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Vidispine

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Vidispine - Managing Digital Assets with Vidispine, a cloud native content management platform for developers and media professionals.

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Vidispine

  1. 1. Vidispine - Cloud Native Content Management for Developers and Media Professionals Erik Åhlin, CEO, co-founder
  2. 2. Vidispine in 15s – the problem we solve Broadcasters and media companies are looking for • Incorporate cloud and cloud services in a pace the budget allows. An evolution rather than a revolution. • Complete, managed and real elasticity for video content to control cost but still being agile • Repurposing content to a much higher degree • Fast turn-around story-telling with video and images on all platforms • Flexibility in business models and technology choices
  3. 3. Vidispine API-based Content Management PaaS Fully featured back-end for any media application Key areas • Metadata • Multi-format management • Performance & Scalability • Cloud native architecture (componentized and distributed) • Support for Multi-application
  4. 4. VidiXplore – The Gravity Point for your content ”Easy to Like” user experience Support for core media management tasks Extensible Component in a ”bigger picture” SaaS (private or public) Leverage Vidispine Content Management PaaS
  5. 5. Combine on-prem & cloud - start your migration 1. Place Vidispine Server Agent (VSA) next to existing on-prem storage 2. Create proxy, analyze metadata, connect to MAM/DAM locally 3. Keep link between ’cloud-side’ asset and ’on-prem’ asset 4. Start execute your cloud strategy by doing what cloud does best 5. Migrate infrastructure in your pace means you can focus on business
  6. 6. VidiXplore – The Gravity Point for your content VIDISPINE PaaS with APIs Transcoder & QC VDA Storage On-Prem Infrastructure Vidispine Server Agent Cloud Services VSAs and/or Biz Destinations
  7. 7. Real World Example #1 Global Premium Content Delivery Spread out workforce and clients Cost efficient and adaptive workflows
  8. 8. VidiXplore at Premium Content Delivery Leader VidiXplore with Custom Panels for managing audio tracks, mezzanine files, delivery points, etc Amazon EC2 Amazon CloudFront Amazon S3 w SSE hosting 8 TB proxies 65 GB thumbnails Amazon VPC Amazon Route 53 On-Prem VSAs connected to local storage Vidispine APIs for integration to workflow engine and more Data & Metadata to Vidispine PaaS Proxies direct to S3 for performance gain Amazon RDS
  9. 9. Migrate and Scale By knowing your content you are ready to scale Spend wisely - let cloud do what cloud do best • Workflows, common user interface for spread-out workforce • Distribute and share ready content • Store/archive and ”second copy” • Burst-out transcoding and compute Integrate and leverage popular cloud services • Dropbox, Slack, Box.com, WeVideo, CloudConvert, Metadata harvesting Migrate, Scale, Adapt also Business Processes, Functions,
  10. 10. Real World Example #2 Partner Company
  11. 11. Alfred – the Dev Ops Butler Partner Company: DSB Using Vidispine dockerized Scaling, Logging, Action Packs AWS Services used • EC2 Container Service • Docker • Node.Js • MongoDB
  12. 12. Real World Example #3 Leading US-based Media Giant Quickly prototyped a complete DAM workflow (Clip Library) Validated the technology stack on very low budget
  13. 13. Prototyping on AWS Marketplace Leading US-based Media Giant 10+ MAMs and DAMs already Some workflows are fit and ready for cloud Overview • Total POC went 6 weeks – total of 4 developers. • Total assets added (5 thousand). • Used the bare minimum box that Vidispine AMI Trial would run on (m3.medium)
  14. 14. Prototyping on AWS Marketplace Next steps • Run Vidispine as HA, where SOLR runs in another EC2 and Postgres runs in RDS • Performance testing with hundreds of thousands of assets Resources used • AWS EC2 (website PoC ran here) • AWS RDS (Postgres DB) • AWS S3 (hot folder in/out) • Vidispine Developer Edition AMI • Bitbucket (PoC code) • Jenkins (for deploying to EC2) • MEAN stack(code stack)
  15. 15. What’s next? Editing • Some very promising prototypes using AWS WorkSpaces Expert systems based on machine learning • Systems adapting to the task at hand rather than how it was originally designed Large-scale metadata harvesting True ubiquitous processing and storing • What is a file, what is a computer? • What to we really want computers to do for us and how? Amazon WorkSpaces Amazon Machine Learning AWS Lambda

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