Apache Flink might be a bit more popular than Benthos. We know about 27 links to it since March 2021 and only 22 links to Benthos. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
If you're interested in Golang and data streaming, https://benthos.dev is a good project to contribute to. There are quite a few issues open on the GitHub project which anyone can pick up. Writing new connectors and adding tests / docs is always a good place to start. The maintainer is super-friendly and he's always active on the https://benthos.dev/community channels. I'm also there most of the time, since I've... - Source: Hacker News / about 1 month ago
I have been working in the stream processing space since 2020 and I used Benthos. Since Benthos is a stateless stream processor, I have other components around it which deal with various types of application state, such as Kafka, NATS, Redis, various flavours of SQL databases, MongoDB etc. Source: almost 1 year ago
You might want to add Benthos to your stack. It’s Open Source and it works great for data streaming tasks. You could have your task orchestrator (Airflow, Flyte etc) run it on demand. I demoed it at KnativeCon last year. Source: about 1 year ago
A few years ago, I found Benthos (the Open Source data streaming processor) and it was really easy to dive into it and add new features. Going through the various 3rd party libraries that it includes is usually straightforward and I'm comfortable enough with the language and various design patterns now to quickly get what's going on. That was rarely the case with C++. Source: about 1 year ago
This is a miniature OAuth provider implemented in Benthos and Bloblang. It is designed to serve a single OAuth client app and will generate JWT access tokens with limited lifetime. Source: about 1 year ago
Data scientists often prefer Python for its simplicity and powerful libraries like Pandas or SciPy. However, many real-time data processing tools are Java-based. Take the example of Kafka, Flink, or Spark streaming. While these tools have their Python API/wrapper libraries, they introduce increased latency, and data scientists need to manage dependencies for both Python and JVM environments. For example,... - Source: dev.to / 18 days ago
Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / 3 months ago
Also, this knowledge applies to learning more about data engineering, as this field of software engineering relies heavily on the event-driven approach via tools like Spark, Flink, Kafka, etc. - Source: dev.to / 4 months ago
Apache SeaTunnel is a data integration platform that offers the three pillars of data pipelines: sources, transforms, and sinks. It offers an abstract API over three possible engines: the Zeta engine from SeaTunnel or a wrapper around Apache Spark or Apache Flink. Be careful, as each engine comes with its own set of features. - Source: dev.to / 4 months ago
Due to the technology transformation we want to do recently, we started to investigate Apache Iceberg. In addition, the data processing engine we use in house is Apache Flink, so it's only fair to look for an experimental environment that integrates Flink and Iceberg. - Source: dev.to / 4 months ago
Apache NiFi - An easy to use, powerful, and reliable system to process and distribute data.
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Apache Beam - Apache Beam provides an advanced unified programming model to implement batch and streaming data processing jobs.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.