3 Leading Stream Processing Solutions for Modern Data Teams

Apache Flink, Apache Kafka, and RisingWave have emerged as the top stream processing frameworks for 2025. Kafka, adopted by over 150,000 organizations, provides a powerful backbone for streaming data pipelines. Flink delivers ultra-low-latency stream processing, fault tolerance, and real-time analytics across industries such as finance and IoT. RisingWave simplifies data architecture with SQL-native compatibility and cost-efficient performance, making stream processing accessible for teams seeking ease of use. Selecting the right framework depends on a team’s technical requirements and business objectives, as each solution addresses different streaming and data challenges.

Key Takeaways

• Stream processing handles data in real time, enabling immediate insights and fast responses to changing conditions.

• Apache Flink offers powerful, low-latency stream and batch processing with advanced features but requires experienced engineers.

• Apache Kafka serves as a reliable backbone for high-throughput event streaming and data pipelines, supporting many industries.

• RisingWave provides a simple SQL-based interface with cloud-native scalability, making real-time analytics accessible and cost-efficient.

• Choosing the right framework depends on your team’s skills, technical needs, and business goals for best results.

• Flink suits complex event processing and unified workloads, Kafka excels in messaging and data ingestion, and RisingWave fits teams needing rapid deployment and ease of use.

• Testing a framework with a small project helps validate its fit before full adoption, reducing risks and improving success.

• Modern stream processing benefits from cloud-native designs, fault tolerance, and integration with popular tools to build scalable, efficient data pipelines.

Stream Processing Overview

Real-Time Data

Stream processing has become essential for organizations that need to act on information as soon as it arrives. Unlike traditional batch processing, which handles large datasets at scheduled intervals, stream processing enables continuous, real-time data processing. This approach allows teams to gain immediate insights and respond quickly to changing conditions.

Real-time data streams present unique challenges and opportunities. These data streams often arrive at high volume and velocity, requiring robust systems to ingest and process millions of events per second. Low latency is critical, especially in sectors like finance or IoT, where delays can lead to significant risks. Data consistency and accuracy must be maintained, even as information flows from diverse sources. Fault tolerance and reliability are non-negotiable, as any failure can disrupt operations or lead to data loss. Integration and scalability also play a major role, since real-time data ingestion must support a wide range of applications and comply with security standards.

Industries such as finance, e-commerce, and IoT rely on real-time data processing for use cases like fraud detection, personalized recommendations, and automated monitoring. For example, e-commerce platforms use streaming analytics tools to personalize customer experiences and optimize marketing campaigns, while IoT systems depend on real-time data ingestion to automate smart home devices.

Stream Processing Frameworks

Modern stream processing frameworks have evolved to meet the growing demands of real-time data processing. These frameworks provide the backbone for streaming analytics tools and data processing pipelines, enabling teams to build scalable, reliable, and low-latency solutions.

Recent trends in stream processing systems include the integration of AI for real-time analytics, cloud-native architectures for scalability and cost efficiency, and edge computing to reduce latency by processing data closer to its source. Streaming analytics tools now support real-time model training and inference, allowing businesses to adapt instantly to new data. Privacy and security enhancements, such as homomorphic encryption and differential privacy, address regulatory requirements and data governance. These advancements empower data teams to build flexible, high-performance data pipelines that deliver immediate value across industries.

Apache Flink

Apache Flink stands out as the premier stream processing engine in 2025. Organizations across industries rely on Flink for high-performance event streaming and real-time event processing. The release of Flink 2.0 marked a significant milestone, expanding its ecosystem and driving a 125% increase in adoption over the past year. Major cloud providers, including Google, now offer managed Flink services, further cementing its leadership among stream processing frameworks.

Flink Features

Flink’s architecture centers on a JobManager and distributed TaskManagers, enabling efficient coordination and execution of data processing tasks. The engine employs a streaming-first design, treating batch processing as a subset of streaming. This approach allows teams to handle both bounded and unbounded data streams with a single programming model. Flink delivers advanced event-time processing, supporting sophisticated handling of late events and watermarks. Its lightweight asynchronous checkpointing ensures robust fault tolerance with minimal performance impact.

Key features include:

• High throughput and low latency for large-scale real-time data processing.

• Exactly-once semantics and advanced state management for data integrity.

• Rich windowing functions applied to keyed streams, supporting event-time, processing-time, and session windows.

• Native support for iterative processing, which benefits machine learning and graph algorithms.

• Specialized libraries such as FlinkCEP for complex event processing and Gelly for graph analytics.

Flink’s ecosystem continues to grow, with integrations to popular tools and data sources, including Kafka, HDFS, Cassandra, and Elasticsearch.

