Spark Streaming
Spark Structured Streaming simplifies the creation of streaming applications by abstracting complex concepts like incremental processing and checkpointing. Utilizing the same structured APIs as batch jobs, it enables seamless migration and code reuse. Built on Spark's robust architecture, it ensures low-latency performance and cost efficiency for real-time data processing.
Top Spark Streaming Alternatives
Leo
Leo transforms data into a real-time stream, enhancing accessibility and usability.
IBM Event Streams
IBM Event Streams is an event streaming platform leveraging Apache Kafka to facilitate real-time data processing.
InfinyOn Cloud
InfinyOn Cloud revolutionizes the creation of event-driven data pipelines, allowing users to build and deploy complex streaming operations in minutes.
Apache Heron
Apache Heron serves as a real-time, distributed, fault-tolerant stream processing engine that enables efficient data processing in dynamic environments.
DeltaStream
DeltaStream is a unified serverless stream processing platform that simplifies real-time analytics and data governance.
Red Hat OpenShift Streams
Red Hat OpenShift Streams for Apache Kafka offers a managed cloud service tailored for developers to efficiently build and scale cloud-native applications.
Cogility Cogynt
It enables seamless model creation without coding, utilizes Hierarchical Complex Event Processing for accurate behavior...
Astra Streaming
It enables developers to create responsive applications with massive throughput and low latency, seamlessly integrating...
Conduktor
Its modular solutions allow organizations to seamlessly integrate with Kafka, facilitating real-time data management and...
Superstream
With options for both self-deployment and full management, it ensures minimal data exposure while providing...
Spring Cloud Data Flow
Utilizing Spring Boot applications, it supports diverse data processing tasks, including ETL, event streaming, and...
kPow
With robust features like Data Inspect and kREPL, it enables rapid troubleshooting and data search...
Pathway
Leveraging a powerful Rust engine, it facilitates incremental computation and supports multithreading and distributed tasks...
Eclipse Streamsheets
This no-code platform facilitates workflow automation and monitoring, operating seamlessly in the cloud or on-premises...
Top Spark Streaming Features
- Incremental processing abstraction
- Unified batch and streaming APIs
- Low latency application support
- Cost-effective streaming pipelines
- Built-in checkpointing mechanisms
- Watermark management for late data
- Language-integrated stream processing
- Out-of-the-box state recovery
- Reuse of existing batch code
- Integration with historical data
- Interactive application development
- Supports Java
- Scala
- and Python
- High availability with ZooKeeper
- Local run mode for development
- Tested with every Spark release
- Contributions and community support
- Easy migration of Spark jobs
- Operational simplicity for developers
- Unified architecture for performance optimizations
