Large-scale processing  using Django Mashing clouds, queues & workflows PyWeb-IL 8 th  meeting Udi h Bauman (@dibau_naum_h) Tikal Knowledge (http://tikalk.com)
Agenda Web apps vs. Back-end Services
Addressing Scalability
Experience with Django
Use-case 1: automated data integration service
Use-case 2: social media analysis service
Recommendations
Links
Web apps vs. Back-end Services Common conception is that a Web framework  is just for Web sites
Web back-ends become thinner - just services
Applications become service providers, usually over HTTP
All reasons for using Django for almost any back-end offering services
Web apps vs. Back-end Services How are back-end services different? Usually have behaviors not triggered by client requests
Usually involve long processing
May involve continuous communications, & not just request-response
Reliability & high-availability are usually more important with non-human users
Lots of communication with other back-ends
Addressing the needs  of back-end services Message Queues abstract invocation & enable reliable distributed processing
Workflow Engines manage long processing
Continuous communication (e.g., TCP-based) is possible, can be abstracted with XMPP
Clouds & auto-scaling enable high-availability
Can use SOAP/REST for protocols against other back-ends
Experience with Django No matter how heavy & large the task & load were – it just worked.
Even when processing took days to complete, Django was 100% robust
Had no issues with  Performance

Large Scale Processing with Django