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OpenSSL VS Apache Spark

Compare OpenSSL VS Apache Spark and see what are their differences

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  3. » Reviews
  4. » Alternatives

OpenSSL logo OpenSSL

OpenSSL is a free and open source software cryptography library that implements both the Secure Sockets Layer (SSL) and the Transport Layer Security (TLS) protocols, which are primarily used to provide secure communications between web browsers and …

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.
  • OpenSSL Landing page
    Landing page //
    2023-09-14
  • Apache Spark Landing page
    Landing page //
    2021-12-31

OpenSSL features and specs

  • Open Source
    OpenSSL is open-source software, which means it is freely available and can be reviewed, modified, and improved by anyone.
  • Widely Used
    OpenSSL is one of the most widely used libraries for SSL and TLS protocols, ensuring high compatibility and support across different platforms and applications.
  • Comprehensive Documentation
    OpenSSL provides extensive documentation and resources that can help users understand and implement its features effectively.
  • Regular Updates
    The OpenSSL project is actively maintained, receiving regular updates and patches to address security vulnerabilities and improve functionality.
  • Community Support
    A large community of developers and users contribute to forums, mailing lists, and other discussion platforms, providing support and sharing knowledge.
  • Flexible and Powerful
    OpenSSL offers a wide range of cryptographic functions and protocols, making it a versatile tool for various security requirements.

Possible disadvantages of OpenSSL

  • Complexity
    OpenSSL can be complex to configure and use, particularly for beginners or those without a deep understanding of cryptographic principles.
  • Security Vulnerabilities
    Despite regular updates, OpenSSL has had several high-profile security vulnerabilities in the past, such as Heartbleed, which can have broad implications.
  • Performance Overhead
    Depending on the implementation and configuration, using OpenSSL can introduce performance overhead, impacting the speed and efficiency of applications.
  • Limited User-Friendly Tools
    While OpenSSL is powerful, it lacks user-friendly tools and interfaces, making it harder for less technical users to operate.
  • Documentation Quality
    Though comprehensive, some users find the OpenSSL documentation to be dense and difficult to navigate, which can make troubleshooting and implementation challenging.

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.

OpenSSL videos

+ Add

Das Kommando "enc" in OpenSSL

More videos:

  • Review - OpenSSL and FIPS... They Are Back Together!
  • Review - OpenSSL After Heartbleed by Rich Salz & Tim Hudson, OpenSSL

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 OpenSSL and Apache Spark)

OpenSSL

Apache Spark

Development Tools
100 100%
Development Tools
0% 0
Databases
0 0%
Databases
100% 100
Javascript UI Libraries
100 100%
Javascript UI Libraries
0% 0
Big Data
0 0%
Big Data
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 OpenSSL and Apache Spark

OpenSSL Reviews

We have no reviews of OpenSSL yet.
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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 a lot more popular than OpenSSL. While we know about 70 links to Apache Spark, we've tracked only 2 mentions of OpenSSL. 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.

OpenSSL mentions (2)

  • Why does Baserow need my personal data so I can run open source?
    Baserow uses open source like https://en.wikipedia.org/wiki/OpenSSL and can use it without handing over data to openssl.org. Source: over 2 years ago
  • Creating private key help
    Noob here; I'm looking at openssl.org Two commands are listed; "openssl-genrsa" and "openssl genrsa" (No hyphen). Source: about 3 years ago

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 / 23 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 OpenSSL and Apache Spark, you can also consider the following products

jQuery - The Write Less, Do More, JavaScript Library.

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

React Native - A framework for building native apps with React

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

Babel - Babel is a compiler for writing next generation JavaScript.

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

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