Flink Pros & Cons

Tip: Flink’s advanced capabilities deliver exceptional performance but require expertise for optimal use.

Advantages:

• High performance and scalability, handling massive data streams with low latency.

• Unified APIs for streaming and batch data processing.

• Expressive APIs for complex transformations, windowing, and analytics.

• Robust fault tolerance with distributed checkpoints and job recovery.

• Expanding ecosystem with integrations to leading data tools.

Disadvantages:

• Steep learning curve due to complex architecture and configuration.

• Resource intensive, demanding significant memory and CPU.

• Less mature Python and SQL APIs compared to Java/Scala.

• Smaller community and limited documentation versus older frameworks.

Flink Use Cases

Flink powers mission-critical applications in diverse industries. The following table highlights real-world implementations:

Teams use Flink for event-driven applications, such as fraud detection and business process monitoring, as well as for building real-time analytics pipelines and continuous ETL workflows. Its ability to process data streams at scale makes it a top choice for organizations seeking reliable, low-latency streaming solutions.

Apache Kafka

Apache Kafka stands as a dominant event streaming platform for modern data teams. Many organizations rely on Kafka to build robust, scalable, and high-throughput stream processing pipelines. Kafka’s architecture supports distributed data processing, making it a preferred choice among stream processing frameworks for handling massive volumes of streaming data.

Kafka Features

Kafka’s distributed architecture enables seamless scaling and fault tolerance. The platform processes large data streams efficiently, supporting high throughput and low latency. Partitioning and replication optimize data distribution, ensuring resilience and durability. Kafka integrates with popular stream processing frameworks such as Apache Flink, Apache Spark Streaming, and Kafka Streams, allowing teams to build complex event-driven applications.

Key features include:

• Partitioned log storage for parallel data processing and efficient retrieval.

• Replication across multiple brokers for fault tolerance and data integrity.

• Schema Registry support for managing schema evolution and compatibility.

• Exactly-once delivery semantics for reliable data processing.

• Automated scaling and resource management using tools like Kubernetes.

• Comprehensive monitoring and alerting tools for pipeline health.

• Integration with cloud-native platforms, simplifying deployment and management.

Kafka’s backbone role in data ingestion enables teams to handle high-velocity data streams from sensors, applications, and logs. The platform supports decoupled communication in microservices, enhancing scalability and reliability.

Kafka Pros & Cons

Note: Kafka’s operational simplicity and mature ecosystem make it a top choice, but teams should consider its limitations before adoption.

Kafka’s strengths include operational simplicity, a vibrant community, and a rich ecosystem of tools. The platform’s architecture allows teams to scale stream processing pipelines with minimal overhead. However, Kafka lacks native multitenancy and robust multi-data center replication in its open-source version. Some users report higher latency spikes and occasional confusion in documentation, especially for legacy APIs.

Kafka Use Cases

Kafka powers a wide range of real-time data processing applications across industries. Major companies such as Uber, Netflix, Capital One, Barcelona, Walmart, and Tesla use Kafka to handle billions of events daily. The platform supports log aggregation, centralized logging, and operational metrics collection, simplifying troubleshooting and monitoring.

Kafka also enables decoupling of producers and consumers, facilitating flexible and scalable data pipelines. In financial services, Kafka supports real-time fraud detection. In smart cities, it manages IoT sensor data collection. Retailers use Kafka for personalized experiences, while AI and machine learning teams stream continuous data feeds for real-time predictions and model updates. Advanced security features such as end-to-end encryption and role-based access control help organizations meet compliance requirements.

RisingWave

RisingWave has quickly become a rising star among stream processing frameworks. Modern data teams recognize its unique blend of SQL-native simplicity, cloud-native scalability, and cost efficiency. This framework addresses the growing demand for real-time analytics and operational agility in cloud-based event streaming environments.

RisingWave Features

RisingWave offers a cloud-native streaming database that unifies data storage and processing. Teams do not need to manage separate storage layers, which reduces operational complexity. The framework is PostgreSQL-compatible, allowing seamless integration with existing tools and workflows. Materialized views and window aggregation support real-time data processing, enabling sub-second delay alerts and immediate insights.

Key features include:

• Built-in storage for simplified architecture and reduced maintenance.

• Compatibility with PostgreSQL tools, such as DBeaver, for easy management.

• Real-time analytics through materialized views and windowed aggregations.

• Integration with visualization tools like Superset for interactive dashboards.

• Rapid development and deployment of streaming analytics solutions.

• Cloud-native design for elastic scaling and cost efficiency.

