Apache Flink and Apache Spark Streaming are competing in the stream processing category. Flink has the upper hand in real-time analytics with its low-latency performance and complex event processing features, whereas Spark Streaming excels in versatility and ecosystem support, easing the integration process with existing infrastructures.
Features: Apache Flink offers real-time data processing with stateful transformations, support for both streaming and batch data, and a checkpointing mechanism for fault tolerance. Spark Streaming provides robust integration with the Spark ecosystem, a unified API for batch and streaming, and excellent scalability for large data processing tasks.
Room for Improvement: Apache Flink can benefit from improved ease of deployment and more comprehensive community support. Its complexity may require a steeper learning curve and additional resource allocation for optimal performance. Spark Streaming could improve in handling extremely low latency tasks and offering advanced stateful stream processing features. Its dependency on existing Spark infrastructure might limit standalone deployments, and additional support for complex event processing could be valuable.
Ease of Deployment and Customer Service: Apache Flink offers flexibility in deployment but can be complex, often requiring in-depth expertise. Its community-driven support might pose challenges for less technical users. Apache Spark Streaming seamlessly integrates within Spark infrastructures, simplifying deployment. With robust community and commercial support options, it offers more accessible user resources.
Pricing and ROI: Apache Flink may involve higher initial costs because of its complexity but can deliver significant ROI with its low-latency applications. Apache Spark Streaming stands out in cost-efficiency due to its smooth integration capabilities, potentially yielding quicker ROI through reduced implementation time and leveraging existing resources.
Apache Flink is an open-source batch and stream data processing engine. It can be used for batch, micro-batch, and real-time processing. Flink is a programming model that combines the benefits of batch processing and streaming analytics by providing a unified programming interface for both data sources, allowing users to write programs that seamlessly switch between the two modes. It can also be used for interactive queries.
Flink can be used as an alternative to MapReduce for executing iterative algorithms on large datasets in parallel. It was developed specifically for large to extremely large data sets that require complex iterative algorithms.
Flink is a fast and reliable framework developed in Java, Scala, and Python. It runs on the cluster that consists of data nodes and managers. It has a rich set of features that can be used out of the box in order to build sophisticated applications.
Flink has a robust API and is ready to be used with Hadoop, Cassandra, Hive, Impala, Kafka, MySQL/MariaDB, Neo4j, as well as any other NoSQL database.
Apache Flink Features
Apache Flink Benefits
Reviews from Real Users
Apache Flink stands out among its competitors for a number of reasons. Two major ones are its low latency and its user-friendly interface. PeerSpot users take note of the advantages of these features in their reviews:
The head of data and analytics at a computer software company notes, “The top feature of Apache Flink is its low latency for fast, real-time data. Another great feature is the real-time indicators and alerts which make a big difference when it comes to data processing and analysis.”
Ertugrul A., manager at a computer software company, writes, “It's usable and affordable. It is user-friendly and the reporting is good.”
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications.
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