quix-streams
aiokafka
| quix-streams | aiokafka | |
|---|---|---|
| 25 | 3 | |
| 796 | 1,051 | |
| 50.8% | 2.0% | |
| 9.0 | 8.6 | |
| about 15 hours ago | 12 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
quix-streams
- Show HN: Streaming DataFrames–a Pandas-like syntax for real-time data
- FLaNK AI-April 22, 2024
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Airflow VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
Airflow for Streaming
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Apache Pulsar VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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redpanda VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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ApacheKafka VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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flink-statefun VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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Apache Spark VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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beam VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
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debezium VS quix-streams - a user suggested alternative
2 projects | 7 Dec 2023
Debezium is a change data capture framework for streaming. Connect it to your databases to detect changes and produce those change events to Kafka. Quix Streams is a Python stream processing library for ML and AI applications. It builds on the Confluent Python library to add a state store with RocksDB and adds a Streaming DataFrames API with declarative operations like Windows. It is designed for analytics and data engineering workloads. Use it together with Debezium to process your CDC data in real-time.
aiokafka
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FastStream: Python's framework for Efficient Message Queue Handling
Our journey with FastStream started when we needed to integrate our machine learning models into a customer's Apache Kafka environment. To streamline this process, we created FastKafka using AIOKafka, AsyncAPI, and asyncio. It was our first step in making message queue management easier.
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Improving Kafka interfaces
aiokafka - https://github.com/aio-libs/aiokafka
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Recently joined a DE team and I've been asked to study async, multiprocessing, queuing, and Kafka. Can anybody tell me how to proceed and also share resources that I can use.
It’s hard to understand exactly what is meant by what you posted but could they be referring to asyncio libraries for use with Kafka in this process? For example, https://github.com/aio-libs/aiokafka
What are some alternatives?
confluent-kafka-python - Confluent's Kafka Python Client
quix-samples - Library samples repository of Quix. Explore and Deploy them easily on https://portal.platform.quix.ai
Faust - Python Stream Processing
qr-code - A no-framework, no-dependencies, customizable, animate-able, SVG-based <qr-code> HTML element.
faust - Python Stream Processing. A Faust fork
bytewax - Python Stream Processing
faststream - FastStream is a powerful and easy-to-use Python framework for building asynchronous services interacting with event streams such as Apache Kafka, RabbitMQ, NATS and Redis.
csv-import - The open-source CSV importer, maintained by @tableflowhq
Propan - Propan is a powerful and easy-to-use Python framework for building event-driven applications that interact with any MQ Broker
ml-runtimes
fastkafka - FastKafka is a powerful and easy-to-use Python library for building asynchronous web services that interact with Kafka topics. Built on top of Pydantic, AIOKafka and AsyncAPI, FastKafka simplifies the process of writing producers and consumers for Kafka topics.