have to keep data in it, and you can't work with virtual data outside of it.

I've been using DataFlow for seven to eight years.

DataFlow can't be scaled easily without strong technical skills, and even then, it has limitations.

Cloudera's technical support is good.

The initial setup was straightforward and took a week to complete.

DataFlow isn't expensive, but its value for money isn't great.

I don't find anything valuable in DataFlow. It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists and their requirement for agility for workers' teams and support for data ops processes versus software development/dev ops processes. If you're a traditional systems software development project needing a Cloudera-type capability, DataFlow is good. But it's no use if you want to empower a federated capability across your organization with data analytics and data-science themes. I would give DataFlow a rating of five out of ten.

We use Cloudera DataFlow for stream analytics.

The most effective features are data management and analytics.

The setup could be simpler.

I have been using Cloudera DataFlow for six years.

We used to build solutions processing up to fifty thousand events per second. It can be hundreds of gigabytes or terabytes, depending on the architecture.

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

We are truly happy with their technical support. We have first and second-level support delivered by the Cloudera site.

The tool is pretty complex to deploy and takes from two months to half a year to complete. You need someone with certification or experience in good teams for execution.

Since we are partnered and have a certified team, we have no difficulties with this product. Five people are needed to deploy the solution.

You have to pay a subscription, but deploying it on-site or on the cloud is possible. Cloud is a little expensive. Compared to other enterprise solutions, it is cheap.

The tool ensures data preparation and data delivery for ML products. We would recommend Cloudera DataFlow to customers with strong requirements for data security and governance and those dealing with large volumes of data or a high frequency of events per day. Additionally, it's suitable for customers with complex infrastructures that require interaction among numerous in-house services.

Overall, I rate the solution a nine out of ten.

Sometimes I need this workflow to make my modules, not for campaign preparation. It is solely focused on developing quality modules for direct telecommunication companies.

In Cloudera DataFlow, I can't say which is the most valuable feature because we use all modules. We need to compare each feature in our system for data analysis because we do not use modules like machine learning technology in our Cloudera DataFlow.

It is not easy to use the R language. Though I don't know if it's possible, I believe it is possible, but it is not the best language for machine learning. This feature could be improved.

I have experience with Cloudera DataFlow.

Sometimes, the language can be challenging, not specifically in relation to our application, because I have to use some parts of Java to do some tasks, though I would prefer to do it in R, which doesn't necessarily give a negative impression.

The initial setup was not so difficult.

The deployment took so long, at least one or two years, because the team has a project that aims to be exceptional in the future. It's good to say because the company is very good. It's a self-confirmation technical integration company. We have numerous reasons why reducing staff workload is beneficial. However, it is important to note that this does not directly apply to the application used. They will only do the service.

Overall, I rate the solution a seven out of ten.

We use this solution for sentimental analysis and fraud detection. We also use it to analyze product royalties.

It was effectively improving end to end data processing and manipulations.

This solution is very scalable and robust. It doesn't involve much coding — which is nice. 

Although its workflow is pretty neat, it still requires a lot of transformation coding; especially when it comes to Python and other demanding programming languages. It should be more like other solutions that don't require that much coding. 

I have been working with Cloudera DataFlow for more than six years.

This solution is both quite scalable and stable. 

The technical support is fair. It's not amazing, but it's good. Sometimes they take a while to respond. Often, I'll just look to Google for the solution rather than wait on the phone.

Positive

If you're interested in using this solution, first perform some return on investment analysis to make sure that this platform is mature enough for your requirements. 

Compare it with some other solutions first and determine which solution is best. It really comes down to your company's needs and what features you require. 

Overall, on a scale from one to ten, I would give Cloudera DataFlow a rating of eight.