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  4. Stream Processing
  5. Heron vs Kafka Streams

Heron vs Kafka Streams

OverviewComparisonAlternatives

Overview

Heron
Heron
Stacks22
Followers63
Votes4
Kafka Streams
Kafka Streams
Stacks404
Followers478
Votes0

Heron vs Kafka Streams: What are the differences?

<Write Introduction here>

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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Detailed Comparison

Heron
Heron
Kafka Streams
Kafka Streams

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.

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.

Statistics
Stacks
22
Stacks
404
Followers
63
Followers
478
Votes
4
Votes
0
Pros & Cons
Pros
  • 1
    Highly Customizable
  • 1
    Operation friendly
  • 1
    Realtime Stream Processing
  • 1
    Support most popular container environment
No community feedback yet

What are some alternatives to Heron, Kafka Streams?

Apache NiFi

Apache NiFi

An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.

Apache Storm

Apache Storm

Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.

Confluent

Confluent

It is a data streaming platform based on Apache Kafka: a full-scale streaming platform, capable of not only publish-and-subscribe, but also the storage and processing of data within the stream

KSQL

KSQL

KSQL is an open source streaming SQL engine for Apache Kafka. It provides a simple and completely interactive SQL interface for stream processing on Kafka; no need to write code in a programming language such as Java or Python. KSQL is open-source (Apache 2.0 licensed), distributed, scalable, reliable, and real-time.

Kapacitor

Kapacitor

It is a native data processing engine for InfluxDB 1.x and is an integrated component in the InfluxDB 2.0 platform. It can process both stream and batch data from InfluxDB, acting on this data in real-time via its programming language TICKscript.

Redpanda

Redpanda

It is a streaming platform for mission critical workloads. Kafka® compatible, No Zookeeper®, no JVM, and no code changes required. Use all your favorite open source tooling - 10x faster.

Faust

Faust

It is a stream processing library, porting the ideas from Kafka Streams to Python. It provides both stream processing and event processing, sharing similarity with tools such as Kafka Streams, Apache Spark/Storm/Samza/Flink.

Samza

Samza

It allows you to build stateful applications that process data in real-time from multiple sources including Apache Kafka.

Benthos

Benthos

It is a high performance and resilient stream processor, able to connect various sources and sinks in a range of brokering patterns and perform hydration, enrichments, transformations and filters on payloads.

Amazon WorkSpaces Streaming Protocol

Amazon WorkSpaces Streaming Protocol

It is a cloud-native streaming protocol that enables a consistent user experience when accessing your end user’s WorkSpaces across global distances and unreliable networks. It also enables additional features such as the beta feature of bi-directional video. As a cloud-native protocol, it delivers feature and performance enhancements without manual updates on your WorkSpaces.

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