RisingWave 2.0 introduces a unified data processing approach with a streaming-first mindset. The open-source model encourages community contributions and ongoing innovation, ensuring the framework evolves with industry needs.

RisingWave Pros & Cons

Note: RisingWave’s SQL-native interface and cloud-native architecture make it accessible and efficient for many teams, but some advanced users may seek deeper customization.

Advantages:

• Simple SQL-based interface lowers the learning curve for data engineers and analysts.

• Cloud-native scalability allows independent scaling of compute and storage resources.

• Cost-effective operation by reducing the need for external storage systems.

• Fast deployment and reduced development time, as demonstrated by DragonPass’s rapid rollout of a real-time monitoring system.

• Open-source nature fosters transparency and innovation.

Disadvantages:

• Less mature than established frameworks like Flink or Kafka, with a smaller community.

• Limited advanced customization for highly specialized stream processing needs.

• Ecosystem and third-party integrations are still growing.

RisingWave Use Cases

RisingWave demonstrates clear advantages in several real-world scenarios:

• Real-time analytics and dashboards: Teams use RisingWave to process streaming data and update materialized views incrementally, enabling dynamic visualization and immediate insights.

• IoT data processing: The framework efficiently handles continuous streams from sensors and devices, supporting anomaly detection and real-time analytics.

• Fraud detection and monitoring: RisingWave processes diverse streaming data sources, identifies suspicious patterns, and triggers timely alerts using continuous queries and stream joins.

• Transactional consistency: The platform guarantees ACID properties, ensuring reliable processing for complex, concurrent transactions, which is vital for ride-booking and financial platforms.

• Cloud-native scalability: Teams can independently scale compute and storage resources, adapting to changing workload demands without overprovisioning.

Metabit Trading leverages RisingWave for real-time monitoring and alerting in high-frequency trading. They achieved a threefold improvement in data freshness and reduced operational costs by minimizing compute nodes. DragonPass built a fully functional real-time monitoring system within days, highlighting RisingWave’s rapid deployment capabilities.

RisingWave stands out by integrating database capabilities directly into its stream processing engine. This approach eliminates the need for separate storage systems and reduces operational complexity, making it an attractive choice for modern data teams seeking efficient, scalable, and easy-to-use streaming solutions.

Best Stream Processing Frameworks: Comparison

Feature Table

Selecting the best stream processing frameworks for modern data teams requires a clear view of each platform’s strengths. The following table summarizes the core features of Apache Flink, Apache Kafka, and RisingWave, helping teams identify the right fit for their streaming analytics tools and data processing needs.

Teams should use this table to quickly compare the best stream processing frameworks and align their selection with project requirements.

Performance & Scalability

Performance and scalability remain critical for any stream processing solution. Recent benchmark studies, including the Nexmark benchmark, reveal that RisingWave delivers significant performance gains over Apache Flink. RisingWave achieves at least double the speed in most queries, with some scenarios showing over 500 times improvement. This advantage comes from its Rust-based implementation and decoupled compute-storage architecture, which allows independent scaling and cost efficiency. Flink, built on Java and using a coupled architecture, faces overhead and complexity, especially when managing large internal states or multi-stream joins. Flink’s reliance on RocksDB for state management can limit scalability and introduce operational challenges.

Both Flink and RisingWave guarantee exactly-once semantics and completeness. However, RisingWave provides additional correctness guarantees, such as consistent snapshots. Apache Kafka, while not directly compared in these benchmarks, serves as the backbone for event streaming platforms and supports linear scale-out for high-throughput data streams. Kafka’s architecture ensures reliability and low latency, making it a preferred choice for real-time analytics pipelines.

For teams prioritizing cost efficiency, ease of scaling, and superior performance in streaming workloads, RisingWave stands out. Flink remains valuable for unified batch and stream processing and advanced analytics.

Ecosystem & Integration

Integration capabilities and ecosystem support play a vital role in the adoption of streaming analytics tools. RisingWave offers broad compatibility, connecting easily with databases like MySQL, PostgreSQL, and MongoDB using simple statements. It eliminates the need for intermediate message queues in many scenarios and leverages the PostgreSQL ecosystem for seamless integration with visualization and management tools such as Grafana, Superset, DBeaver, and dbt. This approach simplifies the data stack and enhances user experience.

RisingWave’s PostgreSQL compatibility allows teams to avoid learning complex APIs or specialized SQL dialects. Its connector ecosystem includes Apache Kafka, ClickHouse, Snowflake, and Apache Iceberg. Features such as dynamic scaling, instant failure recovery, and full streaming SQL support make it a favorite among users seeking cost-efficient, real-time processing.

