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Apache Flink VS Spring Batch

Compare Apache Flink VS Spring Batch and see what are their differences

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Contents:
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  3. » Reviews
  4. » Alternatives

Apache Flink logo Apache Flink

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

Spring Batch logo Spring Batch

Level up your Java code and explore what Spring can do for you.
  • Apache Flink Landing page
    Landing page //
    2023-10-03
  • Spring Batch Landing page
    Landing page //
    2023-08-26

Apache Flink features and specs

  • Real-time Stream Processing
    Apache Flink is designed for real-time data streaming, offering low-latency processing capabilities that are essential for applications requiring immediate data insights.
  • Event Time Processing
    Flink supports event time processing, which allows it to handle out-of-order events effectively and provide accurate results based on the time events actually occurred rather than when they were processed.
  • State Management
    Flink provides robust state management features, making it easier to maintain and query state across distributed nodes, which is crucial for managing long-running applications.
  • Fault Tolerance
    The framework includes built-in mechanisms for fault tolerance, such as consistent checkpoints and savepoints, ensuring high reliability and data consistency even in the case of failures.
  • Scalability
    Apache Flink is highly scalable, capable of handling both batch and stream processing workloads across a distributed cluster, making it suitable for large-scale data processing tasks.
  • Rich Ecosystem
    Flink has a rich set of APIs and integrations with other big data tools, such as Apache Kafka, Apache Hadoop, and Apache Cassandra, enhancing its versatility and ease of integration into existing data pipelines.

Possible disadvantages of Apache Flink

  • Complexity
    Flink’s advanced features and capabilities come with a steep learning curve, making it more challenging to set up and use compared to simpler stream processing frameworks.
  • Resource Intensive
    The framework can be resource-intensive, requiring substantial memory and CPU resources for optimal performance, which might be a concern for smaller setups or cost-sensitive environments.
  • Community Support
    While growing, the community around Apache Flink is not as large or mature as some other big data frameworks like Apache Spark, potentially limiting the availability of community-contributed resources and support.
  • Ecosystem Maturity
    Despite its integrations, the Flink ecosystem is still maturing, and certain tools and plugins may not be as developed or stable as those available for more established frameworks.
  • Operational Overhead
    Running and maintaining a Flink cluster can involve significant operational overhead, including monitoring, scaling, and troubleshooting, which might require a dedicated team or additional expertise.

Spring Batch features and specs

No features have been listed yet.

Apache Flink videos

+ Add

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

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 Apache Flink and Spring Batch)

Apache Flink

Spring Batch

Big Data
86 86%
Big Data
14% 14
Databases
72 72%
Databases
28% 28
Stream Processing
100 100%
Stream Processing
0% 0
ETL
0 0%
ETL
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apache Flink seems to be a lot more popular than Spring Batch. While we know about 34 links to Apache Flink, we've tracked only 2 mentions of Spring Batch. 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.

Apache Flink mentions (34)

  • Major Technologies Worth Learning in 2025 for Data Professionals
    With the explosion of IoT devices and demand for instant insights, real-time analytics is no longer optional. Technologies like Apache Kafka, Apache Flink, and Redpanda are at the forefront of this movement. Learning these platforms will help you design systems that process streaming data efficiently. - Source: dev.to / about 1 month ago
  • Serverless Data Processing on AWS : AWS Project
    To do so, we will use Kinesis Data Analytics to run an Apache Flink application. To enhance our development experience, we will use Studio notebooks for Kinesis Data Analytics that are powered by Apache Zeppelin. - Source: dev.to / about 2 months ago
  • Data Engineering with Scala: Mastering Real-Time Data Processing with Apache Flink and Google Pub/Sub
    Apache Flink is a distributed data processing framework for both batch and streaming processing. It can be used to develop event-driven applications; perform batch and streaming data analysis; and can be used to develop ETL data pipelines. - Source: dev.to / 3 months ago
  • Streaming Data Alchemy: Apache Kafka Streams Meet Spring Boot
    Apache Flink: A more general-purpose stream processing framework known for its low latency and advanced windowing capabilities. https://flink.apache.org/. - Source: dev.to / 5 months ago
  • Show HN: Restate, low-latency durable workflows for JavaScript/Java, in Rust
    Restate is built as a sharded replicated state machine similar to how TiKV (https://tikv.org/), Kudu (https://kudu.apache.org/kudu.pdf) or CockroachDB (https://github.com/cockroachdb/cockroach) since it makes it possible to tune the system more easily for different deployment scenarios (on-prem, cloud, cost-effective blob storage). Moreover, it allows for some other cool things like seamlessly moving from one log... - Source: Hacker News / 7 months ago
View more

Spring Batch mentions (2)

What are some alternatives?

When comparing Apache Flink and Spring Batch, you can also consider the following products

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

Apache Kylin - OLAP Engine for Big Data

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

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.

Spark Mail - Spark helps you take your inbox under control. Instantly see what’s important and quickly clean up the rest. Spark for Teams allows you to create, discuss, and share email with your colleagues

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