From d8f5c7116ddbf88050ca44fda1ca37a7579750eb Mon Sep 17 00:00:00 2001 From: merlin-lacuna <57262132+merlin-lacuna@users.noreply.github.com> Date: Mon, 20 Feb 2023 19:36:36 +1300 Subject: [PATCH 1/2] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 895bbcd..3d13834 100644 --- a/README.md +++ b/README.md @@ -154,6 +154,7 @@ multi-processor, multi-core machines * [Robinhood's Faust](https://github.com/faust-streaming/faust) Forever scalable event processing & in-memory durable K/V store as a library with asyncio & static typing. * [HStreamDB](https://github.com/hstreamdb/hstream) The streaming database built for IoT data storage and real-time processing. * [Kuiper](https://github.com/emqx/kuiper) An edge lightweight IoT data analytics/streaming software implemented by Golang, and it can be run at all kinds of resource-constrained edge devices. +* [Quix.io](https://quix.io/product/for-data-processing-pipelines/) A serverless platform for deploying and running Python projects that need to process high-frequency data streams (such as sensor data or other time-series data) in real time with nano-second precision. # Batch Processing * [Hadoop MapReduce](https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner From 11c1a69c33124806088816724ea39d2f439dd7b9 Mon Sep 17 00:00:00 2001 From: merlin-lacuna <57262132+merlin-lacuna@users.noreply.github.com> Date: Mon, 20 Feb 2023 19:39:55 +1300 Subject: [PATCH 2/2] Add Quix Streams (stream processing w Kafka) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Quix is a powerful stream processing and model serving platform with an integrated Python IDE and client library. The platform was developed at McLaren for running physics models on huge sensor data streams from F1 racing cars but is also useful for anyone who wants to apply ML to sensor data or any other high-frequency time-series data. Useful Links: - [Run an ML model in a real-time environment — Quix Docs](https://docs.quix.io/platform/tutorials/train-and-deploy-ml/deploy-ml.html) - [A quick guide to real-time machine learning — Quix Blog](https://www.quix.io/blog/real-time-machine-learning-quick-guide/) - [The Quix Streams client library](https://github.com/quixio/quix-streams) ## What's the difference between this tool and similar ones? * Under the hood, it uses managed versions of Apache Kafka, Kubernetes, and Docker to abstract away much of the operational complexity involved in deploying ML models. * Natively supports Python which means ML engineers and data scientists can port code directly from Jupyter notebooks and local IDEs into the Quix development environment. * Python projects can be deployed as serverless functions with a couple of clicks using the Quix Platform UI. * Includes an open-source [Quix Streams](https://github.com/quixio/quix-streams) client library for processing streams of time-series data (can also stream data from a local CSV file). --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3d13834..1be5a8e 100644 --- a/README.md +++ b/README.md @@ -154,7 +154,7 @@ multi-processor, multi-core machines * [Robinhood's Faust](https://github.com/faust-streaming/faust) Forever scalable event processing & in-memory durable K/V store as a library with asyncio & static typing. * [HStreamDB](https://github.com/hstreamdb/hstream) The streaming database built for IoT data storage and real-time processing. * [Kuiper](https://github.com/emqx/kuiper) An edge lightweight IoT data analytics/streaming software implemented by Golang, and it can be run at all kinds of resource-constrained edge devices. -* [Quix.io](https://quix.io/product/for-data-processing-pipelines/) A serverless platform for deploying and running Python projects that need to process high-frequency data streams (such as sensor data or other time-series data) in real time with nano-second precision. +* [Quix.io](https://quix.io/product/for-data-processing-pipelines/) A scalable serverless platform for deploying and running Python projects that need to process high-frequency data streams (such as sensor data or other time-series data) in real time with nano-second precision. # Batch Processing * [Hadoop MapReduce](https://hadoop.apache.org/docs/current/hadoop-mapreduce-client/hadoop-mapreduce-client-core/MapReduceTutorial.html) Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner