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Conduit VS Apache Flink

Compare Conduit VS Apache Flink and see what are their differences

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Conduit logo Conduit

Your data-driven AI chief of staff

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
  • Conduit Landing page
    Landing page //
    2023-01-19
  • Apache Flink Landing page
    Landing page //
    2023-10-03

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.

Apache Flink features and specs

  • Real-time Stream Processing
    Apache Flink is designed for real-time data streaming, offering low-latency processing capabilities that are essential for applications requiring immediate data insights.
  • Event Time Processing
    Flink supports event time processing, which allows it to handle out-of-order events effectively and provide accurate results based on the time events actually occurred rather than when they were processed.
  • State Management
    Flink provides robust state management features, making it easier to maintain and query state across distributed nodes, which is crucial for managing long-running applications.
  • Fault Tolerance
    The framework includes built-in mechanisms for fault tolerance, such as consistent checkpoints and savepoints, ensuring high reliability and data consistency even in the case of failures.
  • Scalability
    Apache Flink is highly scalable, capable of handling both batch and stream processing workloads across a distributed cluster, making it suitable for large-scale data processing tasks.
  • Rich Ecosystem
    Flink has a rich set of APIs and integrations with other big data tools, such as Apache Kafka, Apache Hadoop, and Apache Cassandra, enhancing its versatility and ease of integration into existing data pipelines.

Possible disadvantages of Apache Flink

  • Complexity
    Flink’s advanced features and capabilities come with a steep learning curve, making it more challenging to set up and use compared to simpler stream processing frameworks.
  • Resource Intensive
    The framework can be resource-intensive, requiring substantial memory and CPU resources for optimal performance, which might be a concern for smaller setups or cost-sensitive environments.
  • Community Support
    While growing, the community around Apache Flink is not as large or mature as some other big data frameworks like Apache Spark, potentially limiting the availability of community-contributed resources and support.
  • Ecosystem Maturity
    Despite its integrations, the Flink ecosystem is still maturing, and certain tools and plugins may not be as developed or stable as those available for more established frameworks.
  • Operational Overhead
    Running and maintaining a Flink cluster can involve significant operational overhead, including monitoring, scaling, and troubleshooting, which might require a dedicated team or additional expertise.

Analysis of Apache Flink

Overall verdict

  • Yes, Apache Flink is considered a good distributed stream processing framework.

Why this product is good

  • Rich api
    Flink offers a rich set of APIs for various levels of abstraction, catering to different needs of developers.
  • Scalability
    Flink provides excellent horizontal scalability, making it suitable for handling large data streams and high-throughput applications.
  • Fault tolerance
    Flink's checkpointing mechanism ensures fault-tolerance, maintaining data state consistency even after failures.
  • Ease of integration
    Flink integrates well with other big data tools and ecosystems, facilitating broader data architecture designs.
  • Real-time processing
    It excels at processing data in real-time, allowing for immediate insights and action on streaming data.
  • Community and support
    Being a part of the Apache Software Foundation, Flink benefits from a large community and comprehensive documentation.
  • Complex event processing
    It supports complex event processing, which is essential for many real-time applications.

Recommended for

  • real-time analytics
  • stream data processing
  • complex event processing
  • machine learning in streaming applications
  • applications requiring high-throughput and low-latency processing
  • companies looking for robust fault-tolerance in distributed systems

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

Apache Flink videos

+ Add

GOTO 2019 • Introduction to Stateful Stream Processing with Apache Flink • Robert Metzger

More videos:

  • Tutorial - Apache Flink Tutorial | Flink vs Spark | Real Time Analytics Using Flink | Apache Flink Training
  • Tutorial - How to build a modern stream processor: The science behind Apache Flink - Stefan Richter

Category Popularity

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

Conduit

Apache Flink

Productivity
100 100%
Productivity
0% 0
Big Data
0 0%
Big Data
100% 100
Data Integration
100 100%
Data Integration
0% 0
Stream Processing
0 0%
Stream Processing
100% 100

User comments

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Social recommendations and mentions

Based on our record, Apache Flink seems to be a lot more popular than Conduit. While we know about 45 links to Apache Flink, 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.

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

Apache Flink mentions (45)

  • 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
  • Towards Sub-100ms Latency Stream Processing with an S3-Based Architecture
    Many stream processing systems today still rely on local disks and RocksDB to manage state. This model has been around for a while and works fine in simple, single-tenant setups. Apache Flink, for example, uses RocksDB as its default state backend - state is kept on local disks, and periodic checkpoints are written to external storage for recovery. - Source: dev.to / 3 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
  • When plans change at 500 feet: Complex event processing of ADS-B aviation data with Apache Flink
    I wrote a python based aircraft monitor which polls the adsb.fi feed for aircraft transponder messages, and publishes each location update as a new event into an Apache Kafka topic. I used Apache Flink — and more specially Flink SQL, to transform and analyse my flight data. The TL;DR summary is I can write SQL for my real-time data processing queries — and get the scalability, fault tolerance, and low latency... - Source: dev.to / 4 months ago
  • What is Apache Flink? Exploring Its Open Source Business Model, Funding, and Community
    Continuous Learning: Leverage online tutorials from the official Flink website and attend webinars for deeper insights. - Source: dev.to / 5 months ago
View more

What are some alternatives?

When comparing Conduit and Apache Flink, you can also consider the following products

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.

Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

Stitch - Consolidate your customer and product data in minutes

Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.

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