AWS Summit Barcelona - Data Analysis on AWS

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AWS Summit Barcelona - Data Analysis on AWS

  1. 1. AWS Summit 2013 Barcelona Oct 24 – Barcelona, Spain DATA ANALYSIS ON AWS Carlos Conde Sr. Mgr. Solutions Architecture
  2. 2. GENERATE  STORE  ANALYZE  SHARE
  3. 3. THE COST OF DATA GENERATION IS FALLING
  4. 4. THE MORE DATA YOU COLLECT THE MORE VALUE YOU CAN DERIVE FROM IT
  5. 5. Lower cost, higher throughput GENERATE  STORE  ANALYZE  SHARE
  6. 6. Lower cost, higher throughput  GENERATE  STORE  ANALYZE  SHARE Highly constrained
  7. 7. DATA VOLUME Generated data Available for analysis Gartner: User Survey Analysis: Key Trends Shaping the Future of Data Center Infrastructure Through 2011 IDC: Worldwide Business Analytics Software 2012–2016 Forecast and 2011 Vendor Shares
  8. 8. GENERATE STORE  ANALYZE  SHARE
  9. 9. ACCELERATE GENERATE  STORE  ANALYZE  SHARE
  10. 10. + ELASTIC AND HIGHLY SCALABLE + NO UPFRONT CAPITAL EXPENSE + ONLY PAY FOR WHAT YOU USE + AVAILABLE ON-DEMAND = REMOVE CONSTRAINTS
  11. 11. GENERATE  STORE  ANALYZE  SHARE
  12. 12. AWS Import / Export AWS Direct Connect GENERATE  STORE  ANALYZE  SHARE
  13. 13. Generated and stored in AWS Inbound data transfer is free Multipart upload to S3 Physical media AWS Direct Connect Regional replication of AMIs and snapshots
  14. 14. Amazon S3, Amazon Glacier, Amazon DynamoDB, Amazon RDS, Amazon Redshift, AWS Storage Gateway, Data on Amazon EC2 GENERATE  STORE  ANALYZE  SHARE
  15. 15. AMAZON S3 SIMPLE STORAGE SERVICE
  16. 16. AMAZON DYNAMODB HIGH-PERFORMANCE, FULLY MANAGED NoSQL DATABASE SERVICE
  17. 17. DURABLE & AVAILABLE CONSISTENT, DISK-ONLY WRITES (SSD)
  18. 18. LOW LATENCY AVERAGE READS < 5MS, WRITES < 10MS
  19. 19. NO ADMINISTRATION
  20. 20. 500,000 WRITES PER SECOND DURING SUPER BOWL
  21. 21. AMAZON REDSHIFT FULLY MANAGED, PETA-BYTE SCALE DATAWAREHOUSE ON AWS
  22. 22. DESIGN OBJECTIVES: A petabyte-scale data warehouse service that was… A Lot Faster AMAZON REDSHIFT A Lot Cheaper A Whole Lot Simpler
  23. 23. AMAZON REDSHIFT RUNS ON OPTIMIZED HARDWARE HS1.8XL: 128 GB RAM, 16 Cores, 16 TB compressed user storage, 2 GB/sec scan rate HS1.XL: 16 GB RAM, 2 Cores, 2 TB compressed customer storage
  24. 24. 30 MINUTES DOWN TO 12 SECONDS
  25. 25. AMAZON REDSHIFT LETS YOU START SMALL AND GROW BIG Extra Large Node (HS1.XL) Single Node (2 TB) Cluster 2-32 Nodes (4 TB – 64 TB) Eight Extra Large Node (HS1.8XL) Cluster 2-100 Nodes (32 TB – 1.6 PB)
  26. 26. CREATE A DATAWAREHOUSE IN MINUTES
  27. 27. JDBC/ODBC
  28. 28. Price Per Hour for HS1.XL Single Node Effective Hourly Price Per TB Effective Annual Price per TB On-Demand $ 0.850 $ 0.425 $ 3,723 1 Year Reservation $ 0.500 $ 0.250 $ 2,190 3 Year Reservation $ 0.228 $ 0.114 $ 999
  29. 29. DATA WAREHOUSING DONE THE AWS WAY Easy to provision and scale up massively No upfront costs, pay as you go Really fast performance at a really low price Open and flexible with support for popular tools
  30. 30. USAGE SCENARIOS
  31. 31. S3 EMR Redshift Reporting and BI
  32. 32. OLTP Web Apps DynamoDB Redshift Reporting and BI
  33. 33. OLTP ERP RDBMS Redshift Reporting & BI
  34. 34. OLTP ERP RDBMS Redshift + Reporting & BI
  35. 35. Social Point Analytics in AWS Marc Canaleta (CTO) @mcanaleta AWS Summit Barcelona 2013
  36. 36. Social Games developer para Mobile y Facebook Fundada en 2008, oficinas en Barcelona (22@), 170 personas. Top #20 mobile grossing games worldwide Top #3 facebook developer
  37. 37.  Juegos Sociales: interacción entre amigos, viralidad  Modelo freemium: Jugar es gratis, algunos items de pago  Sector Midcore  Leader in Breeding & Collecting strategy games
  38. 38.  Top 20 Grossing en iOS App Store worldwide  Lanzado recientemente en Android, featured en Google Play  6M DAU en Facebook
