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Keynote (Cloud Developer Roadshow 2014)

Keynote (Cloud Developer Roadshow 2014)

Keynote session for the Google Cloud Platform Developer Roadshow events from July-August 2014


August 20, 2014

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  1. Agenda Keynote Blurring the IaaS/PaaS Divide From Data to Meaning

    Containerizing the Cloud How to Design, Build, and Run a Cloud App Fireside Chat
  2. of CEOs see technology change as the #1 external force

    that could most impact their organization over the next 3-5 years Transformation: Business, Technology & Culture 71%
  3. enabling a new world via mobile global connections at any

    moment connections virtually everywhere Any Place Any Device Adoption Speed Any Team Any Time Big Trends vitally important to stay ahead consumer leads, business follows minds of many need to collaborate
  4. Decreasing cost enables virtually limitless storage in the cloud. $600

    can buy enough storage for the world’s music. (Source: McKinsey Global Institute May 2011) Computing as a utility is now available for easy purchase, provided from massively efficient data centers. (Source: Nicholas Carr, The Big Switch, 2008) The internet allows for a model of real-time access to new innovation, information, and applications from a wide range of devices. Affordable capacity On-demand computing Instant access IT Trends
  5. 75 years 1957 2003 2013 500 25 years 10 years

    (average age of a company joining the S&P 500)
  6. For the past 15 years, Google has been building out

    the world’s fastest, most powerful, highest quality cloud infrastructure on the planet.
  7. Google has been running some of the world’s largest distributed

    systems with unique and stringent requirements.
  8. Innovating Software & Driving Technology Forward Spanner Dremel MapReduce Bigtable

    Colossus 2012 2013 2002 2004 2006 2008 2010 GFS Compute Engine
  9. Google Cloud Platform Storage Cloud Storage Cloud SQL Cloud Datastore

    Compute Compute Engine App Engine App Services BigQuery Cloud Endpoints
  10. Product Momentum August 2013 Encryption at Rest for Cloud Storage

    Layer 3 Load Balancing in Compute Engine June 2014 Docker support HTTPS Load Balancing SSD Persistent Disk November 2013 Cloud Endpoints GA Dedicated Memcache GA December 2014 Compute Engine GA Persistent Disk March 2014 AppEngine with Managed VMs Windows Server, SuSE, RHEL support BigQuery streaming @100K RPS Major price drops February 2014 Cloud SQL GA HIPAA Support
  11. Economies of scale from sharing infrastructure with other developers and

    reduction of “fragmentation”. Infrastructure changes too rapidly to be locked into physical platforms - you could miss the next competitive advantage. Every second spent on infrastructure and operations is time not spent on your applications, your customers, or your business. Always Lower Cost Flexibility + Adaptability Why Are Developers Moving to Cloud Lets You Focus on Customers
  12. $0 100 servers 1,000 servers 10,000 servers 100,000 servers $8,000

    $6,000 $4,000 $2,000 Public Cloud Private Cloud 10x cost benefit for large scale agencies Cloud Economics
  13. Computing Patterns • On & off workloads (e.g. batch job)

    • Over provisioned capacity is wasted • Successful services needs to scale • Difficult to provision hardware • Unexpected/unplanned peak in demand • Sudden spike impacts performance • Can’t over-provision for extreme cases Growth Bursting On and Off
  14. Prices are falling • Public cloud prices have dropped 6-8%

    annually Source: Google Internal Data 2014 2006 Public Cloud Prices
  15. But prices are not falling fast enough • Hardware costs

    have dropped 20-30% annually Hardware Cost Public Cloud Prices • Public cloud prices have dropped 6-8% annually Source: Google Internal Data 2014 2006
  16. 100% 0% 20% 40% 60% 80% Sustained Use Previous On

    Demand New On Demand $0.11 $0.10 $0.09 $0.08 $0.07 $0.06 $0.05 $0.04 $0.03 Sustained-use discounts Net Price Per Hour
  17. Cloud is still too hard Developers often make trade offs

    to work around the weaknesses and limitations of today’s public clouds
  18. Cloud is still too hard Big Data or Real Time

    Time to Market or Scalability Flexibility or Automatic Management
  19. Cloud is still too hard Big Data or Real time

    Time to Market or Scalability Flexibility or Automatic Management We are changing or to and
  20. Developer Productivity • Use the tools you know and love

    • • Fast, reliable deployments • • Isolate and fix issues in production Developer Productivity Time to Market Scale and
  21. Developer Productivity • Single interface for monitoring all of your

    cloud resources • Rich dashboards and alerting capabilities • Find and fix performance problems quickly Cloud Monitoring Cloud Monitoring Powered By StackDriver
  22. Developer Productivity • Debug Production Applications without Stopping the process

    • Inspect Stack, locals, parameters • Safe for production: No user noticeable effects Cloud Debugger
  23. Developer Productivity • Visualize time spent in your application •

    Quickly identify performance bottlenecks • Compare performance from release to release Cloud Trace
  24. Manage your infrastructure Flexibility Agility Google Compute Engine Your Code

    Compute as a Spectrum Replica Pools Provisioning and health checking Managed VMs OS management, deployments Your Code Your Code App Engine Managed Runtimes Manage your serving stack Your Code
  25. Managed VMs • Flexibility of Compute Engine with productivity of

    App Engine • Provides best of both worlds Flexibility Management and
  26. • Package applications Independent of the VM layer • Predictability

    • Quality of service • Efficient overcommit • Resource accounting At Google, we have been doing this for many years... Images by Connie Zhou Containers and Kubernetes
  27. Networking • Projects are isolated private networks • Networks can

    be global • Addresses • public and private: free while in use • Routes, gateways, VPNs, and IP Forwarding • Google has a massive backbone with best in class throughput and performance • This makes GCP the prime-move for latency and throughput sensitive information -- ie content and data
  28. Complex technical infrastructure to support distributed computing Requires specialized expertise

    Big Data is Hard Big Data is Expensive Time consuming Big Data remains inaccessible Storage costs scale with larger datasets Computing resources must be provisioned for peak-loads Personnel are expensive
  29. No complex data architecture required Use the technical and product

    skillsets you already have Big Data is Hard Big Data is Expensive Google is making Big Data accessible Pay on-demand for only the resources you use Take advantage of falling prices & Moore’s Law Reduce infrastructure management burden Easy Affordable Query within seconds and get real-time results
  30. Store Capture Analyze We help you manage the entire lifecycle

    of Big Data BigQuery Dataflow Open Source Tools Pub/Sub Process Dataflow Storage Datastore SQL
  31. Streaming+Batch+Graph • Near real-time analysis • High fidelity, low latency

    • Focus on results, not sharding and transforming Streaming: Real-Time Data Graph: Variable Analysis Batch: Volumes of Data
  32. 1 2 3 Summary Harness the power and flexibility of

    Google Big innovations are coming of age Cloud is the real deal