Apache Kafka integrates seamlessly with distributed systems and is widely adopted by organizations like LinkedIn and Netflix. Its ecosystem supports reliable, scalable, and low-latency event streaming platforms. Apache Flink excels in stateful stream processing and supports complex pipelines, but may face challenges with multi-stream joins and state management. Despite these challenges, Flink remains favored for its fault tolerance and accuracy in real-time analytics.

Cost & Efficiency

Cost and efficiency play a critical role when data teams evaluate the best stream processing frameworks. Each solution presents unique strengths and trade-offs, especially in cloud and on-premises environments.

Apache Flink supports both cloud and on-premises deployments. Teams value its distributed processing engine, which optimizes stateful streaming computations. Flink scales efficiently, but its resource requirements can increase operational costs. Organizations often deploy Flink for complex analytics, where performance justifies higher infrastructure investment. Flink integrates with many data sources, which helps reduce development time and maintenance overhead.

Apache Kafka acts as a backbone for event streaming platforms. Kafka’s distributed architecture enables high throughput and reliability. Many organizations use Kafka to decouple producers and consumers, which streamlines data pipelines. Kafka’s operational simplicity reduces management costs, but the platform does not offer direct stream processing capabilities. Teams often pair Kafka with other frameworks, which can increase total cost of ownership. Kafka’s open-source model allows flexible deployment, but cloud-managed services may introduce additional expenses.

RisingWave introduces a cloud-native architecture designed for cost efficiency. The platform claims up to ten times better performance and cost savings compared to traditional systems. RisingWave’s decoupled compute-storage model allows teams to scale resources independently, which helps control spending. The fully managed RisingWave Cloud service starts at $200 per month, making it accessible for small and medium-sized teams. RisingWave’s SQL-native interface reduces the need for specialized skills, which lowers training and onboarding costs. The platform eliminates the need for separate storage systems, which simplifies operations and further improves efficiency.

Teams seeking the best stream processing frameworks should consider both direct costs and operational efficiency. RisingWave stands out for cloud-native deployments with predictable pricing and rapid scaling. Flink remains a strong choice for organizations that prioritize advanced analytics and can invest in robust infrastructure. Kafka offers reliability and flexibility, but teams must account for integration and management expenses.

The choice depends on workload complexity, scalability needs, and budget constraints. Data teams should evaluate long-term operational costs, including infrastructure, maintenance, and personnel. Selecting the right framework ensures efficient resource utilization and supports business growth.

Choosing the Right Stream Processing Solution

Team & Skills

Selecting a stream processing framework begins with evaluating the team’s expertise and available skills. Teams with strong Java or Scala backgrounds often excel with Apache Flink, which offers advanced APIs and requires deep understanding of distributed systems. Flink’s architecture demands knowledge of state management and checkpointing, making it suitable for organizations with experienced engineers. Teams familiar with SQL and database management find RisingWave more approachable. Its SQL-native interface and PostgreSQL compatibility lower the learning curve, allowing data analysts and engineers to build real-time solutions quickly. Apache Kafka appeals to teams with experience in distributed messaging and event-driven architectures. Kafka’s operational simplicity and broad documentation support rapid onboarding for new users.

Tip: Teams should assess their existing skill sets before committing to a framework. Matching the tool to the team’s strengths reduces training time and accelerates project delivery.

Technical Needs

Technical requirements play a critical role in framework selection. Apache Flink uses a checkpointing mechanism based on the Chandy-Lamport algorithm, injecting barriers through the data stream to achieve globally consistent checkpoints. This approach supports exactly-once delivery semantics but introduces alignment overhead during checkpointing. Flink relies on local RocksDB storage with periodic checkpointing to S3, which can result in longer failure recovery times and stop-the-world scaling. RisingWave employs a global consistent checkpoint mechanism with higher frequency, enabling instant failure recovery. Its custom-built storage engine and decoupled cloud-native architecture allow transparent scaling and lightweight state management. RisingWave supports concurrent ad-hoc SQL queries and snapshot reads, simplifying correctness guarantees.

Teams must consider latency requirements, scaling capabilities, and operational complexity. Flink suits scenarios needing high parallelism and advanced event processing. RisingWave favors use cases requiring fast recovery, dynamic scaling, and ease of use with SQL-based streaming database capabilities. Kafka’s distributed architecture supports high-throughput data ingestion and reliable event streaming, but it does not offer direct stream processing features.

