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Apache Spark VS Micronaut Framework

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

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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.

Micronaut Framework logo Micronaut Framework

Build modular easily testable microservice & serverless apps
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Micronaut Framework Landing page
    Landing page //
    2022-02-01

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.

Micronaut Framework features and specs

  • High Performance
    Micronaut is designed for low memory consumption and fast startup time, which makes it ideal for serverless and microservices architectures.
  • Compile-Time Dependency Injection
    Micronaut uses compile-time dependency injection, which eliminates reflection. This leads to faster execution, smaller binaries, and lower memory usage.
  • Kotlin Support
    Micronaut provides excellent support for Kotlin, taking advantage of Kotlin's features to make application development more concise and expressive.
  • Cloud Native
    Built with cloud-native applications in mind, Micronaut has integrations with cloud services and support for distributed configuration and service discovery.
  • Reactive Programming
    Micronaut supports reactive programming, making it easier to build scalable applications that can handle many concurrent users efficiently.
  • Easy Testing
    Micronaut provides extensive support for testing, including a built-in HTTP client that simplifies the testing of microservice interactions.

Possible disadvantages of Micronaut Framework

  • Learning Curve
    Developers familiar with traditional frameworks like Spring might experience a learning curve transitioning to Micronaut, particularly due to its annotation-driven programming model.
  • Ecosystem Maturity
    Compared to more established frameworks, Micronaut's ecosystem is still growing, which may result in fewer third-party integrations and community resources.
  • Newer Technology
    Being a relatively new framework, it might not have the depth of proven enterprise deployments that older, more established frameworks have.
  • Limited Use Cases
    While Micronaut excels in microservices and serverless environments, it may not be the best choice for applications that require traditional monolithic architectures.

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

Micronaut Framework videos

+ Add

Micronaut Framework | Build Microservices with This JVM-Based Framework | Java Techie

Category Popularity

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Apache Spark

Micronaut Framework

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

User comments

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Reviews

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

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 – 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

Micronaut Framework Reviews

We have no reviews of Micronaut Framework yet.
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Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Micronaut Framework. It has been mentiond 70 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.

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 28 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 30 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

Micronaut Framework mentions (41)

  • Top Backend Technologies for Scalable Web Development
    Micronaut for Microservices Micronaut is a modern Java framework built for microservices. It starts quickly, uses minimal memory, and is highly testable, making it perfect for cloud-native applications. - Source: dev.to / 2 months ago
  • Year After Switching from Java to Go: Our Experiences
    But Javas has so many of these web frameworks?! * Spring (https://spring.io/) * Spring Boot (https://spring.io/projects/spring-boot) * Helidon (https://helidon.io/) * Micronaut (https://micronaut.io/) * Quarkus (https://quarkus.io/) * JHipster (https://www.jhipster.tech/) * Vaadin (https://vaadin.com/) That's just to mention the bigger ones, there's lots of mini frameworks like Javalin (https://javalin.io/) and... - Source: Hacker News / 3 months ago
  • JPA entity relationship with Micronaut data JDBC
    Micronaut is a JVM-based framework for building lightweight, modular applications. Micronaut is the latest framework designed to make creating microservices quick and easy. - Source: dev.to / 5 months ago
  • Choosing the Right Java Microservices Framework: Spring Boot, Quarkus, Micronaut, and Beyond
    Micronaut is designed for building modular microservices with a focus on reactive programming and low resource consumption. - Source: dev.to / 6 months ago
  • My Journey with AWS CDK and Java: What You Need to Know
    The CDK also seems to become more widely adopted in the Java community with more recent Java frameworks like Micronaut even having built-in support for AWS CDK in the framework. - Source: dev.to / 9 months ago
View more

What are some alternatives?

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

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

vert.x - From Wikipedia, the free encyclopedia

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

helidon - Helidon Project, Java libraries crafted for Microservices

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

Javalin - Simple REST APIs for Java and Kotlin

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