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Scilab VS Apache Spark

Compare Scilab VS Apache Spark and see what are their differences

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Scilab logo Scilab

Scilab Official Website. Enter your search in the box aboveAbout ScilabScilab is free and open source software for numerical . Thanks for downloading Scilab!

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.
  • Scilab Landing page
    Landing page //
    2023-02-10
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Scilab features and specs

  • Open Source
    Scilab is free and open-source software, allowing users to access the source code and modify it to suit their needs without any cost.
  • Extensive Mathematical Functionality
    Scilab provides a wide range of mathematical functions and capabilities for numerical computation, making it suitable for a variety of scientific and engineering applications.
  • Toolboxes and Modules
    It offers various built-in toolboxes and modules for specialized tasks, such as signal processing, control systems, and optimization, expanding its functionality.
  • Cross-Platform Support
    Scilab runs on different operating systems, including Windows, macOS, and Linux, providing flexibility for users working in diverse environments.
  • Strong Community Support
    A large and active user community means that users can find plenty of support, tutorials, and third-party contributions, easing the learning curve.
  • Integration Capabilities
    Scilab can be easily integrated with other software and tools, such as Modelica for modeling and simulation, enhancing its versatility in different workflows.

Possible disadvantages of Scilab

  • Performance
    Scilab may not be as performance-optimized as some other numerical computation software, like MATLAB, especially for very large datasets or highly complex calculations.
  • Learning Curve
    While Scilab is powerful, it can be challenging for beginners to master due to its extensive functionality and the need to learn its scripting language.
  • Less Commercial Support
    As open-source software, Scilab does not offer the same level of commercial support or extensive professional resources that are available for some paid alternatives like MATLAB.
  • Documentation Quality
    Although Scilab has a lot of documentation, some users find that it lacks depth or clarity compared to other software, making it harder to find thorough explanations or examples.
  • Graphical User Interface
    The graphical user interface (GUI) of Scilab is not as polished or user-friendly as that of some competitor tools, which can impact user experience.
  • Compatibility Issues
    Interoperability with MATLAB can be limited, potentially causing issues when porting code or collaborating with MATLAB users.

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.

Scilab videos

+ Add

Scilab IPCV 1.2

More videos:

  • Review - Raspberry Pi for Computer Vision with Scilab
  • Review - Tone Recognition with Scilab and LabVIEW to Scilab Gateway

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 Scilab and Apache Spark)

Scilab

Apache Spark

Technical Computing
100 100%
Technical Computing
0% 0
Databases
0 0%
Databases
100% 100
Numerical Computation
100 100%
Numerical Computation
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

Share your experience with using Scilab 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 Scilab and Apache Spark

Scilab Reviews

25 Best Statistical Analysis Software
Scilab is a powerful, free, and open-source software widely used by researchers, students, and professionals in various fields such as engineering, mathematics, physics, and more.
Source: www.scijournal.org
7 Best MATLAB alternatives for Linux
The syntax of Scilab is similar to MATLAB it also provides a source code translator to convert MATLAB code to Scilab.
Source: cyanogenmods.org
Matlab Alternatives
Scilab is an open-source similar to the implementation of Matlab. The approximation techniques known as Scientific Computing is used to solve numerical problems. To achieve this, the team of Scilab developers made use of Solvers and algorithms to build the algebraic libraries. Scilab is one of the major alternatives to Matlab along with GNU Octave.
Source: www.educba.com
10 Best MATLAB Alternatives [For Beginners and Professionals]
Scilab has 1700 mathematical functions for engineering applications and data analysis. You can also use Scilab to solve various constrained and unconstrained problems such as shape and topology optimizations etc.
Source: thetechtop10.com
4 open source alternatives to MATLAB
Scilab is another open source option for numerical computing that runs across all the major platforms: Windows, Mac, and Linux included. Scilab is perhaps the best known alternative outside of Octave, and (like Octave) it is very similar to MATLAB in its implementation, although exact compatibility is not a goal of the project's developers.
Source: opensource.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 – 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 seems to be more popular. 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.

Scilab mentions (0)

We have not tracked any mentions of Scilab yet. Tracking of Scilab recommendations started around Mar 2021.

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 / 21 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 / 22 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
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What are some alternatives?

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

MATLAB - A high-level language and interactive environment for numerical computation, visualization, and programming

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

Wolfram Mathematica - Mathematica has characterized the cutting edge in specialized processing—and gave the chief calculation environment to a large number of pioneers, instructors, understudies, and others around the globe.

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

GNU Octave - GNU Octave is a programming language for scientific computing.

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

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