Unrivaled Excellence in Streaming Processing and Fault Tolerance
Apache Flink is employed within our company exclusively in our real-time data pipeline. Apache Flink stands out as one of the few …
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What is Apache Flink?
Apache Flink is a framework and distributed processing engine designed for stateful computations over unbounded and bounded data streams. It is a versatile solution suitable for companies of all sizes, from small startups to large enterprises. According to the vendor, Apache Flink is utilized by a range...
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What is Apache Flink?
Apache Flink is a framework and distributed processing engine for stateful computations over unbounded and bounded data streams. Flink has been designed to run in all common cluster environments, perform computations at in-memory speed and at any scale. And FlinkCEP is the Complex Event Processing…
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Product Details
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What is Apache Flink?
Apache Flink is a framework and distributed processing engine designed for stateful computations over unbounded and bounded data streams. It is a versatile solution suitable for companies of all sizes, from small startups to large enterprises. According to the vendor, Apache Flink is utilized by a range of professionals and industries, including data engineers, data scientists, software engineers, IT professionals, and the financial services sector.
Key Features
Exactly-once state consistency: According to the vendor, Apache Flink provides exactly-once state consistency to eliminate duplicate or missing data issues.
Event-time processing: Apache Flink supports event-time processing, allowing users to handle data streams based on event timestamps for accurate processing of out-of-order events and delayed data.
Sophisticated late data handling: Flink offers mechanisms to handle late data in event streams, enabling users to define windowing strategies that accommodate late data arrivals.
Layered APIs: Apache Flink provides layered APIs, including SQL on both stream and batch data, DataStream API for low-level stream processing, and DataSet API for batch processing, offering flexibility and ease of use for developers.
SQL on Stream & Batch Data: Flink allows users to write SQL queries to process both streaming and batch data, leveraging SQL skills for data transformations, aggregations, and analytics.
DataStream API & DataSet API: Flink provides DataStream API for building low-level stream processing applications and DataSet API for batch processing, offering a rich set of operators and functions for data manipulation and complex computations.
ProcessFunction (Time & State): According to the vendor, Flink's ProcessFunction API allows developers to define custom functions for data stream processing based on time and state, providing fine-grained control over event processing and state management.
Flexible deployment: Apache Flink supports flexible deployment options, allowing users to run applications on various cluster environments such as standalone clusters, Apache Mesos, Apache Hadoop YARN, and Kubernetes, enabling seamless integration with existing infrastructure.
High-availability setup: Flink supports high-availability setups for fault tolerance and continuous operation of streaming applications, with mechanisms for automatic failover and recovery.
Savepoints: According to the vendor, Flink allows users to create savepoints, consistent snapshots of application state, for upgrades, debugging, or restoring the application state in case of failures.
Apache Flink Technical Details
| Operating Systems | Unspecified |
|---|---|
| Mobile Application | No |
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Community Insights
TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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Users have found that Apache Flink effectively addresses their needs in managing and controlling projects and companies by supporting international and national accounting and controlling standards. With its modern interface and user experience, users have praised Apache Flink for making project management easy and enjoyable. Additionally, Apache Flink has proven to be highly versatile in developing Java web apps, providing an excellent user experience and offering great value for the money. Users have also successfully utilized Apache Flink for both online streaming and offline batch processing, using it to enrich incoming data and perform aggregation tasks. The scalability of Apache Flink has been highly regarded, with users deploying it with hundreds of nodes in AWS using Kubernetes containers. The Flink UI has been described as a valuable tool for users, enabling them to debug issues efficiently and streamline their workflows. Furthermore, users have leveraged Apache Flink for IoT data processing and analytics, taking advantage of its capabilities for stream processing and real-time anomaly detection. With its distributed streaming dataflow engine, Apache Flink has empowered users to build real-time data pipelines that handle large volumes of data for various use cases.
Easy and enjoyable to use: Many users have found Apache Flink to be easy and fun to use, making their experience with the software enjoyable.
Comprehensive approach: Users appreciate that Apache Flink takes into account requirements, rules, and international standards for modern project and enterprise management, ensuring a comprehensive approach.
Open-source nature: The open-source nature of Apache Flink is highly valued by users as it allows for customization and community contributions.
Lack of Customizability: Some users have expressed a desire for more flexibility in Apache Flink, particularly when it comes to turning off unused functions individually. They suggest having the option to disable features such as the project structure configurator, process tracking, or the role and hierarchy model.
Limited Mobile Optimization: Reviewers have noted that there is currently no optimized version of Apache Flink for mobile devices. This limitation may hinder users who rely heavily on mobile platforms and prefer to access the software on-the-go.
Lack of Unique Features Compared to Competitors: Several users have mentioned that they feel Apache Flink lacks unique features when compared to other real-time frameworks like Apache Spark. While Flink is gaining popularity as a tool for stream data processing, some reviewers believe it could benefit from more distinctive capabilities.
Reviews
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January 15, 2024
Unrivaled Excellence in Streaming Processing and Fault Tolerance
Apache Flink is employed within our company exclusively in our real-time data pipeline. Apache Flink stands out as one of the few frameworks capable of providing the scalable and distributed processing we require while ensuring the integrity and fault tolerance of our pipeline through its built-in systems. Without Apache Flink, we might struggle to get valuable insights and benefits to our business.
- Low latency Stream Processing, enabling real-time analytics
- Scalability, due its great parallel capabilities
- Stateful Processing, providing several built-in fault tolerance systems
- Flexibility, supporting both batch and stream processing
- Python/SQL API, since both are relatively new, still misses a few features in comparison with the Java/Scala option
- Steep Learning Curve, it's documentation could be improved to something more user-friendly, and it could also discuss more theoretical concepts than just coding
- Community smaller than other frameworks
57.77777777777778%
5.8
- Allowed for real-time data recovery, adding significant value to the busines
- Enabled us to create new internal tools that we couldn't find in the market, becoming a strategic asset for the business
- Enhanced the overall technical capability of the team
Apache Spark is more user-friendly and features higher-level APIs. However, it was initially built for batch processing and only more recently gained streaming capabilities. In contrast, Apache Flink processes streaming data natively. Therefore, in terms of low latency and fault tolerance, Apache Flink takes the lead. However, Spark has a larger community and a decidedly lower learning curve.