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 It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists and their requirement for agility for workers' teams and support for data ops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.
What is Streaming Analytics? Streaming analytics, also known as event stream processing (ESP), refers to the analyzing and processing of large volumes of data through the use of continuous queries. Traditionally, data is moved in batches. While batch processing may be an efficient method for handling huge pools of data, it is not suitable for time-sensitive, “in-motion” data that could otherwise be streamed, since that data can expire by the time it is processed. By using streaming...
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 It's an outdated legacy product that doesn't meet the needs of modern data analysts and scientists and their requirement for agility for workers' teams and support for data ops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.