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Hyperledger VS Apache Spark

Compare Hyperledger VS Apache Spark and see what are their differences

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Hyperledger logo Hyperledger

Hyperledger is a multi-project open source collaborative effort hosted by The Linux Foundation, created to advance cross-industry blockchain technologies.

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.
  • Hyperledger Landing page
    Landing page //
    2023-09-26
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Hyperledger features and specs

  • Permissioned Network
    Hyperledger operates on a permissioned blockchain, meaning that participants must be known and authorized. This enhances security and trust among members of the network.
  • Modular Architecture
    Its modular architecture allows users to plug and play different components like consensus algorithms, membership services, and data storage options, offering great flexibility and customization.
  • High Scalability
    Hyperledger is designed to scale with the needs of different businesses, making it suitable for large enterprise-level applications.
  • Strong Governance
    Backed by the Linux Foundation, Hyperledger benefits from strong governance and contributions from industry leaders, ensuring better code quality and ongoing development.
  • Interoperability
    Hyperledger prioritizes interoperability between different blockchain networks, allowing for seamless integration and communication across different platforms.

Possible disadvantages of Hyperledger

  • Complex Setup
    Setting up and managing a Hyperledger network can be complex and may require significant expertise, making it less accessible for small businesses or individual developers.
  • Limited Adoption
    Compared to public blockchains like Ethereum and Bitcoin, Hyperledger has less widespread adoption, which could limit its network effects and community support.
  • Performance Overhead
    The additional layers of security and permissioned access can introduce performance overhead, potentially affecting transaction speeds and overall system performance.
  • Cost
    The need for specialized knowledge and potentially complex hardware setups can translate to higher costs, which may not be feasible for all organizations.
  • Less Decentralization
    Because Hyperledger is permissioned, it offers less decentralization compared to public blockchains. This could be a drawback for users who prioritize a decentralized network.

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.

Hyperledger videos

+ Add

Traxion ICO review - Hyperledger fabric technology

More videos:

  • Review - Matrix AI Review - $MAN - Intelligent Blockchain - Easier | Safer | Faster | Flexible + Hyperledger
  • Review - Overview: Agents and Hyperledger Indy - Kyle Den Hartog, Evernym - Part 1

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 Hyperledger and Apache Spark)

Hyperledger

Apache Spark

Cloud Infrastructure
100 100%
Cloud Infrastructure
0% 0
Databases
0 0%
Databases
100% 100
Cloud Computing
100 100%
Cloud Computing
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 Hyperledger and Apache Spark

Hyperledger Reviews

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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 Hyperledger. While we know about 70 links to Apache Spark, we've tracked only 2 mentions of Hyperledger. 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.

Hyperledger mentions (2)

  • Do You Need a Blockchain?
    In my day job[0], I talk to a lot of start-up ventures about blockchain. Only one was honest enough to say they were only using it because, at the time, it was easier to get funding. [0]: https://hyperledger.org/. - Source: Hacker News / over 3 years ago
  • Ethereum Tech Used to Build a Smart Contract Platform for 5G Mobile Networks
    Ethereum is not just currency at its core, its a smart contract platform which is used to implement distributed consensus, where each participating party sign the result, with their consensus algorithm. Currency is a side effect. You can just remove the entire ETH/gas dependency on the base, to use the platform as a distributed ledger between all the participants. And use another kind of consensus algo(proof of... Source: almost 4 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 / about 1 month 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 / about 1 month 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 / 3 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 Hyperledger and Apache Spark, you can also consider the following products

Ethereum - Ethereum is a decentralized platform for applications that run exactly as programmed without any chance of fraud, censorship or third-party interference.

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

IBM MQ - IBM MQ is messaging middleware that simplifies and accelerates the integration of diverse applications and data across multiple platforms.

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

BlockCypher - AWS for Block Chains

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

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