1. AWS Summit 2013 Milan
31 Ottobre 2013
DATA ANALYSIS ON AWS
Hakan Gurel
Solutions Architecture

3. THE COST OF
GENERATING DATA
IS FALLING

5. THE MORE DATA YOU COLLECT
THE MORE VALUE YOU CAN
DERIVE FROM IT

10. Lower cost,
higher throughput

GENERATE  STORE  ANALYZE  SHARE
Highly
constrained

11. 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

12. ACCELERATE
GENERATE 
STORE  ANALYZE  SHARE

13. + ELASTIC AND HIGHLY SCALABLE
+ NO UPFRONT CAPITAL EXPENSE
+ PAY FOR ONLY WHAT YOU USE
+ AVAILABLE ON-DEMAND
= REMOVE
CONSTRAINTS

14. AWS Import / Export
AWS Direct Connect
GENERATE  STORE  ANALYZE  SHARE

15. 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

16. Amazon S3,
Amazon Glacier,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
AWS Storage Gateway,
Data on Amazon EC2
GENERATE  STORE  ANALYZE  SHARE

17. AMAZON S3
SIMPLE STORAGE SERVICE

19. AMAZON
DYNAMODB
HIGH-PERFORMANCE, FULLY MANAGED
NoSQL DATABASE SERVICE

20. DURABLE &
AVAILABLE
CONSISTENT, DISK-ONLY
WRITES (SSD)

21. LOW LATENCY
AVERAGE READS < 5MS,
WRITES < 10MS

22. NO ADMINISTRATION

23. 500,000 WRITES PER SECOND
DURING SUPER BOWL

24. AMAZON
REDSHIFT
FULLY MANAGED, PETA-BYTE SCALE
DATAWAREHOUSE ON AWS

26. DESIGN OBJECTIVES:
A petabyte-scale data warehouse service that was…
A Lot Faster
AMAZON
REDSHIFT
A Lot Cheaper
A Whole Lot Simpler

27. 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

30. 30 MINUTES
DOWN TO
12 SECONDS

32. AMAZON REDSHIFT LETS YOU
START SMALL AND GROW BIG
Extra Large Node (HS1.XL)
Eight Extra Large Node (HS1.8XL)
Cluster 2-100 Nodes (32 TB – 1.6 PB)
Single Node (2 TB)
Cluster 2-32 Nodes (4 TB – 64 TB)

33. CREATE A DATAWAREHOUSE IN
MINUTES

38. JDBC/ODBC

42. 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

43. 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

44. USAGE SCENARIOS

45. Cloud ETL for Big Data
S3
EMR
Redshift
Reporting
and BI
• Maintain online SQL access to historical logs
• Transformation and enrichment with EMR
• Longer history ensures better insight

46. Live archive for (structured) Big Data
OLTP
Web Apps
•
•
•
•
DynamoDB
Redshift
Direct integration with copy command
High velocity data
Data ages into Redshift
Low cost, high scale option for new apps
Reporting
and BI

47. Reporting Warehouse
OLTP
ERP
RDBMS
Redshift
• Accelerated operational reporting
• Support for short-time use cases
• Data compression, index redundancy
Reporting
and BI

48. On-Premises Integration
OLTP
ERP
RDBMS
Redshift
+
Reporting
& BI

49. GENERATE  STORE  ANALYZE  SHARE
Amazon EC2
Amazon Elastic
MapReduce

50. AMAZON EC2
ELASTIC COMPUTE CLOUD

52. CLUSTER GPU
QUADRUPLE EXTRA LARGE
Intel Xeon X5570, quad-core
Nehalem architecture
NVIDIA Tesla Fermi
M2050 GPUs
22 GB of memory – 1.7 TB of storage
2x
2x

