Hangfire VS Apache Flink

Hangfire (https://hangfire.io) includes default exception handling and is very extensible, I think it's a good mid-level choice and a good alternative to other queue mechanism, if you can't afford to host a separated queue service or can't manage a separated service; also scales pretty well (you can have multiple servers handling the same background job queue, or different queues). It runs on Sql Server and MySql... Source: almost 2 years ago

I used to just use hangfire.io in .net and worked wonderfully for any long running tasks or schedules. Had a great queuing system, UI to know if they failed , etc. That's how I'd send emails, pdf's, and other things along that nature. Then if it were more just a db related operation, just setup a schedule in mssql job service. Source: almost 2 years ago

You can use hangfire for cronjob, to run at a time in future, you can use Hangfire.Schedule(jobid, datetime). Source: about 2 years ago

So another option is to use something like https://hangfire.io to pull the jobs and process them? Source: about 2 years ago

I've got a fairly large process I need to handle in background on my .net core web app so I've exported it to a background task using Hangfire. Source: almost 3 years ago

You should let the Apache Flink team know, they mention exactly-once processing on their home page (under "correctness guarantees") and in their list of features. [0] https://flink.apache.org/ [1] https://flink.apache.org/what-is-flink/flink-applications/#building-blocks-for-streaming-applications. - Source: Hacker News / 8 days 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 / about 1 month 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