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data.world VS Apache Flink

Compare data.world VS Apache Flink and see what are their differences

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Turn SQL Data into Decisions. Build professional dashboards and data visualizations without technical expertise. Easily embed analytics anywhere, receive automated alerts, and discover AI-powered insights all through a straightforward interface. featured
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data.world logo data.world

The social network for data people

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
  • data.world Landing page
    Landing page //
    2023-09-26
  • Apache Flink Landing page
    Landing page //
    2023-10-03

data.world

Website
data.world
Pricing URL
Official data.world Pricing
$ Details
-
Release Date
2015 January
Startup details
Country
United States
State
Texas
City
Austin
Founder(s)
Brett Hurt
Employees
50 - 99
Edit details

Apache Flink

Website
flink.apache.org
Pricing URL
-
$ Details
Release Date
-
Edit details

data.world features and specs

  • Collaborative Environment
    data.world provides a platform for teams to collaborate on data projects in real-time, making it easier for data scientists, analysts, and enthusiasts to work together and share insights.
  • Integration Capabilities
    The platform supports integrations with popular tools and services like Excel, Tableau, and Python, making it easier to import, export, and manipulate data across various applications.
  • Extensive Dataset Catalog
    data.world offers a vast collection of public datasets, empowering users to find and leverage data from a wide range of sources for their projects.
  • Querying Tools
    Users can execute SQL queries directly on the data.world platform, enabling powerful data analysis and transformations within the environment.
  • User-Friendly Interface
    The platform features an intuitive user interface that makes it accessible for users with varying levels of technical expertise.

Possible disadvantages of data.world

  • Pricing
    While data.world offers a free tier, more advanced features and functionality require a paid subscription, which might be cost-prohibitive for individuals or smaller organizations.
  • Learning Curve
    Despite its user-friendly interface, there is still a learning curve associated with fully utilizing all of the platform's features, particularly for users who are not familiar with SQL or data analysis tools.
  • Performance Limitations
    For very large datasets or complex analytical operations, the platform may experience performance constraints, potentially requiring users to rely on more powerful, external data processing tools.
  • Data Privacy Concerns
    As with any cloud-based platform, there are inherent data privacy and security concerns. Users must be cautious about the sensitivity of the data they upload and ensure compliance with relevant regulations.
  • Feature Parity with Competitors
    While data.world offers many great features, some users might find that other data collaboration platforms provide more advanced or specialized tools that better suit their needs.

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.

data.world videos

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Apache Flink videos

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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 data.world and Apache Flink)

data.world

Apache Flink

Data Dashboard
100 100%
Data Dashboard
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

Share your experience with using data.world and Apache Flink. For example, how are they different and which one is better?
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Social recommendations and mentions

Based on our record, Apache Flink should be more popular than data.world. It has been mentiond 41 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.

data.world mentions (24)

  • Is data at every company still an absolute mess?
    I'll be sure to check out data.world propose to use it if it makes sense, thanks. Source: almost 2 years ago
  • GIS data for a project. I apologize for the banality of my request and for my English.
    Just google qgis datasets. There are so so many interesting sets you will find. Check out qgis.org, or data.world for starters. Source: about 2 years ago
  • Best way to open source a my dataset?
    But, I'm also aware that there are dedicated platforms to catalog and share data (e.g. https://www.dolthub.com/, https://data.world/), and that uploading data on Github, in general, doesn't seem best practise. Source: over 2 years ago
  • Alation vs. Atlan vs. Collibra
    The client is considering the 3 I mentioned, plus data.world. I need to research that one next. Microsoft Purview has already been considered. Source: over 2 years ago
  • Looking for christmas cost dataset by year and country.
    Im looking for Christmas cost dataset by year and country, Im looking in the data.world and other web pages and I cant found anything. Source: over 2 years ago
View more

Apache Flink mentions (41)

  • 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 / 2 days ago
  • Is RisingWave the Next Apache Flink?
    Apache Flink, known initially as Stratosphere, is a distributed stream processing engine initiated by a group of researchers at TU Berlin. Since its initial release in May 2011, Flink has gained immense popularity in both academia and industry. And it is currently the most well-known streaming system globally (challenge me if you think I got it wrong!). - Source: dev.to / 16 days 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 / 20 days ago
  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / 25 days ago
  • Twitter's 600-Tweet Daily Limit Crisis: Soaring GCP Costs and the Open Source Fix Elon Musk Ignored
    Apache Flink: Flink is a unified streaming and batching platform developed under the Apache Foundation. It provides support for Java API and a SQL interface. Flink boasts a large ecosystem and can seamlessly integrate with various services, including Kafka, Pulsar, HDFS, Iceberg, Hudi, and other systems. - Source: dev.to / about 1 month ago
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What are some alternatives?

When comparing data.world and Apache Flink, you can also consider the following products

Denodo - Denodo delivers on-demand real-time data access to many sources as integrated data services with high performance using intelligent real-time query optimization, caching, in-memory and hybrid strategies.

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

IBM Cloud Pak for Data - Move to cloud faster with IBM Cloud Paks running on Red Hat OpenShift – fully integrated, open, containerized and secure solutions certified by IBM.

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

Informatica Intelligent Data Platform - Unleash data's potential with Informatica infrastructure services that all roll up under a robust and intelligent data integration platform.

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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