From Analytics to Intelligence: Amazon Redshift

An introduction to Amazon Redshift, presented at Microstrategy World, 2013.

1. to from ANALYTICS INTELLIGENCE a presentation at MICROSTRATEGY WORLD 2013 by DR MATT WOOD
2. Hello.
3. Thank you.
4. I Data, data everywhere
5. I II Collection & storage Data, data everywhere
6. I II III Data security Data, data everywhere Collection & storage
7. I II III IV Data movement Data, data everywhere Data security Collection & storage
8. I II III IV Data, data everywhere Data movement Data security Collection & storage 0. Amazon web Services
9. Building blocks.
10. Compute, storage & databases.
11. Retail Merchant services Web services
12. Blinding flash of the obvious.
13. Available.
14. Low cost.
15. Flexible.
16. Every day, AWS adds enough server capacity to power amazon.com in 2003, when it was a $5B enterprise
17. Data, data everywhere I
18. Data for competitive advantage.
19. Customer segmentation, financial modeling, system analysis, line of sight, business intelligence...
20. Generation Collection & storage Analytics & computation Collaboration & sharing
21. Cost of data generation is falling.
22. Kindle Fire HD, Kindle Fire, Kindle Paperwhite and Kindle hold the top four spots on the Amazon world wide best seller chart since launch. devices
23. Amazon Appstore selection tripled in 2012. apps and games
24. Amazon customers purchased more than one toy per second on mobile devices. commerce
25. most gifted kindle book
26. Generation Collection & storage Analytics & computation Collaboration & sharing lower cost, increased throughput
27. Generation Collection & storage Analytics & computation Collaboration & sharing highly constrained
28. Gap.
29. 1990 2000 2010 2020 The Data Analysis Gap Enterprise Data Data in Warehouse 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 Generated data Available for analysis Data volume
30. Enter AWS.
31. Utility.
32. Remove constraints.
33. Generation Collection & storage Analytics & computation Collaboration & sharing highly constrained
34. Generation Collection & storage Analytics & computation Collaboration & sharing
35. Full value.
36. Close the gap.
37. Reduced time to market.
38. Identify and meet new business opportunities.
39. Lower costs.
40. Collection & Storage II
41. One schema to rule them all.
42. One schema to rule them all.
43. Lots of data. Lots of users. Lots of uses. Lots of locations.
44. Cost.
45. Multipliers.
46. Object storage.
47. 99.999999999% durability
48. Relational databases.
49. NoSQL data stores.
50. HDFS based stores.
51. Undi erentiated heavy lifting.
52. Lower costs. Ease of use.
53. Lower costs. Ease of use. Lower costs. no capital investment pay as you go no subscriptions only pay for what you use
54. Lower costs. Ease of use. Ease of use. programmable zero admin easy to configure integrate with existing tools
55. Data warehousing.
56. Expensive. Complicated.
57. Enterprises average between 3 and 4 DBAs per data warehouse. Source: Gartner. Critical factors in calculating the data warehouse TCO, July 2009
58. Source: Oracle technology global price list 11/1/2012
59. Expensive. Complicated.
60. Unobtainable.
61. Amazon Redshift.
62. Fast. Powerful. Petabyte scale.
63. Managed service.
64. Automated deployment & configuration.
65. SQL access and BI tool integration.
66. Parallel execution.
67. Leader Node
68. Compute Node Compute Node Compute Node Leader Node
69. Compute Node Compute Node Compute Node Leader Node
70. 10gigE full bisection network.
71. Compute Node Compute Node Compute Node Leader Node
72. Compute Node Compute Node Compute Node Leader Node Common BI Tools JDBC/ODBC
73. Certified for use with Microstrategy.
74. Data compression.
75. Automated backup to S3.
76. Data encrypted in transit & at rest.
77. Automated failover.
78. Compute Node Compute Node Compute Node Leader Node Common BI Tools JDBC/ODBC
79. Compute Node Compute Node Compute Node Leader Node Common BI Tools JDBC/ODBC
80. Compute Node Compute Node Compute Node Leader Node Common BI Tools JDBC/ODBC
81. Elastic.
82. Compute Node Compute Node Compute Node Leader Node Common BI Tools JDBC/ODBC
83. Compute Node Compute Node Compute Node Leader Node Common BI Tools JDBC/ODBC Compute Node Compute Node
84. Compute Node Compute Node Compute Node Leader Node Common BI Tools JDBC/ODBC
85. Data warehouse node types.
86. 15GB RAM 2TB local attached storage 3 drives 2 virtual cores High Storage Extra Large (XL)
87. High Storage Extra Large (XL) 15GB RAM 2TB local attached storage 3 drives 2 virtual cores 8 High Storage Extra Large (8XL) 120GB RAM 16TB local attached storage 24 drives 16 virtual cores
88. Pay as you go.
89. 2 TB nodes 16 TB nodes On-demand $0.850 $6.80 1 Year Reservation $0.50 $4.00 3 Year Reservation $0.228 $1.824 Hourly Prices
90. 2 TB nodes 16 TB nodes On-demand $0.850 $6.80 1 Year Reservation $0.50 $4.00 3 Year Reservation $0.228 $1.824 Hourly Prices
91. $999 per TB
92. Don’t pay for the leader node.
93. No additional storage charge for backups of active clusters.
94. VPC ready.
95. Low cost. Easy to use.
96. Focus on analysis.
97. Private beta today.
98. Available early this year.
99. aws.amazon.com/redshift
100. 2 billion row dataset. 6 representative queries.
101. Compared to 32 nodes. 128 CPUs. 4.2 TB RAM. 1.6 PB storage. 2 billion row data set. Amazon Redshift: 2 instance cluster 12x to 150x faster
102. 29 minutes 58 seconds down to 12 seconds
103. Data security. III
104. Security is our number one priority.
105. Shared responsibility.
107. Choose your region.
108. Availability zones.
109. ITAR FIPS 140-2 MPAA ISO 27001 SOC 2 ISAE 3402 PCI DSS HIPAA FISMA Moderate
111. “You basically turn yourself into a polymorphic surface to which the attack guy has a much tougher time getting at. That, ultimately, is the real key advantage to drive security and make things much better for us across the board.” Gus Hunt, CTO Central Intelligence Agency
112. Virtual Private Cloud.
113. Network isolated environment.
114. Public and private subnets.
115. Redshift, relational databases, Hadoop can run inside the VPC.
116. Extend your VPN.
117. Identity and access federation.
118. Identity and access management.
119. Data movement. IV
120. “How do I get my data into the cloud?”
121. Generated and stored in the AWS cloud.
122. Inbound transfer if free.
123. Multipart upload.
124. Aspera, IRODS.
125. Physical media.
126. AWS Direct Connect.
127. 1Gbps or 10Gbps
128. Built in AZ replication.
129. Regional replication.
130. “How do I integrate my data?”
131. Amazon DynamoDB HDFS (Amazon EMR) Amazon S3 Amazon Redshift On Premise Amazon RDS
132. AWS Data Pipeline
133. Data-intensive orchestration & automation.
134. Reliable, scheduled data movement and analytics.
135. aws.amazon.com/datapipeline
136. aws.amazon.com
137. I Data, data everywhere
138. I II Collection & storage Data, data everywhere
139. I II III Data security Data, data everywhere Collection & storage
140. I II III IV Data movement Data, data everywhere Data security Collection & storage
141. Thank you.
142. to from ANALYTICS INTELLIGENCE get in touch MATTHEW@AMAZON.COM or @MZA AWS.AMAZON.COM