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Hetzner Object Storage VS Apache Flink

Compare Hetzner Object Storage VS Apache Flink and see what are their differences

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Hetzner Object Storage logo Hetzner Object Storage

Scalable object storage, S3-compatible and ideal for growing data volumes. Secure and flexible for efficient data storage.

Apache Flink logo Apache Flink

Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Not present

Object Storage is the S3-compatible storage solution that grows with any data requirements – secure, flexible and accessible via API. Object Storage offers the ideal solution for managing data-intensive workloads e.g. for backups, multimedia or big data. In contrast to conventional storage solutions, data is organized as objects consisting of the data itself, the metadata and a unique ID. This structure makes it easy to manage the data, search through it and organize it, and is particularly suitable for large, unstructured amounts of data. As a data center operator and hosting provider headquartered in Germany, data protection is a top priority at Hetzner. Thanks to strict compliance with the General Data Protection Regulation (GDPR), customers benefit from the highest security standards and best practices.

  • Apache Flink Landing page
    Landing page //
    2023-10-03

Hetzner Object Storage

Website
hetzner.com
Pricing URL
Official Hetzner Object Storage Pricing
$ Details
paid €5.94 / Monthly (per TB)
Release Date
2024 December
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Apache Flink

Website
flink.apache.org
Pricing URL
-
$ Details
Release Date
-
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Hetzner Object Storage features and specs

No features have been listed yet.

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.

Hetzner Object Storage 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 Hetzner Object Storage and Apache Flink)

Hetzner Object Storage

Apache Flink

Cloud Storage
100 100%
Cloud Storage
0% 0
Big Data
0 0%
Big Data
100% 100
Cloud Computing
100 100%
Cloud Computing
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 more popular. 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.

Hetzner Object Storage mentions (0)

We have not tracked any mentions of Hetzner Object Storage yet. Tracking of Hetzner Object Storage recommendations started around Apr 2025.

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 / 15 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 / 28 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 / about 1 month 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 / about 1 month 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 2 months ago
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What are some alternatives?

When comparing Hetzner Object Storage and Apache Flink, you can also consider the following products

Wasabi Cloud Object Storage - Storage made simple. Faster than Amazon's S3. Less expensive than Glacier.

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

Contabo Object Storage - S3-compatible cloud object storage with unlimited, free transfer at a fraction of what others charge. Easy migration & predictable billing. Sign up now & save.

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

IONOS Object Storage - IONOS Object Storage is a convenient, affordable and compliant way to store any amount of static company data. Pay only for what you use.

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

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