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Apache Spark VS IPFS

Compare Apache Spark VS IPFS 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.

IPFS logo IPFS

IPFS is the permanent web. A new peer-to-peer hypermedia protocol.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • IPFS Landing page
    Landing page //
    2024-06-25

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.

IPFS features and specs

  • Decentralization
    IPFS operates on a peer-to-peer network, reducing dependency on central servers and improving resilience and fault tolerance.
  • Content Addressing
    Resources in IPFS are accessed through content hashes, ensuring data integrity and authenticity by directly referencing content, not its location.
  • Improved Load Distribution
    By distributing data across multiple nodes, IPFS can balance load, which can improve availability and access speed.
  • Offline Access
    Data stored in IPFS can be accessed offline if the content is already cached locally, enabling persistent availability.
  • Resistance to Censorship
    Decentralization makes it harder to censor content since there is no single point of failure that can be targeted.
  • Reduced Bandwidth Usage
    IPFS can save bandwidth by referencing previously downloaded content from local networks or peers rather than fetching it from remote servers.
  • Historical Versioning
    IPFS can keep track of historical versions of content, allowing for content versioning and retrieval of past data states.

Possible disadvantages of IPFS

  • Complexity
    Implementing and managing an IPFS network can be complex, requiring understanding of peer-to-peer networking and content addressing.
  • Initial Content Distribution
    Uploading content to IPFS and ensuring it gets distributed across the network can require significant initial effort and time.
  • Storage Redundancy
    Data is stored redundantly across multiple nodes, which can lead to increased storage requirements compared to traditional centralized storage.
  • Persistence
    Unless explicitly pinned, content might not persist indefinitely on IPFS, potentially leading to loss of data that's not sufficiently replicated.
  • Scalability of Pinning Services
    To ensure data persistence and availability, pinning services might be required, which can incur additional costs and complexity as the network scales.
  • Legal and Compliance Issues
    Decentralized storage can complicate legal compliance and content moderation, as it's harder to control and regulate distributed data.
  • Performance Variability
    Access speeds can vary based on the availability and performance of peers in the network, leading to inconsistent user experiences.
  • Energy Consumption
    Maintaining a large, distributed network of nodes can lead to higher energy consumption compared to centralized infrastructure.

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

IPFS videos

+ Add

Why IPFS? - Juan Benet

More videos:

  • Review - Ether-1 Project Review - Decentralized Web Hosting - IPFS Protocol - DAPPS
  • Review - Best Decentralised Storage Systems : ARWEAVE vs IPFS FILECOIN
  • Review - Why IPFS Is SO Important! (Simple Explanation)

Category Popularity

0-100% (relative to Apache Spark and IPFS)

Apache Spark

IPFS

Databases
100 100%
Databases
0% 0
Cloud Storage
0 0%
Cloud Storage
100% 100
Big Data
100 100%
Big Data
0% 0
File Sharing
0 0%
File Sharing
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 IPFS

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

IPFS Reviews

We have no reviews of IPFS yet.
Be the first one to post

Social recommendations and mentions

Based on our record, IPFS should be more popular than Apache Spark. It has been mentiond 290 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 / 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 / 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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IPFS mentions (290)

  • zkJSON Litepaper v1.0
    WeaveChain will be a CosmosSDK based DePIN blockchain and a marketplace to match database developers / dapps with rollup operators. It's basically a Filecoin for database. zkDB/WeaveDB is to WeaveChain as IPFS is to Filecoin. We will introduce 2 unique components to connect with real-world data and web2. - Source: dev.to / 14 days ago
  • Showcase Your Achievements Securely with CertiFolio 🚀
    IPFS (optional: if you want to run your own IPFS node). - Source: dev.to / 11 months ago
  • Decentralized media Made easy
    When I click on https://synapsemedia.io/ I get redirected to a link like https://ipfs.io/ipns/synapsemedia.io (to use ipfs.io instead of my local node). Source: about 2 years ago
  • 4EVERLAND’s IPFS Pinning Service: 4EVER Pin
    You may already be aware that the Interplanetary File System or IPFS is a distributed storage network where computers from all over the world form nodes to share data. Source: about 2 years ago
  • How to host an encrypted page
    In case of you don't trust them, it gets harder. Especially if you need to have it hosted without any trace to yourself. I'd probably pay a service to store my data on ipfs. You can pay with crypto. But I'm this case there's the question, how will you be able to access it. My thought would be to have a [tails][tails] USB with the necessary software. Source: over 2 years ago
View more

What are some alternatives?

When comparing Apache Spark and IPFS, 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.

FileCoin - Filecoin is a data storage network and electronic currency based on Bitcoin.

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

Dropbox - Online Sync and File Sharing

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

Google Drive - Access and sync your files anywhere

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