Based on our record, nginx should be more popular than Apache Flink. It has been mentiond 46 times since March 2021. 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.
Can’t find any changelog other than this, > nginx-1.26.0 stable version has been released, incorporating new features and bug fixes from the 1.25.x mainline branch — including experimental HTTP/3 support, HTTP/2 on a per-server basis, virtual servers in the stream module, passing stream connections to listen sockets, and more https://nginx.org/. - Source: Hacker News / 12 days ago
However, it's very unlikely that .NET developers will directly expose their Kestrel-based web apps to the internet. Typically, we use other popular web servers like Nginx, Traefik, and Caddy to act as a reverse-proxy in front of Kestrel for various reasons:. - Source: dev.to / 2 months ago
So at least the servers that host https://nginx.org are not down. - Source: Hacker News / about 1 month ago
> curl www.mydomain.com Welcome to nginx! Html { color-scheme: light dark; } Body { width: 35em; margin: 0 auto; Font-family: Tahoma, Verdana, Arial, sans-serif; } Welcome to nginx! If you see this page,... - Source: dev.to / 3 months ago
APISIX is an API Gateway. It builds upon OpenResty, a Lua layer built on top of the famous nginx reverse-proxy. APISIX adds abstractions to the mix, e.g., Route, Service, Upstream, and offers a plugin-based architecture. - Source: dev.to / 5 months 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 / 25 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 / 5 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 / 5 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 / 5 months ago
Apache Tomcat - An open source software implementation of the Java Servlet and JavaServer Pages technologies
Apache Spark - Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
Apache HTTP Server - Apache httpd has been the most popular web server on the Internet since April 1996
Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.
Oracle WebLogic - Receive a complimentary technical review and consultation on moving your Oracle WebLogic Server applications into containers.
Spring Framework - The Spring Framework provides a comprehensive programming and configuration model for modern Java-based enterprise applications - on any kind of deployment platform.