The AWS Chicago meetup featured talks on various AWS data storage and processing solutions, including DynamoDB, Redshift, and RDS. Key insights included the advantages and use cases of Redshift for data analysis, noting its cost-effectiveness and ease of integration with existing systems. The event also discussed best practices and recommendations for optimizing data workflows in analytics environments.

1. !
AWS Chicago User Group
!
Big Data Day

2. Have an idea for a meetup? Talk
to me:
!
Margaret Walker
CohesiveFT
!
!
Tweet: @MargieWalker
#AWSChicago
Sponsors & Hosts
#AWSChicago

3. 6:00 pm Introductions
6:05 pm Short Talks
!
"AWS Storage Options" Ben Blair, CTO at MarkITx
@stochastic_code
!
"APIs and Big Data in AWS" - Kin Lane,API Evangelist
@kinlane
!
"Democratizing Data Analysis with Amazon Redshift" - Bill
Wanjohi @billwanjohi and Michelangelo D'Agostino
@MichelangeloDA, Civis Analytics
!
6:45 pm Q & A
7:00 pm Networking, drinks and pizza
Agenda
#AWSChicago
Sponsors & Hosts

4. Next Meetups:
October 15?
!
+Nov 12
Let’s drink at re:Invent

5. Keep it Secret,
Keep it Safe
(and Fast and Available would be nice too)

6. Hi
Ben Blair
CTO @ MarkITx
We live on AWS

7. TL;DW
• Use IAM roles for access control
• Use DynamoDB for online storage &
transactions
• Use Redshift for offline storage & analysis
• Use S3 to keep *everything*

8. It’s hard to keep a
secret
Use AIM EC2 roles instead

9. 3rd normal form,
anyone?
Data duplication is OK
Optimize for each context

10. Interactive Data goes
in DynamoDB
If your users read or write it, and it’s not huge, it should
probably go into DynamoDB

11. Why DynamoDB
• Works with tests. Tests are good.
• Predictable Performance & Cost
• Low Maintenance

12. Why Not DynamoDB
• Vendor lock-in vs Cassandra
• Can’t add / change indexes (but that’s ok)
• Need to watch utilization

13. SimpleDB
No, just no

14. ElastiCache
Good place to end, bad place to start

15. RDS
Hosted SQL Goodness

16. Redshift
Seriously wonderful

17. Redshift vs RDS
• Start with RDS
• Redshift is actually very cheap
• RDS for simple reporting on small data sets
• Redshift for all other analysis

18. S3
Store Everything.
!
You won’t, and you’ll regret it later.

19. EBS
Distributed Availability > Instance Recovery

20. Names Matter
Distributed systems care about your keyspace even
when you don’t

21. Thanks
ben@markitx.com
!
@stochastic_code
!
github.com/markitx

22. "APIs and Big Data in AWS"
Kin Lane
API Evangelist
!
@kinlane
!
Click here for slides on GitHub
#AWSChicago
Sponsors & Hosts

23. Democratizing Data
Analysis with Amazon
Redshift
Michelangelo D’Agostino - Civis Analytics Senior Data Scientist
Bill Wanjohi - Civis Analytics Senior Engineer

24. ● advantages of Redshift
● some pitfalls
● workflows and recommendations on best
practices
What you’ll learn

25. Why should you listen?
● 18 months of heavy Redshift use
● Two complementary perspectives:
The Scientist and The Engineer

26. Michelangelo @MichelangeloDA

27. Bill @billwanjohi

28. ● collaborated on monolithic Vertica analytics
database
● dozens of TB of data
● scaled from 4-20 server blades
● dozens of concurrent users across
departments (hundreds total)
● arbitrary SQL allowed/encouraged
Life before Redshift

29. Our early requirements
● SQL language
● low starting cost
● easy to integrate with OSS, other DBs
● performant on large data sets
● minimal database administration

30. Choosing Redshift
● timing: first full release in Feb 2013
● drastically cheaper to start than other
commercial offerings
● very similar to our previous choice, HP
Vertica
● many fewer administration tasks

31. Basics
● RDBMS
● MPP/Columnar
Supports window functions
Few enforceable constraints
No concept of an index
● Redshift <= ParAccel <= PostgreSQL 8
Postgres drivers work
ORM requires mocking
● Most data I/O via S3 service

32. Things analytics DBs are good at
● Big aggregates
● Parallel I/O
● Merge joins between tables

33. Things they’re not good at
● Updates
● Retrieval of individual records
● Enforcing data quality

34. How’s it worked out?
Pretty good!
● adequate performance
○ big step up from traditional RDBMS
○ comparable to other analytics DBs
● easy to stand up new clusters
● cheaper clusters now available
● most workflows can live entirely in-database
● s3 is a good broker for what can’t

35. Data Science Workflow
Our custom plumbing syncs tables from dozens
of source databases into Redshift at varying
refresh frequencies.

36. We’ve found that SQL just invites so many
more people to the analytics game.
Analysts and data scientists run exploratory
SQL and build up complex tables for statistical
modeling一utilizing crazy joins, aggregates and
rollup features.
Redshift supports powerful window functions
Data Science Workflow

37. Predictive Modeling
Data is pulled directly from Redshift into
python/R to train statistical models

38. Predictive Modeling
For simple linear models, scoring is done
directly in redshift via SQL.
For more complicated models, data is pulled
from redshift to s3 with a COPY SQL
command, processed in EMR, and loaded back
into redshift with another COPY command.

39. Hurdles we’ve faced along
the way
● inconsistent runtimes
● catalog contention
● bugs (databases are hard)
● resizing
● too easy to end up with uncompressed data
● “missing” PostgreSQL functionality
● complex workload management

40. Setup Recommendations
● at least two nodes
● send 35-day snapshots to other regions
● at-rest encryption
● enforce SSL
● provision with boto or AWS CLI
● cluster isolation to hide objects
● buy 3-year reservations

41. We’re Hiring!
Through research, experimentation, and iteration, we’re
transforming how organizations do analytics. Our clients
range in scale and focus from local to international, all
empowered by our individual-level, data-driven approach.
civisanalytics.com/apply