7 Ways to Execute Scheduled Jobs with Python
A curated list of ways to Integrate scheduled jobs into your Data science python Applications
Job scheduling is a common programming challenge that most organizations and developers at some point must tackle in order to solve critical problems. This is further exacerbated by the proliferation of big data and training models for machine learning.
Having the ability to crunch petabytes of data on a predictable and automated basis in order to derive insights is a key driving factor to set you apart from the competition.
There are various opensource solutions, such as Hadoop and Apache-Spark, and proprietary vendor solutions, such as AWS Glue, used to handle these large data sets. One key component that is required by all these technologies is a “job scheduler” that gives the ability to trigger events in predefined time intervals in a fault-tolerant and scalable way.
Scheduled Jobs are automated pieces of work that can be performed at a specific time or on a recurring basis which predominately work with Unix style expressions called cron. These are time-based event triggers, which enable applications to schedule a work to be performed at a certain date or time based on cron expressions.
