Apache Spark Streaming Competitors and Alternatives

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Read reviews of Apache Spark Streaming competitors and alternatives

Mohammad Masudu Rahaman
Founder at Talkingdeal.com LLC
Real User
Top 20
Oct 22, 2020
Good logging mechanisms, a strong infrastructure and pretty scalable

What is our primary use case?

Mostly the use cases are related to building a data pipeline. There are multiple microservices that are working in the Spring Cloud Data Flow infrastructure, and we are building a data pipeline, mostly a step-by-step process processing data using Kafka. Most of the processor sync and sources are being developed based on the customers' business requirements or use cases. In the example of the bank we work with, we are actually building a document analysis pipeline. There are some defined sources where we get the documents. Later on, we extract some information united from the summary and we… more »

Pros and Cons

  • "There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."
  • "The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

What other advice do I have?

While the deployment is on-premises, the data center is not on-premises. It's in a different geographical location, however, it was the client's own data center. We deployed there, and we installed the CDF server, then the Skipper server, and everything else including all the microservices. We used the PCF Cloud Foundry platform and for the bank, we deployed in Kubernetes. Spring Cloud Data Flow server is pretty standard to implement. The year before it was a new project, however, now it is already implemented in many, many projects. I think developers should start using it if they are not…
RameshCh
Sr. BigData Architect at ITC Infotech
MSP
Top 20
Jul 1, 2020
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…
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