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Apache Spark VS Conduit

Compare Apache Spark VS Conduit 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.

Conduit logo Conduit

Your data-driven AI chief of staff
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Conduit Landing page
    Landing page //
    2023-01-19

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.

Conduit features and specs

  • Privacy-focused
    Conduit is built with a strong emphasis on user privacy, employing end-to-end encryption to ensure that all communication is secure and private.
  • Lightweight
    Designed to be lightweight, Conduit is able to run efficiently on low-resource systems, making it suitable for a wide range of deployment environments.
  • Federated Network Support
    Conduit supports the Matrix protocol, which allows for decentralized communication across servers, providing flexibility and resilience.
  • Open Source
    As an open-source project, Conduit allows users and developers to inspect, modify, and contribute to the codebase, fostering community involvement and transparency.
  • Easy Setup
    The platform is designed with an easy setup process, making it accessible for users who may not be deeply technical to set up and run their own server.

Possible disadvantages of Conduit

  • Limited Features
    Compared to more established platforms, Conduit may have a more limited feature set, which could be a disadvantage for users requiring advanced functionalities.
  • Maturity
    Being a relatively new project, it may lack the maturity and stability of more established communication platforms, potentially resulting in less polished experiences.
  • Community Size
    With a smaller user and developer community compared to some alternative platforms, users might find less support or fewer third-party integrations available.
  • Scaling Challenges
    While designed to be lightweight, users may encounter challenges when attempting to scale Conduit for very large deployments, as optimizations may still be ongoing.
  • Learning Curve
    For users unfamiliar with the Matrix protocol or federated systems, there could be a learning curve involved in understanding how to make the most of Conduit's capabilities.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

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

Conduit videos

+ Add

The Conduit Nintendo Wii Review - Video Review

More videos:

  • Review - Conduit 2 Video Review
  • Review - Classic Game Room HD - THE CONDUIT for Wii review

Category Popularity

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

Apache Spark

Conduit

Databases
100 100%
Databases
0% 0
Productivity
0 0%
Productivity
100% 100
Big Data
100 100%
Big Data
0% 0
Data Integration
0 0%
Data Integration
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 Conduit

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

Conduit Reviews

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

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Conduit. While we know about 72 links to Apache Spark, we've tracked only 1 mention of Conduit. 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 (72)

  • Gravitino - the unified metadata lake
    In the meantime, other query engine support is on the roadmap, including Apache Spark, Apache Flink, and others. - Source: dev.to / about 2 months ago
  • Introducing RisingWave's Hosted Iceberg Catalog-No External Setup Needed
    Because the hosted catalog is a standard JDBC catalog, tools like Spark, Trino, and Flink can still access your tables. For example:. - Source: dev.to / 3 months ago
  • 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 / 5 months 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 / 6 months 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 / 7 months ago
View more

Conduit mentions (1)

  • What services you guys used for CDC (Change Data capture) for Sql as well as no sql databases ?
    If you're looking for a tool with a UI and in which you can also easily extend the functionality with your own, custom data connectors, you might also want take a look at Conduit which is another open-source tool we've developed to make building and running real-time data infrastructure more straightforward and less time consuming. Source: about 3 years ago

What are some alternatives?

When comparing Apache Spark and Conduit, 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.

FactBranch - Build data apps inside your everyday tools, so nothing slows you down. Connect to your database, API, or CRM and build user interfaces that show data in your other tools.

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

Airtable - Airtable works like a spreadsheet but gives you the power of a database to organize anything. Sign up for free.

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

Stitch - Consolidate your customer and product data in minutes

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