Apache Flink and Amazon Kinesis are products in the data processing arena, with Amazon Kinesis gaining an edge through robust AWS integration and real-time analytics features.
Features: Apache Flink's strengths include stateful stream processing, low-latency, and high-throughput capabilities. It also supports inbuilt checkpointing and statefulness, facilitating easier state management. Meanwhile, Amazon Kinesis offers features like real-time data streaming, serverless architecture, and automatic scaling, making it a seamless choice for AWS-centric operations.
Room for Improvement: Apache Flink could benefit from simplified deployment processes and reduced technical expertise requirements. Easier integration with cloud services would enhance Flink's utility. Amazon Kinesis might improve by enhancing its cost structure and extending its functionality outside the AWS ecosystem. Better documentation for complex use cases could appeal to a broader user base.
Ease of Deployment and Customer Service: Apache Flink requires manual configuration, potentially increasing deployment complexity and resource management demands. However, Amazon Kinesis provides more straightforward deployment due to its managed AWS services, reducing operational overhead. While both offer comprehensive customer service, Amazon Kinesis benefits from 24/7 AWS-backed support.
Pricing and ROI: Apache Flink as an open-source solution can offer lower long-term costs if companies have the expertise to manage it. Amazon Kinesis employs a pay-as-you-go pricing model, offering rapid setup and scaling, which can lead to a quicker ROI within an AWS-oriented framework.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.
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.”
We monitor all Streaming Analytics reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.