

Apache Flink and TIBCO Streaming compete in real-time data processing. Flink is often preferred for scalability and data processing, while TIBCO Streaming stands out in enterprise integration and usability.
Features: Apache Flink offers advanced event-driven programming, state management, and large-scale data processing. TIBCO Streaming provides seamless connectivity with enterprise systems, sophisticated real-time analytics, and is favored in integration-heavy environments.
Ease of Deployment and Customer Service: Apache Flink provides flexible deployment but may require significant configuration with customer support varying due to reliance on community engagement. TIBCO Streaming offers robust customer support and easier setup within enterprise ecosystems, making it more adaptable for structured deployment and support.
Pricing and ROI: Apache Flink as an open-source solution presents lower initial setup costs with potentially higher ROI through reduced licensing fees. TIBCO Streaming, though more expensive, delivers value with integrated enterprise capabilities and support services, enhancing ROI through operational efficiencies.
| Product | Market Share (%) |
|---|---|
| Apache Flink | 12.3% |
| TIBCO Streaming | 1.1% |
| Other | 86.6% |

| Company Size | Count |
|---|---|
| Small Business | 5 |
| Midsize Enterprise | 3 |
| Large Enterprise | 12 |
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.”
TIBCO Streaming enables real-time data processing and complex event handling with exceptional throughput, facilitating immediate insights and actions. It's designed to help businesses process high-speed data for rapid decision-making and enhanced operational efficiency.
TIBCO Streaming is a real-time analytics platform designed for the fast-paced demands of modern businesses, allowing enterprises to manage streams of data effortlessly. By providing a flexible and scalable environment, it specializes in data ingestion, analysis, and response, transforming data streams into actionable insights. Its architecture supports continuous querying and real-time pattern detection, ensuring that businesses can respond promptly to emerging opportunities or threats.
What are the key features of TIBCO Streaming?In finance, TIBCO Streaming aids in the detection of fraudulent transactions by processing live data streams to spot anomalies. In telecommunications, it improves network performance monitoring by generating alerts from live data feeds, optimizing service quality for users. Its adaptability ensures it meets industry-specific demands, enhancing business outcomes across diverse sectors.
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