Google Cloud Platform
Big Data Unlimited
Sebastien Agnan, Solution Engineer
sagnan@google.com
Building what’s next 3
Time to Understanding
Typical Big Data
Processing
Programming
Resource
provisioning
Performance
tun...
Building what’s next 4
Time to Understanding
Big Data with Google:
Focus on insight,
not infrastructure.
Programming
StoreCapture Analyze
BigQuery
Big Data at Google
Process
DataflowCloud Storage
DatastoreCloud SQL
Hadoop/SparkKafka
Pub/Su...
• Publish and Subscription Service
• Simple
• Seamlessly Scalable
• Many-to-many or one-to-one
• Guaranteed Durable Delive...
Building what’s next 7
Lorem Ipsum
Lorem Ipsum
Reliable and Flexible Storage
Performant and economical
Blob, Block, Key Va...
Building what’s next 8
Merges batch and stream processing
Data processing pipelines
Monitoring interface
Significantly low...
#gcpug | #googlecloud
Google Cloud Dataflow
Optimize
Schedule
GCS GCS
User Code & SDK Monitoring UI
Life of a Pipeline
Building what’s next 10
Scales automatically
No setup or administration
Stream up to 100,000 rows p/sec
Easily integrates ...
#gcpug | #googlecloud
BigQuery Analytic Service in the Cloud
BigQuery
Analyze ExportImport
How to use BigQuery?
Google
Ana...
Building what’s next 12
A more flexible App Engine
Complete Platform-as-a-Service.
Focus on Code. Not infrastructure.
Deve...
Conclusion
StoreCapture Analyze
BigQuery
Big Data at Google - Easy, Inexpensive, and Fast
Process
DataflowCloud Storage
DatastoreClou...
NEXT PARIS 13 OCtobre
2015
https://goo.gl/wgKqhT
Google confidential │ Do not
distribute
Thank You
#gcpug | #googlecloud
Column Oriented Storage
Record Oriented Storage Column Oriented Storage
Less bandwidth, More compres...
#DataUnlimited - Google Big Data Unlimited
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#DataUnlimited - Google Big Data Unlimited

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A talk on Google Coud Platform and its big data solution by Sebastien Agnan, Google Cloud Platform Engineer, at the meetup Data Unlimited.

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#DataUnlimited - Google Big Data Unlimited

  1. 1. Google Cloud Platform Big Data Unlimited Sebastien Agnan, Solution Engineer sagnan@google.com
  2. 2. Building what’s next 3 Time to Understanding Typical Big Data Processing Programming Resource provisioning Performance tuning Monitoring Reliability Deployment & configuration Handling growing scale Utilization improvements
  3. 3. Building what’s next 4 Time to Understanding Big Data with Google: Focus on insight, not infrastructure. Programming
  4. 4. StoreCapture Analyze BigQuery Big Data at Google Process DataflowCloud Storage DatastoreCloud SQL Hadoop/SparkKafka Pub/Sub Hadoop/Spark
  5. 5. • Publish and Subscription Service • Simple • Seamlessly Scalable • Many-to-many or one-to-one • Guaranteed Durable Delivery • Push or Pull • Secure and Encrypted PUB / SUB : Real-time and reliable messaging
  6. 6. Building what’s next 7 Lorem Ipsum Lorem Ipsum Reliable and Flexible Storage Performant and economical Blob, Block, Key Value, SQL and NoSQL Easy to use : API High performance : 99.95% SLA and 24x7 phone support Strong ecosystem of partners Data location (US,EU or APAC)
  7. 7. Building what’s next 8 Merges batch and stream processing Data processing pipelines Monitoring interface Significantly lower cost Google Cloud Dataflow makes complex data analysis simple
  8. 8. #gcpug | #googlecloud Google Cloud Dataflow Optimize Schedule GCS GCS User Code & SDK Monitoring UI Life of a Pipeline
  9. 9. Building what’s next 10 Scales automatically No setup or administration Stream up to 100,000 rows p/sec Easily integrates with third-party software Google BigQuery makes complex data analysis simple
  10. 10. #gcpug | #googlecloud BigQuery Analytic Service in the Cloud BigQuery Analyze ExportImport How to use BigQuery? Google AnalyticsETL tools Connectors Google Cloud BI tools and Visualization Google Cloud Spreadsheets, R, Hadoop
  11. 11. Building what’s next 12 A more flexible App Engine Complete Platform-as-a-Service. Focus on Code. Not infrastructure. Develop in your favorite language. Deploy with a click.
  12. 12. Conclusion
  13. 13. StoreCapture Analyze BigQuery Big Data at Google - Easy, Inexpensive, and Fast Process DataflowCloud Storage DatastoreCloud SQL Hadoop/SparkKafka Pub/Sub Hadoop/Spark
  14. 14. NEXT PARIS 13 OCtobre 2015 https://goo.gl/wgKqhT
  15. 15. Google confidential │ Do not distribute Thank You
  16. 16. #gcpug | #googlecloud Column Oriented Storage Record Oriented Storage Column Oriented Storage Less bandwidth, More compression

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