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Apache Flink

Score9 out of 10

5 Reviews and Ratings

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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.

Categories & Use Cases

Key Features

Learn about the best (and worst!) features Apache Flink has to offer, as determined by TrustRadius' reviewers.
Based on 5 ratings of Apache Flink's features
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Top Performing Features

  • Real-Time Data Analysis

    Ability to analyze data in motion

    Category average: 8.5

  • Low Latency

    How many milli-seconds or seconds it takes to ingest, analyze and respond to an incoming event or data-point

    Category average: 8.4

  • Data Enrichment

    Ability to enrich the data stream with static reference data

    Category average: 7.7

Areas for Improvement

  • Linear Scale-Out

    Easy to scale out or scale down by visually changing the resource allocation. This allows changes in load or traffic to be handled without interruptions

    Category average: 7.5

  • Data Ingestion from Multiple Data Sources

    Ability to ingest data from many sources including Internet of Things (IoT) endpoint data, stock trading data etc, as well as static data

    Category average: 8.7

  • Data wrangling and preparation

    Tools to rapidly prepare data for analysis by normalization, data cleansing, etc.

    Category average: 7.8

Reviews

What users are saying about Apache Flink.
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Unrivaled Excellence in Streaming Processing and Fault Tolerance

Incentivized
Verified User
Engineer in Engineering (201-500 employees employees)

Use Cases and Deployment Scope

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.

Pros

  • 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

Cons

  • 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

Return on Investment

  • 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

Alternatives Considered

Apache Spark

Other Software Used

ClickHouse, Slack, Snowflake