Apache Log4j VS Apache Flink

The official website. The vulnerability was introduced in 2.0-beta7 which was released in 2013. Source: about 1 year ago

What you need is log4j-core, what you downloaded is some kind of connector between log4j and JUL. Tbh I don't know what JUL is, but that's not important. You can get log4j-core on from the official website - https://logging.apache.org/log4j/2.x/ or in maven repo. In case you're not using maven, I highly, highly recommend you using it for managing your dependencies. Source: about 1 year ago

Log4J(https://logging.apache.org/log4j/2.x/) is a Java-based logging framework. It is a part of Apache Logging Services. It was also the most popular and widely used Java logging solution until the exposure of its Log4Shell vulnerability last year. - Source: dev.to / over 1 year ago

Almost nothing is more ubiquitous in applications than logging libraries. No matter which type of application - hastily thrown-together prototypes, decades-old enterprise monoliths, newly built event-driven serverless apps - there is always the need to log. Even in non-production-grade applications where standard observability patterns such as monitoring and alerting might not be applied - logging is usually... - Source: dev.to / about 2 years ago

Most applications currently use Log4J2 or SLF4J. Both provide a compatible System.Logger implementation. - Source: dev.to / about 2 years 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 / 27 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