Arroyo

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.

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.

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

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.

This interface makes it easier to identify topics which are unevenly distributed across the cluster or have partition leaders unevenly distributed across the cluster. It supports management of multiple clusters, preferred replica election, replica re-assignment, and topic creation. It is also great for getting a quick bird’s eye view of the cluster.

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.

It provides a RESTful interface to a Kafka cluster. It makes it easy to produce and consume messages, view the state of the cluster, and perform administrative actions without using the native Kafka protocol or clients. Examples of use cases include reporting data to Kafka from any frontend app built in any language, ingesting messages into a stream processing framework that doesn't yet support Kafka, and scripting administrative actions.

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.

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.

This gem is a modern Kafka client library for Ruby based on librdkafka. It wraps the production-ready C client using the ffi gem and targets Kafka 1.0+ and Ruby 2.3+.

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.

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

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 simple tool that makes your data flows observable, helps find and troubleshoot issues faster and deliver optimal performance. Its lightweight dashboard makes it easy to track key metrics of your Kafka clusters - Brokers, Topics, Partitions, Production, and Consumption.

It is a web UI for viewing Kafka topics and browsing consumer groups. The tool displays information such as brokers, topics, partitions, consumers, and lets you view messages.

It is a web application that helps you to explore messages in your Apache Kafka cluster and get better insights on what is actually happening in your Kafka cluster in the most comfortable way.

It is a Kafka GUI for topics, topics data, consumers group, schema registry, connect and more. It works with modern Kafka cluster.

It is a simple and secure self service DataOps platform, to operate with confidence on Apache Kafka & Kubernetes.

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.

It is a unified one-stop platform for Kafka cluster management and maintenance, producer / consumer monitoring, and use of ecological components.

DoctorKafka can automatically detect broker failure and reassign the workload on the failed nodes to other nodes. DoctorKafka can also perform load balancing based on topic partitions's network usage, and makes sure that broker network usage does not exceed the defined settings.

It is for enterprises that need a stable way to change Apache Kafka configurations. It maintains the configuration and avoids drifts.

It is a web GUI that helps you quickly start up Zookeeper and Kafka servers on your local machine without any code configuration. Easily view, manage, and configure your Kafka topics and brokers with a push of a button. It also displays relevant realtime metrics including Request Rate, Network I/O Rate, etc.

It is a free and open source server and web application, written in Node.js, that allows adding human intelligence to data streaming in scenarios where computers are not suitable to make educated enough choices. In just a couple lines of code it will ingest your data stream, open an HTTP server with a WebApplication that will be fed with all the data from the stream. Now you and your team can add decisions to each item of your data stream.

It is an open source downloadable desktop tool that works across all platforms and provides a simple way to visualize the performance of your Kafka cluster.

It aims to be a minimal Kafka implementation for simple workloads that wish to use Kafka as a distributed write-ahead log. It is not a general-purpose Kafka implementation, instead, it is heavily optimised for simplicity, both in terms of implementation and its emergent operational characteristics.

It is a service persisting Kafka logs to Amazon S3, Google Cloud Storage, Microsoft Azure Blob Storage and Openstack Swift.

It is an open-source web application to help small teams with monitoring and management of Apache Kafka clusters. With this tool, you can monitor key metrics related to broker and topic performance and take actions around them.

It is a Kafka health monitoring suite. It offers a dynamic solution to observe your Kafka platform in real time, compare against historically logged data and ensures your broker does not throttle user experience.

It is a visualization and optimization insight tool for Apache Kafka. It simplifies monitoring of a Kafka cluster by allowing developers to quickly view a snapshot of a cluster's health and visualize and compare topic level metrics in a cluster.

riko is a pure Python library for analyzing and processing streams of structured data. riko has synchronous and asynchronous APIs, supports parallel execution, and is well suited for processing RSS feeds. riko also supplies a command-line interface for executing flows, i.e., stream processors aka workflows.

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.

It is an open source framework that lets you build a data pipeline by simply attaching a decorator to a Python function. All you need to do is describe where your input is coming from and where your output should be written, and BuildFlow handles the rest.