  39. 39.  No mantener ni planificar hardware: aumenta la velocidad del negocio  Flexible: Pago por uso  Facilita la escalabilidad: Auto Scaling  Facilita la alta disponibilidad: múltiples availability zones  Managed components: Load Balancers, Bases de datos, …
  40. 40. Analytics Driven. Necesarias para casi todos nuestros equipos:  Ingenieros: analíticas realtime, monitorización, detección de problemas  Producto: tomar decisiones, A/B testing, game balancing, …  Marketing: optimización de campañas  Finanzas: seguimiento del negocio
  41. 41. FLASH CLIENT IOS CLIENT ANDROID CLIENT BACKEND SERVERS BACKEND SERVERS BACKEND SERVERS Symfony 2 ANALYTICS QUEUES ANALYTICS QUEUES ANALYTICS QUEUES Redis LOGFILES STORAGE ANALYTICS DATABASE AWS S3 AWS Redshift
  42. 42.  Backend escribe eventos en listas de redis  Porque Redis?  Coste y rendimiento: 10K eventos/segundo/servidor  Problema: es una base de datos en memoria, hay que vaciar las colas constantemente  Escalado y HA: N servidores distribuidos aleatoriamente BACKEND REDIS REDIS REDIS
  43. 43.  Procesos python consumen las colas constantemente y  Calculan métricas Real Time  Almacenan logfiles de eventos para subirlos a S3 GENERACIÓN DE EVENTOS Redis Queue LPOP event Consumer Redis Real Time write event Event Log File  Encolan en SQS la URL del objeto S3 INCR counter put object Amazon S3 CARGA DE DATOS Amazon SQS enqueue S3 object URL
  44. 44. GENERACIÓN DE EVENTOS  Python es muy adecuado para desarrollar workers y tratar datos  Redis: estructuras como contadores, sets, sorted sets, para métricas Real Time  S3: espacio virtualmente infinito, escalable, alta disponibilidad  SQS fiabilidad y disponibilidad a mayor precio que Redis Redis Queue LPOP event Consumer INCR counter Redis Real Time write event Event Log File put object Amazon S3 CARGA DE DATOS Amazon SQS enqueue S3 object URL
  45. 45. PROCESADO DE EVENTOS  Los importers leen URLs de SQS Amazon S3 Amazon SQS  Se descargan logfiles de S3  Convierten a TSV  Importan masivamente a Redshift (N logfiles a la vez) Importer TSV RedShift
  46. 46.  Nos permite ser flexibles -> cambios de esquema sin downtime  Muy escalable (con downtime de escrituras)  Poco riesgo de implantación  Sistema offline  Backups  Mantenimiento mínimo: vacuums, espacio  Buen soporte de SQL, a diferencia de otras columnar databases
  47. 47.  Transformaciones y cálculos diarios implementados en SQL Ejemplo: UPDATE USER SET total_revenues = (SELECT SUM(amount) FROM transaction t WHERE t.user_id = user.user_id);  Por qué no hadoop?  Mucho más complejo y lento; de momento las operaciones SQL cumplen todos nuestros requisitos
  48. 48. ¿Te gustaría trabajar en el sector de los videojuegos? Buscamos talento. El talento atrae al talento. www.socialpoint.es/jobs ¡GRACIAS! 
  49. 49. GENERATE  STORE  ANALYZE  SHARE Amazon EC2 Amazon Elastic MapReduce
  50. 50. AMAZON ELASTIC MAPREDUCE HADOOP AS A SERVICE
  51. 51. • • • • A FRAMEWORK SPLITS DATA INTO PIECES LETS PROCESSING OCCUR GATHERS THE RESULTS
  52. 52. Corporate Data Center Elastic Data Center
  53. 53. Corporate Data Center Application data and logs for analysis pushed to S3 Elastic Data Center
  54. 54. Amazon Elastic Map Reduce name node to control analysis N Corporate Data Center Elastic Data Center
  55. 55. N Corporate Data Center Hadoop cluster started by Elastic Map Reduce Elastic Data Center
  56. 56. N Corporate Data Center Adding many hundreds or thousands of nodes Elastic Data Center
  57. 57. Disposed of when job completes N Corporate Data Center Elastic Data Center
  58. 58. Corporate Data Center Results of analysis pulled back into your systems Elastic Data Center
  59. 59. Amazon S3, Amazon DynamoDB, Amazon RDS, Amazon Redshift, Data on Amazon EC2 GENERATE  STORE  ANALYZE  SHARE
  60. 60. PUBLIC DATA SETS http://aws.amazon.com/publicdatasets
  61. 61. GENERATE  STORE  ANALYZE  SHARE
  62. 62. GENERATE  STORE  ANALYZE  SHARE
  63. 63. FROM DATA TO ACTIONABLE INFORMATION

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