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Spring Cloud Data Flow VS Spring Batch

Compare Spring Cloud Data Flow VS Spring Batch and see what are their differences

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Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Spring Cloud Data Flow logo Spring Cloud Data Flow

Spring Cloud Data Flow is a platform capable of stream and batch data pipelines having the tools to create delicate topologies.

Spring Batch logo Spring Batch

Level up your Java code and explore what Spring can do for you.
  • Spring Cloud Data Flow Landing page
    Landing page //
    2023-07-30
  • Spring Batch Landing page
    Landing page //
    2023-08-26

Spring Cloud Data Flow features and specs

No features have been listed yet.

Spring Batch features and specs

  • Robust Framework
    Spring Batch is a mature and robust framework that has been widely adopted in the industry for batch processing, offering a comprehensive set of features and a high level of reliability.
  • Integration with Spring
    Tightly integrated with the Spring ecosystem, making it easy to leverage other Spring modules and features, such as dependency injection, for batch applications.
  • Scalability
    Supports both parallel and distributed processing, allowing for scalable batch processing solutions that can handle large volumes of data efficiently.
  • Transaction Management
    Provides robust transaction management, ensuring data consistency and integrity during batch processing.
  • Comprehensive Error Handling
    Offers detailed error handling and retry mechanisms, which help in managing exceptions and ensuring that batch jobs can recover gracefully from failures.
  • Strong Community Support
    Backed by a strong community and excellent documentation, which can help developers overcome challenges and optimize their batch processing solutions.

Possible disadvantages of Spring Batch

  • Steep Learning Curve
    The framework's extensive features and configurations can result in a steep learning curve for new users, especially those unfamiliar with the Spring ecosystem.
  • Complex Configuration
    Configuring batch jobs can be complex and may require significant setup, particularly for users unfamiliar with XML or Spring configuration.
  • Verbose Code
    Spring Batch can lead to verbose code, as developers need to define many components and configurations, which can make maintenance more challenging.
  • Overhead for Small Jobs
    For simple batch tasks, using Spring Batch may introduce unnecessary complexity and overhead, as the framework is designed for more complex and large-scale batch processing.

Spring Cloud Data Flow videos

+ Add

Orchestrate All the Things! with Spring Cloud Data Flow - Eric Bottard & Ilayaperumal Gopinathan

More videos:

  • Review - Demo: Partitioning Batch jobs with Spring Cloud Data Flow & Task
  • Demo - 3 min demo: Spring Cloud Data Flow Metrics

Spring Batch videos

+ Add

Spring Batch Scheduling

More videos:

  • Review - ETE 2012 - Josh Long - Behind the Scenes of Spring Batch

Category Popularity

0-100% (relative to Spring Cloud Data Flow and Spring Batch)

Spring Cloud Data Flow

Spring Batch

Big Data
52 52%
Big Data
48% 48
Databases
0 0%
Databases
100% 100
Stream Processing
100 100%
Stream Processing
0% 0
Data Management
100 100%
Data Management
0% 0

User comments

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Social recommendations and mentions

Based on our record, Spring Batch should be more popular than Spring Cloud Data Flow. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Spring Cloud Data Flow mentions (1)

  • Dataflow, a self-hosted Observable Notebook Editor
    And a Cloudera project: https://www.cloudera.com/products/cdf.html And an Azure feature: https://docs.microsoft.com/en-us/azure/data-factory/control-flow-execute-data-flow-activity And a Spring feature: https://spring.io/projects/spring-cloud-dataflow. - Source: Hacker News / almost 4 years ago

Spring Batch mentions (2)

What are some alternatives?

When comparing Spring Cloud Data Flow and Spring Batch, you can also consider the following products

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

Apache Kylin - OLAP Engine for Big Data

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

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

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