Cloudera DataFlow Competitors and Alternatives

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Read reviews of Cloudera DataFlow competitors and alternatives

Sr. BigData Architect at ITC Infotech
MSP
Top 5
Very elastic, easy to scale, and a straightforward setup

What is our primary use case?

We work with clients in the insurance space mostly. Insurance companies need to process claims. Their claim systems run under Databricks, where we do multiple transformations of the data.

Pros and Cons

  • "It's easy to increase performance as required."
  • "Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."

What other advice do I have?

There isn't really a version, per se. It's a popular service. I'd recommend the solution. The solution is cloud-agnostic right now, so it really can go into any cloud. It's the users who will be leveraging installed environments that can have these services, no matter if they are using Azure or Ubiquiti, or other systems. I don't think you can find any other tool or any other service that is faster them Databricks. I don't see that right now. It's your best option. Overall, I'd rate the solution eight out of ten. The reason I'm not giving it full marks is that it's expensive compared to open…
AB
Partner / Head of Data & Analytics at a computer software company with 11-50 employees
Real User
Gives us low latency for fast, real-time data, with useful alerts for live data processing

What is our primary use case?

We use Apache Flink to monitor the network consumption for mobile data in fast, real-time data architectures in Mexico. The projects we get from clients are typically quite large, and there are around 100 users using Apache Flink currently. For maintenance and deployment, we split our team into two squads, with one squad that takes care of the data architecture and the other squad that handles the data analysis technology. Each squad is three members each.

Pros and Cons

  • "The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis."
  • "One way to improve Flink would be to enhance integration between different ecosystems. For example, there could be more integration with other big data vendors and platforms similar in scope to how Apache Flink works with Cloudera. Apache Flink is a part of the same ecosystem as Cloudera, and for batch processing it's actually very useful but for real-time processing there could be more development with regards to the big data capabilities amongst the various ecosystems out there."

What other advice do I have?

My advice to others when using Apache Flink is to hire good people to manage it. When you have the right team, it's very easy to operate and scale big data platforms. I would rate Apache Flink a nine out of ten.
Get our free report covering VMware, Databricks, Confluent, and other competitors of Cloudera DataFlow. Updated: April 2021.
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