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.
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.
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 It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists and their requirement for agility for workers' teams and support for data ops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.
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.
Streaming Analytics processes and analyzes data continuously as it is generated, enabling businesses to derive insights in real-time. The solution empowers organizations to make immediate data-driven decisions and respond swiftly to dynamic market conditions. This technology handles vast amounts of data in motion and offers a platform for continuous computation of results using that data. Designed for time-sensitive business operations, it supports use cases such as fraud detection,...
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.
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.
Overall, I rate the solution a seven out of ten.
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.
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.