Open Source Mac Stream Processing Tools

Benthos 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 comes with a powerful mapping language, is easy to deploy and monitor, and ready to drop into your pipeline either as a static binary, docker image, or serverless function, making it cloud native as heck. Delivery guarantees can be a dodgy subject. Benthos processes and acknowledges messages using an in-process transaction model with no need for any disk persisted state, so when connecting to at-least-once sources and sinks it's able to guarantee at-least-once delivery even in the event of crashes, disk corruption, or other unexpected server faults. This behaviour is the default and free of caveats, which also makes deploying and scaling Benthos much simpler.

Project Flogo is an ultra-light, Go-based open source ecosystem for building event-driven apps. Event-driven, you say? Yup, the notion of triggers and actions are leveraged to process incoming events. An action, a common interface, exposes key capabilities such as application integration, stream processing, etc. All capabilities within the Flogo Ecosystem have a few things in common, they all process events (in a manner suitable for the specific purpose) and they all implement the action interface exposed by Flogo Core. Integration Flows Application Integration process engine with conditional branching and a visual development environment. A simple pipeline-based stream processing action with event joining capabilities across multiple triggers & aggregation over time windows. Microgateway pattern for conditional, content-based routing, JWT validation, rate limiting, circuit breaking and other common patterns.

Wally is a fast-stream-processing framework. Wally makes it easy to react to data in real-time. By eliminating infrastructure complexity, going from prototype to production has never been simpler. When we set out to build Wally, we had several high-level goals in mind. Create a dependable and resilient distributed computing framework. Take care of the complexities of distributed computing "plumbing," allowing developers to focus on their business logic. Provide high-performance & low-latency data processing. Be portable and deploy easily (i.e., run on-prem or any cloud). Manage in-memory state for the application. Allow applications to scale as needed, even when they are live and up-and-running. The primary API for Wally is written in Pony. Wally applications are written using this Pony API.

Built on cloud-native technologies. Taking advantage of proven stability and performance. Asynchronous and Distributed by design. High-level abstractions like Actors and Virtual Grains. Capable of millions of messages per second cross-process communication. Write systems that self-heal using supervisor hierarchies. The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It alleviates the developer from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems. Grain abstraction, which provides a straightforward approach to building distributed interactive applications, without the need to learn complex programming patterns for handling concurrency, fault tolerance, and resource management. This allows Proto.Actor to leverage in-process performance for realtime stream processing.

Go library for building event-driven applications. Our goal was to create a tool that is easy to understand, even by junior developers. It doesn't matter if you want to do Event-driven architecture, CQRS, Event Sourcing or just stream MySQL Binlog to Kafka. Watermill was designed to process hundreds of thousands of messages per second. Every component is built in a way that allows you to configure it for your needs. You can also implement your own middleware for the router. Watermill is using proven technologies and has a strong unit and integration tests coverage for critical areas. Watermill is a Go library for working efficiently with message streams. It is intended for building event driven applications, enabling event sourcing, RPC over messages, sagas and basically whatever else comes to your mind. You can use conventional pub/sub implementations like Kafka or RabbitMQ, but also HTTP or MySQL binlog if that fits your use case.

Apache Spark™ is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. The pre-built images are available in the Amazon Elastic Container Registry (Amazon ECR), and this repository serves as a reference for those wishing to build their own customized Spark containers for use in Amazon SageMaker.