53. ON A SINGLE INSTANCE
COMPUTE TIME: 4h
COST: 4h x $2.1 = $8.4

54. ON MULTIPLE INSTANCES
COMPUTE TIME: 1h
COST: 1h x 4 x $2.1 = $8.4

56. For 3 hours
$4828.85/hr
instead of
$20+ MILLIONS
in infrastructure

57. AMAZON ELASTIC
MAPREDUCE
HADOOP AS A SERVICE

58. •
•
•
•
A FRAMEWORK
SPLITS DATA INTO PIECES
LETS PROCESSING OCCUR
GATHERS THE RESULTS

59. Corporate Data
Center
Application data
and logs for
analysis pushed
to S3
Elastic Data
Center

60. Amazon Elastic
Map Reduce
name node to
control analysis
N
Corporate Data
Center
Elastic Data
Center

61. N
Corporate Data
Center
Hadoop cluster
started by Elastic
Map Reduce
Elastic Data
Center

62. N
Corporate Data
Center
Adding many
hundreds or
thousands of
nodes
Elastic Data
Center

63. Disposed of when
job completes
N
Corporate Data
Center
Elastic Data
Center

64. Corporate Data
Center
Results of
analysis pulled
back into your
systems
Elastic Data
Center

66. Amazon S3,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
Data on Amazon EC2
GENERATE  STORE  ANALYZE  SHARE

68. GENERATE  STORE  ANALYZE  SHARE
AWS Data Pipeline

69. AWS Data Pipeline
Data-intensive orchestration and automation
Reliable and scheduled
Easy to use, drag and drop
Execution and retry logic
Map data dependencies
Create and manage compute resources

73. AWS Import / Export
AWS Direct Connect
Amazon S3,
Amazon Glacier,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
AWS Storage Gateway,
Data on Amazon EC2
Amazon S3,
Amazon DynamoDB,
Amazon RDS,
Amazon Redshift,
Data on Amazon EC2
GENERATE  STORE  ANALYZE  SHARE
Amazon EC2
Amazon Elastic
MapReduce
AWS Data Pipeline

74. FROM DATA TO
ACTIONABLE
INFORMATION

76. Stefano Rodighiero

77. MXM FACTS
7+ million lyrics catalogue in more than 50
distinct languages
Currently musiXmatch is the only lyrics
platform allowed for worldwide licensing and
has deals with top Music Publishers: Warner
Chappell, Universal, BMG, EMI Publishing,
Sony ATV, Peer Music, ...
Daily updated with more than 1 million artists
and more than 20 million music tracks
Synced lyrics!
Music Discography Meta Data: Lyrics, Artists,
Albums, Songs, Biographies, Worldwide
Charts
Words matter

78. SYNCED LYRICS

79. OUR DATA
MUSIC
METADATA:
RECORDING &
PUBLISHING

80. OUR DATA
CONTENT USAGE

81. OUR DATA
OTHER SOURCES

82. DATA ANALYSIS @ MXM
CONTENT
USAGE:
REPORTING &
ANALYTICS

83. DATAFLOW
Frontend
Filter/norma
lization
Redis
(real time
analytics)
"Unrolling"
Redshift
Hive
Analytics
Post
process
Publishing
catalogue
Batch
Words matter

84. BATCH REPORTING
Step 1. Aggregation of views by country,
application and content type
Step 2. Join with a 500M+ rows table
Hive
It takes approx 1 hour with 5 c1.xlarge
instances
It used to take days with traditional techniques!
Post
process
Batch
SQL interface makes it easier to review and
share the process
Words matter

85. DATAFLOW
Frontend
proxy
Filter/norm
alization
Redis
(real time
analytics)
"Unrolling"
Redshift
Hive
Analytics
Publishing
catalogue
Post
process
Interactive
Words matter

86. INTERACTIVE ANALYTICS
SQL interface like Hive, accessible with any
Postgresql client...
Redis
(real time
analytics)
Redshift
...but faster!
Flexible costs
Analytics
With Redshift doing all the heavy lifting, it's
easier to build analytics tools
Interactive
Words matter

87. DATAFLOW
Frontend
proxy
Filter/normali
zation
Redis
(real time
analytics)
"Unrolling"
Redshift
Hive
Analytics
Post
process
Publishing
catalogue
Interactive
Batch
Words matter

88. MUSIXMATCH
Stefano Rodighiero
stefano@musixmatch.com
@larsen
Words matter

89. MUSIXMATCH
THANK YOU!

91. THANK YOU
hakan@amazon.lu