Many companies recognize the use of data analytics as an opportunity to better understand their customers and gain a lead on their competition. The ability to get better insight from vast amounts of unstructured data, coming from a multitude of sources, can give businesses the advantage in an industry where even the smallest improvement can mean a big difference. Amazon Web Services offers a range of big data, analytics and storage solutions that are used by companies such as NASDAQ, Bankinter and S&P Capital to deliver a highly secure and agile platform. Join this session and learn how it allows customers to start on a small scale but grow as their business requires, giving them the agility they need to deliver cutting edge solutions to their customers without any upfront CAPEX investment.

1. Big Data Analytics
David de Santiago
Business Development Manager, Analytics EMEA

2. Overview
1. Introducing Big Data
2. From data to actionable information
3. Analytics and Cloud Computing

3. 1
Introducing Big Data

4. Generation
Collection & storage
Analytics & computation
Collaboration & sharing

5. The cost of data generation
is falling

6. Lower cost,
higher throughput
Generation
Collection & storage
Analytics & computation
Collaboration & sharing

7. Lower cost,
higher throughput
Generation
Collection & storage
Analytics & computation
Collaboration & sharing
Highly
constrained

8. 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

9. Elastic and highly scalable
+
No upfront capital expense
+
Only pay for what you use
+
Available on-demand
=
Remove
constraints

10. Lower cost,
higher throughput
Generation
Collection & storage
Analytics & computation
Collaboration & sharing
Highly
constrained

11. Generation
Collection & storage
Accelerated
Analytics & computation
Collaboration & sharing

12. Big Data
Technologies and techniques for
working productively with data,
at any scale.

13. 2
From data to
actionable information

15. Per day:
3.5 billion records
13 TB of click stream logs
71 million unique cookies

16. User bought
recently a home
theatre system
Targeted Ad
And is now
looking at sport
games

17. Results:
500% return on ad spend
17,000% reduction in procurement time
“We couldn’t have done it”

19. Finding signal in the noise of logs
Identified early mobile usage
Invested heavily in mobile development

20. In January 2013
9,432,061 unique mobile devices
used the Yelp mobile app.
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21. Open web index.
3.4 billion records.
Available to all.

22. Tweeting about Flu
You Are What You Tweet: Analyzing Twitter for Public Health. M. J. Paul and M. Dredze, 2011

23. Full parse for impact of
social networks
300 lines of Ruby code.
14 hours.
$100.

24. 3
Analytics and
Cloud Computing

25. Generation
Collection & storage
Analytics & computation
Collaboration & sharing

26. Generation
Collection & storage
Analytics & computation
Collaboration & sharing
S3, Glacier,
Storage Gateway,
DynamoDB,
Redshift, RDS,
HBase

27. Generation
Collection & storage
Analytics & computation
Collaboration & sharing
EC2 &
Elastic MapReduce

28. Generation
Collection & storage
Analytics & computation
Collaboration & sharing
EC2 & S3,
CloudFormation,
Elastic MapReduce,
RDS, DynamoDB, Redshift

29. Amazon Redshift
Fully Managed Data Warehouse
Scales to 1.6PB
Faster, Simpler, Cheaper

30. Amazon Redshift
Effective
Hourly Price
Per TB
Effective
Annual Price
per TB
On-Demand
$ 0.425
$ 3,723
1 Year Reservation
$ 0.250
$ 2,190
3 Year Reservation
$ 0.114
$
999

31. “TOWARDS THE END OF
LAST YEAR OUR DATA
VOLUMES LITERALLY
BROKE THE EXISTING
DATABASE. WE WERE NO
LONG ABLE TO SCALE THE
DATABASE OR DO ANYTHING
USEFUL; LIKE RUNNING
QUERIES”
“Two months to migrate to Amazon Redshift.”
Greg Johnson, Head of Analytics, Nokia

32. Elastic Map Reduce: How does it work?
1. Put the data
into S3 (or HDFS)
S3
EMR Cluster
EMR
3. Get the
results
2. Launch your cluster.
Choose:
• Hadoop distribution
• How many nodes
• Node type (hi-CPU,
hi-memory, etc.)
• Hadoop apps (Hive,
Pig, HBase)

33. Elastic Map Reduce: How does it work?
EMR Cluster
S3
EMR
You can
easily resize
the cluster

34. Elastic Map Reduce: How does it work?
EMR Cluster
S3
EMR
Use Spot
nodes to
save time
and money

35. Elastic Map Reduce: How does it work?
EMR Cluster
S3
EMR
Launch parallel clusters
against the same data
source (tune for the
workload)

36. Elastic Map Reduce: How does it work?
S3
EMR Cluster
When the work is complete,
you can terminate the cluster
(and stop paying)

37. Thousands of Customers, 5+ Million Clusters

38. Give it a try.
Cost to run a 100-node EMR cluster:
£4.90 / hour

39. Generation
Collection & storage
AWS Data Pipeline
Analytics & computation
Collaboration & sharing
S3, Glacier,
Storage Gateway,
DynamoDB,
Redshift, RDS,
HBase
EC2 &
Elastic MapReduce
EC2 & S3,
CloudFormation,
Elastic MapReduce,
RDS, DynamoDB, Redshift

40. 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 temporary compute
resources

41. Anatomy of a pipeline

42. Arbitrarily complex pipelines

43. Thanks.
daviddes@amazon.co.uk
To Learn More:
aws.amazon.com/elasticmapreduce
aws.amazon.com/datapipeline
aws.amazon.com/big-data
aws.amazon.com/redshift
aws.amazon.com/rds

44. Thank you!