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.

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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
"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.""Apache Spark Streaming has features like checkpointing and Streaming API that are useful.""The solution is better than average and some of the valuable features include efficiency and stability.""Apache Spark Streaming was straightforward in terms of maintenance. It was actively developed, and migrating from an older to a newer version was quite simple.""It's the fastest solution on the market with low latency data on data transformations.""As an open-source solution, using it is basically free.""Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.""The solution is very stable and reliable."

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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.""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 most valuable feature is real-time streaming."

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Cons
"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.""The initial setup is quite complex.""The solution itself could be easier to use.""In terms of improvement, the UI could be better.""There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused.""It was resource-intensive, even for small-scale applications.""We would like to have the ability to do arbitrary stateful functions in Python.""The cost and load-related optimizations are areas where the tool lacks and needs improvement."

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"Some of the features, like the monitoring tools, are not very mature and are still evolving.""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.""The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."

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Pricing and Cost Advice
  • "People pay for Apache Spark Streaming as a service."
  • "I was using the open-source community version, which was self-hosted."
  • "On a scale from one to ten, where one is expensive, or not cost-effective, and ten is cheap, I rate the price a seven."
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  • "This is an open-source product that can be used free of charge."
  • "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."
  • More Spring Cloud Data Flow Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:Apache Spark Streaming is versatile. You can use it for competitive intelligence, gathering data from competitors, or for internal tasks like monitoring workflows.
    Top Answer:In terms of improvement, the UI could be better. Additionally, Spark Streaming works well for various use cases, but improvements could be made for ultra-fast scenarios where seconds matter. While… more »
    Top Answer:As a data engineer, I use Apache Spark Streaming to process real-time data for web page analytics and integrate diverse data sources into centralized data warehouses.
    Top Answer: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… more »
    Top Answer:Spring Cloud Data Flow is used for asynchronous workloads. We are working on streams. For example, a workload is generated at a particular point, and at the source, it gets passed down through a… more »
    Top Answer:The solution requires little maintenance. My advice to others is for them to follow the documentation. The solution is very well-designed and they deliver on their promises. I rate Spring Cloud Data… more »
    Ranking
    8th
    out of 38 in Streaming Analytics
    Views
    4,523
    Comparisons
    3,689
    Reviews
    6
    Average Words per Review
    473
    Rating
    8.2
    10th
    out of 38 in Streaming Analytics
    Views
    3,998
    Comparisons
    3,018
    Reviews
    1
    Average Words per Review
    610
    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.

    Sample Customers
    UC Berkeley AMPLab, Amazon, Alibaba Taobao, Kenshoo, eBay Inc.
    Information Not Available
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm19%
    Computer Software Company19%
    Comms Service Provider7%
    Manufacturing Company6%
    VISITORS READING REVIEWS
    Financial Services Firm29%
    Computer Software Company16%
    Manufacturing Company7%
    Retailer7%
    Company Size
    REVIEWERS
    Small Business56%
    Midsize Enterprise11%
    Large Enterprise33%
    VISITORS READING REVIEWS
    Small Business21%
    Midsize Enterprise12%
    Large Enterprise67%
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise10%
    Large Enterprise77%
    Buyer's Guide
    Streaming Analytics
    March 2024
    Find out what your peers are saying about Databricks, Amazon, Confluent and others in Streaming Analytics. Updated: March 2024.
    763,955 professionals have used our research since 2012.

    Apache Spark Streaming is ranked 8th in Streaming Analytics with 6 reviews while Spring Cloud Data Flow is ranked 10th in Streaming Analytics with 1 review. Apache Spark Streaming is rated 8.0, while Spring Cloud Data Flow is rated 7.8. The top reviewer of Apache Spark Streaming writes "Easy deployment as a cluster and good documentation". 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, Azure Stream Analytics, Confluent, Apache Pulsar and Starburst Enterprise, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Azure Data Factory, Mule Anypoint Platform and TIBCO BusinessWorks.

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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.