Business Goals

Business objectives such as cost reduction, scalability, and time-to-market influence the choice of stream processing frameworks. Benchmarking results show that all major frameworks can scale linearly with sufficient cloud resources, but resource efficiency varies. Flink’s operational complexity and resource demands may increase costs, especially for large-scale deployments. RisingWave’s cloud-native design and decoupled architecture enable cost-effective scaling and predictable pricing, supporting organizations focused on budget control. Kafka’s reliability and flexibility make it a strong backbone for event-driven systems, but integrating additional processing tools can raise total costs.

Optimizing stream processing pipelines reduces latency and increases throughput, supporting faster time-to-market. Frameworks that enable dynamic scaling and load balancing help manage variable data loads, enhancing scalability. Real-time analytics accelerates innovation cycles, enabling rapid hypothesis testing and continuous feedback. Automation and operational efficiency improve capacity utilization and support cost reduction goals.

Note: Aligning framework selection with business goals ensures efficient resource utilization and supports long-term growth.

When to Choose Each

Selecting the right stream processing framework depends on several factors. Teams must consider their skills, technical requirements, and business objectives. The following table summarizes when each solution fits best:

Apache Flink
Teams choose Flink when they need ultra-low latency and advanced stateful processing. Flink excels in environments that require unified batch and stream workloads. Engineers use Flink for complex event-driven applications, such as financial fraud detection or predictive maintenance in IoT. Flink suits organizations with strong Java or Scala expertise. Teams that prioritize exactly-once semantics and robust fault tolerance benefit from Flink’s architecture.

Apache Kafka
Kafka serves as the backbone for scalable data pipelines. Teams select Kafka for high-throughput event streaming and reliable log storage. Kafka fits organizations that need to decouple producers and consumers, enabling flexible microservices architectures. Distributed systems experts use Kafka for messaging, operational metrics, and centralized logging. Kafka integrates with other processing frameworks, making it ideal for teams building modular data platforms.

RisingWave
RisingWave appeals to teams seeking simplicity and rapid deployment. Data analysts and engineers use RisingWave’s SQL-native interface to build real-time dashboards and analytics with minimal coding. RisingWave fits cloud-native environments that require elastic scaling and predictable costs. Teams with limited engineering resources benefit from RisingWave’s ease of use and fast failure recovery. Organizations that prioritize cost efficiency and quick time-to-market often choose RisingWave.

Tip: Teams should pilot the selected framework with a small project before full-scale adoption. This approach helps validate performance and compatibility with existing workflows.

Example Decision Paths:

• A financial institution with a dedicated engineering team selects Flink for real-time risk analysis.

• An e-commerce company uses Kafka to manage high-volume clickstream data and integrate multiple microservices.

• A startup with limited resources chooses RisingWave to launch a real-time analytics dashboard without complex infrastructure.

Teams must align their choice with project goals, available expertise, and long-term scalability needs. The right framework supports efficient data processing and drives business success.

Each stream processing framework offers unique advantages. Apache Flink delivers advanced analytics but requires deep expertise and higher maintenance. RisingWave provides a SQL-based interface, low maintenance, and cost efficiency for real-time analytics. Apache Kafka excels as a reliable backbone for high-throughput data pipelines. Teams should match their expertise and project needs to the right tool. Before full adoption, organizations benefit from pilot testing with clear objectives, measurable metrics, and active stakeholder engagement.

FAQ

What is the main difference between stream processing and batch processing?

Stream processing handles data in real time as it arrives. Batch processing collects data over time and processes it in groups. Stream processing supports immediate insights, while batch processing focuses on high throughput and accuracy.

Can teams use Apache Kafka, Flink, and RisingWave together?

Yes. Many organizations combine Kafka for data ingestion, Flink for complex event processing, and RisingWave for real-time analytics. This approach leverages each tool’s strengths and creates a robust streaming data pipeline.

Which framework is best for teams with limited engineering resources?

RisingWave offers a SQL-native interface and cloud-native deployment. Teams can build real-time analytics solutions quickly without deep expertise in distributed systems or programming languages.

How does RisingWave ensure data consistency and reliability?

RisingWave uses ACID-compliant transactions and consistent snapshots. This design guarantees reliable processing and accurate results, even during failures or scaling events.

Does Apache Flink support both batch and stream processing?

Apache Flink provides a unified engine for both batch and stream processing. Teams can use the same APIs and architecture for real-time and historical data workloads.

What are common use cases for Apache Kafka?

• Real-time data pipelines

• Log aggregation

• Messaging between microservices

• Operational metrics collection

Kafka excels in high-throughput, low-latency environments.

How do these frameworks handle scaling in the cloud?

Flink and Kafka support distributed scaling with managed services. RisingWave uses a decoupled compute-storage model for elastic scaling. Teams can adjust resources based on workload demands, ensuring cost efficiency and performance.