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Heron vs Kafka Streams: What are the differences?
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1. State Management: Heron provides explicit state management capabilities, allowing users to access and manage the stateful operations within their applications, whereas Kafka Streams primarily focuses on stateless processing, making it more suitable for lightweight stream processing tasks.
2. Fault Tolerance: Heron offers stronger fault tolerance mechanisms, with the ability to recover from failures at both the process and instance level, while Kafka Streams relies on external systems like Apache ZooKeeper for fault tolerance, which can introduce additional complexity to the system.
3. Scalability: Heron is designed for horizontal scalability, enabling the distributed processing of large-scale data streams across a cluster of machines, while Kafka Streams is limited by the scalability of the underlying Apache Kafka infrastructure, which may pose challenges for handling massive workloads.
4. Processing Guarantees: Heron supports different processing guarantees including at-least-once, at-most-once, and effectively-once semantics, offering flexibility for users to choose the level of guarantee they need, whereas Kafka Streams provides exactly-once processing semantics out of the box, ensuring message delivery without duplicates.
5. Integration Ecosystem: Heron integrates well with various data processing frameworks such as Apache Storm and Apache Flink, providing interoperability with existing systems, whereas Kafka Streams is tightly coupled with Apache Kafka ecosystem, limiting its compatibility with other frameworks.
6. Development Model: Heron follows a more traditional development model resembling a batch processing system, with support for complex event processing, while Kafka Streams embraces a more modern stream processing paradigm, focusing on high-throughput and low-latency processing for real-time applications.
In Summary, Heron and Kafka Streams differ in their approach to state management, fault tolerance, scalability, processing guarantees, integration ecosystem, and development model.
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- Support most popular container environment1
- Highly Customizable1
- Operation friendly1
- Realtime Stream Processing1
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What is Heron?
Heron is realtime analytics platform developed by Twitter. It is the direct successor of Apache Storm, built to be backwards compatible with Storm's topology API but with a wide array of architectural improvements.
What is Kafka Streams?
It is a client library for building applications and microservices, where the input and output data are stored in Kafka clusters. It combines the simplicity of writing and deploying standard Java and Scala applications on the client side with the benefits of Kafka's server-side cluster technology.
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What companies use Heron?
What companies use Kafka Streams?
What companies use Heron?
What companies use Kafka Streams?
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What tools integrate with Heron?
What tools integrate with Kafka Streams?
What tools integrate with Heron?
No integrations found
What tools integrate with Kafka Streams?
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What are some alternatives to Heron and Kafka Streams?
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
Pelican
Pelican is a static site generator that supports Markdown and reST syntax. Write your weblog entries directly with your editor of choice (vim!) in reStructuredText or Markdown.
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
PostgreSQL
PostgreSQL is an advanced object-relational database management system
that supports an extended subset of the SQL standard, including
transactions, foreign keys, subqueries, triggers, user-defined types
and functions.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.