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Based on our record, Apache Spark seems to be a lot more popular than Resque. While we know about 56 links to Apache Spark, we've tracked only 5 mentions of Resque. 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.
Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉. - Source: dev.to / 3 months ago
Consume data into third party software (then let Open Search or Apache Spark or Apache Pinot) for analysis/datascience, GIS systems (so you can put reports on a map) or any ticket management system. - Source: dev.to / 4 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
A JVM based framework named "Spark", when https://spark.apache.org exists? - Source: Hacker News / 11 months ago
You can use a background job queue like Resque to scrape and process data in the background, and a scheduler like resque-scheduler to schedule jobs to run your scraper periodically. Source: almost 2 years ago
So how do we trigger such a long-running process from a Rails request? The first option that comes to mind is a background job run by some of the queuing back-ends such as Sidekiq, Resque or DelayedJob, possibly governed by ActiveJob. While this would surely work, the problem with all these solutions is that they usually have a limited number of workers available on the server and we didn’t want to potentially... - Source: dev.to / about 2 years ago
Background jobs are another limitation. Since only the Aha! Web service runs in a dynamic staging, the host environment's workers would process any Resque jobs that were sent to the shared Redis instance. If your branch hadn't updated any background-able methods, this would be no big deal. But if you were hoping to test changes to these methods, you would be out of luck. - Source: dev.to / about 2 years ago
The Schedules worker corresponds to the appwrite-schedule service in the docker-compose file. The Schedules worker uses a Resque Scheduler under the hood and handles the scheduling of CRON jobs across Appwrite. This includes CRON jobs from the Tasks API, Webhooks API, and the functions API. - Source: dev.to / about 3 years ago
There are a few of popular systems. A few need a database, such as Delayed::Job, while others prefer Redis, such as Resque and Sidekiq. - Source: dev.to / about 3 years ago
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Sidekiq - Sidekiq is a simple, efficient framework for background job processing in Ruby
Apache Airflow - Airflow is a platform to programmaticaly author, schedule and monitor data pipelines.
Hangfire - An easy way to perform background processing in .NET and .NET Core applications.
Hadoop - Open-source software for reliable, scalable, distributed computing
delayed_job - Database based asynchronous priority queue system -- Extracted from Shopify - collectiveidea/delayed_job