3. 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
4. Building what’s next 4
Time to Understanding
Big Data with Google:
Focus on insight,
not infrastructure.
Programming
6. • 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
7. Building what’s next 7
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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)
8. 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
9. #gcpug | #googlecloud
Google Cloud Dataflow
Optimize
Schedule
GCS GCS
User Code & SDK Monitoring UI
Life of a Pipeline
10. 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
11. #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
12. 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.
14. StoreCapture Analyze
BigQuery
Big Data at Google - Easy, Inexpensive, and Fast
Process
DataflowCloud Storage
DatastoreCloud SQL
Hadoop/SparkKafka
Pub/Sub
Hadoop/Spark