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Apache Spark VS Apache Jena

Compare Apache Spark VS Apache Jena 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.

Apache Jena logo Apache Jena

Java Web Frameworks
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Apache Jena Landing page
    Landing page //
    2023-05-15

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.

Apache Jena features and specs

  • Rich Semantics Support
    Apache Jena provides comprehensive support for RDF, RDFS, and OWL, which allows for complex semantics and reasoning capabilities, making it ideal for applications requiring rich semantic processing.
  • Flexible and Scalable
    Jena is designed to be modular and offers flexibility in its architecture, allowing developers to use only the components they need. It also scales well for both small and large datasets.
  • Strong Query Capabilities
    With SPARQL support, Jena offers robust query capabilities for extracting and manipulating data stored in RDF format, allowing for precise data management and retrieval.
  • Integration and Extensibility
    Jena integrates well with other tools and technologies, such as Fuseki, for deploying SPARQL endpoints, and its plugin architecture allows for easy extension of its capabilities.
  • Active Community and Documentation
    Supported by a strong open-source community, Jena benefits from extensive documentation, online resources, and community support, helping ease the learning curve and troubleshooting process.

Possible disadvantages of Apache Jena

  • Steep Learning Curve
    The complexity of semantic web technologies and Jena's rich feature set can present a steep learning curve for beginners, requiring a significant investment in time to become proficient.
  • Performance Overhead
    Operating with RDF and especially OWL can result in performance overheads in terms of processing time and memory usage, particularly when reasoning over large datasets.
  • Java Dependency
    As a Java-based framework, Jena inherits any limitations associated with Java, such as memory management and the need to be run in Java environments, which might not be suitable for all projects.
  • Less Suitability for Non-Semantic Use Cases
    Jena is specifically geared towards semantic web applications and may not offer significant advantages for projects that do not require semantic web technologies.
  • Complex Deployment
    Setting up and configuring Jena components, especially in complex environments, can be challenging and might require a fair amount of expertise to ensure optimal deployment and operation.

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

Apache Jena videos

+ Add

Apache Jena

More videos:

  • Review - apache jena installation
  • Review - Семантический веб. Protégé и Apache Jena

Category Popularity

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Apache Spark

Apache Jena

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

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

Apache Jena Reviews

We have no reviews of Apache Jena yet.
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Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Apache Jena. While we know about 70 links to Apache Spark, we've tracked only 5 mentions of Apache Jena. 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 / 22 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 / 24 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

Apache Jena mentions (5)

  • Does any useful knowledge graph tool that you recommend？
    Another good one I just started working with is AnzoGraph. Also, a product but (at least according to a colleague, I'm just starting to use it myself) you can also do quite a bit of serious work with the community version. Also, GraphDB from OntoText and TBD from Apache Jena as well. Source: over 2 years ago
  • Generating websites with SPARQL and Snowman, part 1
    Completely agree. I'm hoping to one day see Jena [0] compiled to a native image [1]. Having a persistent triple store with transactions, and an inference api in owl/rdfs/shacl with a prolog-like "logic programming engine", running in process like SQLite, would be awesome. [0] https://jena.apache.org/ [1] https://www.graalvm.org/22.0/reference-manual/native-image/. - Source: Hacker News / almost 3 years ago
  • Deployement in semantic web
    The first thing you need to decide is how to link your ontology with a programming language. Speaking very broadly there are 2 approaches: 1) Use a library like Apache Jena (for Java) or OWLReady2 (for Python). What these libraries do is enable you to take your model and create objects in your Java or Python program to manipulate it (query it, create instances of classes, set property values, etc.). Source: over 3 years ago
  • Choice between cybersecurity and Semantic Web course
    The semantic web is more than just front end. Apache jena is an example of a semantic web library. Source: over 3 years ago
  • Ask HN: What under-the-radar technology are you super excited about?
    I worked in a semweb company ~10 years ago - https://jena.apache.org/ as a general starting point is a useful library. I remember distinctly OWLIM https://www.w3.org/2001/sw/wiki/Owlim as a great triple store. - Source: Hacker News / about 4 years ago

What are some alternatives?

When comparing Apache Spark and Apache Jena, 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.

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

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

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

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

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

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