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Spring Framework VS Apache Spark

Compare Spring Framework VS Apache Spark and see what are their differences

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

Spring Framework logo 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 Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Spring Framework Landing page
    Landing page //
    2023-08-18
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Spring Framework features and specs

  • Comprehensive Ecosystem
    Spring Framework provides a vast array of tools and modules which address various aspects of application development such as security, data access, and messaging. This helps in building robust enterprise applications.
  • Inversion of Control (IoC) Container
    Spring's IoC container promotes loose coupling by managing object lifecycles and dependencies, making the code more modular and testable.
  • Aspect-Oriented Programming (AOP)
    Spring's AOP module allows for separating cross-cutting concerns like logging, transaction management, and security, making the code cleaner and more maintainable.
  • Spring Boot
    Spring Boot streamlines the setup and development of new Spring applications with built-in configurations and convention over configuration, reducing boilerplate code and speeding up development time.
  • Large Community and Support
    Spring has a large and active community, extensive documentation, and a wide selection of online resources which make it easier to find support and solutions to common problems.
  • Integration Capabilities
    Spring Framework offers seamless integration with various other technologies and frameworks, including Hibernate for ORM, Apache Kafka for messaging, and more.

Possible disadvantages of Spring Framework

  • Complexity
    Spring Framework can be complex and have a steep learning curve, especially for newcomers who are not familiar with its extensive set of features and configurations.
  • Configuration Overhead
    Although Spring Boot reduces the configuration burden, traditional Spring applications may still require extensive XML or annotation-based configurations, which can be cumbersome.
  • Performance Overhead
    The flexibility and the modular nature of Spring can introduce some performance overhead compared to more lightweight solutions, which could be a concern in highly performance-sensitive applications.
  • Version Incompatibility
    Upgrading between different versions of the Spring Framework and its associated projects can sometimes lead to compatibility issues and necessitate significant code changes.
  • Dependency Management
    Managing dependencies in a large Spring application can become complicated, particularly when dealing with multiple modules and third-party libraries, potentially leading to dependency conflicts.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Analysis of Spring Framework

Overall verdict

  • Yes, Spring Framework is generally considered a good framework with robust features, strong community support, and extensive documentation, making it a reliable choice for Java developers working on complex, enterprise-level applications.

Why this product is good

  • Features
    Spring provides a wide range of features including dependency injection, aspect-oriented programming, transaction management, and integration with various tools and technologies.
  • Community
    Spring has a large and active community, which contributes to a wealth of resources, documentation, and third-party extensions.
  • Popularity
    Spring Framework is one of the most popular frameworks for Java development, widely used in building enterprise applications.
  • Versatility
    It offers a comprehensive programming and configuration model for modern Java-based enterprise applications, on any kind of deployment platform.

Recommended for

  • Developers building large-scale, enterprise-grade applications.
  • Teams looking for a mature and well-supported framework with a large ecosystem.
  • Projects that require integration with popular Java technologies and tools.
  • Developers focusing on microservices architecture, as Spring Boot simplifies the development of microservices.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Spring Framework videos

+ Add

What is the Spring framework really all about?

More videos:

  • Tutorial - Spring Framework Tutorial | Full Course

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Spring Framework and Apache Spark)

Spring Framework

Apache Spark

Web Frameworks
100 100%
Web Frameworks
0% 0
Databases
0 0%
Databases
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using Spring Framework and Apache Spark. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare Spring Framework and Apache Spark

Spring Framework Reviews

Top 9 best Frameworks for web development
Spring offers a wide range of frameworks, such as an MVC framework, a data access framework and a transaction management framework. With its focus on scalability and security, Spring is an excellent choice.
Source: www.kiwop.com
17 Popular Java Frameworks for 2023: Pros, cons, and more
Therefore, the configuration, setup, build, and deployment processes all require multiple steps you might not want to deal with, especially if youโ€™re working on a smaller project. Spring Boot (a micro framework that runs on top of the Spring Framework) is a solution for this problem, as it allows you to set up your Spring application faster, with much less configuration.
Source: raygun.com
Top 10 Phoenix Framework Alternatives
Spring Framework is an open-source app framework and inversion of control container for the Java platform, providing the infrastructure required to develop Java and web apps on top of the Java EE platform.
Source: blog.back4app.com
10 Best Java Frameworks You Should Know
Spring Framework is one of the most extensively used, top-notch, lightweight software application frameworks built for software design, development, and deployment in Java.
Source: www.simplilearn.com

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing โ€“ batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Spring Framework. It has been mentiond 80 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 Framework mentions (13)

  • March 2025 Java Key Updates in Boot, Security, and More
    The release of Spring Framework 6.2.5 includes:. - Source: dev.to / over 1 year ago
  • Getting Started with Spring Boot 3 for .NET Developers
    Spring Framework 6: https://spring.io/projects/spring-framework. - Source: dev.to / over 1 year ago
  • Want to Get Better at Java? Go Old School.
    We had to write our own frameworks (uphill, both ways) but most current frameworks will have similar documentation pages as well. Both Apache and Spring are especially good at that. - Source: dev.to / over 3 years ago
  • Best Frameworks For Web Development
    Framework link: https://spring.io/projects/spring-framework Github Link: https://github.com/spring-projects/spring-framework. - Source: dev.to / almost 4 years ago
  • What to you do now?
    A common used Java framework is Spring framework (ie https://spring.io/projects/spring-framework and short tutorials at https://www.baeldung.com/spring-intro). Source: about 4 years ago
View more

Apache Spark mentions (80)

  • MLOps Lifecycle: Stages, Workflow, and Best Practices
    Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / 2 months ago
  • 7 Free Tools for Data Pipeline Reconciliation and Cross-Source Validation
    Apache Spark provides distributed in-memory data processing and is the appropriate tool when the data set to be reconciled does not fit in a single machine's memory, or when parallelizing the comparison across a cluster would reduce runtime from hours to minutes. - Source: dev.to / 3 months ago
  • Why Apache IoTDB Is Written in Java: A Decade of Engineering Trade-offs
    When IoTDB was initiated in 2011, almost all influential distributed systems and databases were built in Java or on the JVMโ€”such as Hadoop, HBase, Spark (Scala on JVM), Cassandra, Kafka, and Flink. To integrate deeply with the big data ecosystem, choosing Java was a natural decision. - Source: dev.to / 5 months ago
  • I Scraped 47M+ Hacker News Items Into Parquet Files โ€“ Here's What I Discovered About HN's Hidden Data Patterns
    For handling even larger datasets or building production applications, Apache Spark provides excellent Parquet support with distributed processing capabilities. - Source: dev.to / 5 months ago
  • Show HN: Spark โ€“ Zero-config IoT deployment tool written in Rust
    You may want to consider renaming this project. The name "Spark" already refers to: A popular data analytics framework of the Apache Foundation: https://spark.apache.org/ A subset of the Ada programming language used for formal verification: https://learn.adacore.com/courses/intro-to-spark/chapters/01_Overview.html An Nvidia AI development system: https://www.nvidia.com/en-us/products/workstations/dgx-spark/. - Source: Hacker News / 7 months ago
View more

What are some alternatives?

When comparing Spring Framework and Apache Spark, you can also consider the following products

Grails - An Open Source, full stack, web application framework for the JVM

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

Apache Struts - Apache Struts is an open-source web application framework for developing Java EE web applications.

Hadoop - Open-source software for reliable, scalable, distributed computing

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

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

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