Apache Spark Streaming vs Spring Cloud Data Flow comparison

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Executive Summary

We performed a comparison between Apache Spark Streaming and Spring Cloud Data Flow based on real PeerSpot user reviews.

Find out what your peers are saying about Databricks, Microsoft, Confluent and others in Streaming Analytics.
To learn more, read our detailed Streaming Analytics Report (Updated: November 2022).
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Featured Review
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The solution is better than average and some of the valuable features include efficiency and stability.""Apache Spark Streaming's most valuable feature is near real-time analytics. The developers can build APIs easily for a code-steaming pipeline. The solutions have an ecosystem of integration with other stock services.""The solution is very stable and reliable.""As an open-source solution, using it is basically free."

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"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."

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Cons
"We would like to have the ability to do arbitrary stateful functions in Python.""There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused.""The solution itself could be easier to use.""The service structure of Apache Spark Streaming can improve. There are a lot of issues with memory management and latency. There is no real-time analytics. We recommend it for the use cases where there is a five-second latency, but not for a millisecond, an IOT-based, or the detection anomaly-based. Flink as a service is much better."

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"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."

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Pricing and Cost Advice
  • "People pay for Apache Spark Streaming as a service."
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  • "If you want support from Spring Cloud Data Flow there is a fee. The Spring Framework is open-source and this is a free solution."
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    Questions from the Community
    Top Answer:The solution is better than average and some of the valuable features include efficiency and stability.
    Top Answer:There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused. For example, it is still not plug and play and use as… more »
    Top Answer:The primary use of the solution is to implement predictive maintenance qualities.
    Top Answer:The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation. The documentation on offer is not that… more »
    Top Answer: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… more »
    Top Answer: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… more »
    Ranking
    10th
    out of 38 in Streaming Analytics
    Views
    4,619
    Comparisons
    4,127
    Reviews
    4
    Average Words per Review
    357
    Rating
    7.8
    7th
    out of 38 in Streaming Analytics
    Views
    8,940
    Comparisons
    6,787
    Reviews
    1
    Average Words per Review
    598
    Rating
    7.0
    Comparisons
    Also Known As
    Spark Streaming
    Learn More
    Overview

    Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.

    Spring Cloud Data Flow is a toolkit for building data integration and real-time data processing pipelines.
    Pipelines consist of Spring Boot apps, built using the Spring Cloud Stream or Spring Cloud Task microservice frameworks. This makes Spring Cloud Data Flow suitable for a range of data processing use cases, from import/export to event streaming and predictive analytics. Use Spring Cloud Data Flow to connect your Enterprise to the Internet of Anything—mobile devices, sensors, wearables, automobiles, and more.

    Offer
    Learn more about Apache Spark Streaming
    Learn more about Spring Cloud Data Flow
    Sample Customers
    UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
    Information Not Available
    Top Industries
    VISITORS READING REVIEWS
    Computer Software Company20%
    Financial Services Firm15%
    Comms Service Provider12%
    Retailer6%
    VISITORS READING REVIEWS
    Financial Services Firm22%
    Computer Software Company20%
    Comms Service Provider7%
    Retailer7%
    Company Size
    VISITORS READING REVIEWS
    Small Business17%
    Midsize Enterprise13%
    Large Enterprise70%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise10%
    Large Enterprise74%
    Buyer's Guide
    Streaming Analytics
    November 2022
    Find out what your peers are saying about Databricks, Microsoft, Confluent and others in Streaming Analytics. Updated: November 2022.
    657,397 professionals have used our research since 2012.

    Apache Spark Streaming is ranked 10th in Streaming Analytics with 4 reviews while Spring Cloud Data Flow is ranked 7th in Streaming Analytics with 1 review. Apache Spark Streaming is rated 7.8, while Spring Cloud Data Flow is rated 7.0. The top reviewer of Apache Spark Streaming writes "Mature and stable with good scalability". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Simple programming model, low maintenance, but interface could improve". Apache Spark Streaming is most compared with Amazon Kinesis, Apache Flink, Azure Stream Analytics, Confluent and Databricks, whereas Spring Cloud Data Flow is most compared with Apache Flink, Amazon Kinesis, Databricks, Mule Anypoint Platform and Cloudera DataFlow.

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    We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.