The challenges of every day life as the CTO of ClickMeter. Crafting and managing a "big data" ready infrastructure is no easy task, but it can be done step-by-step also by startups. The cloud is a cool starting ground which provides you with many of the toys you'll need, so you can focus on what part of "big data" provides you with the most value.

1. Ingesting click data for analytics
Francesco Furiani, CTO @

2. $ whoami
Francesco Furiani (@ilfurio):
 Backend Engineer
 Roamed these halls not too long ago
Ingesting clicks data for analytics
Loves:
 Studying new CS stuff
 PlayStation / Bike / Traveling / Soccer
 O RLY? books
How do I make a living:
 CTO @ ClickMeter
 Backend Engineer @ ClickMeter
 Enum.take_random(IT_ROLES,1) @ ClickMeter

3. Ingesting clicks data for analytics
ClickMeter

4.  100k+ customers
 Getting events for customers from 10 to 3000 req/sec
Ingesting clicks data for analytics
ClickMeter

5. We receive data anytime someone:
 Clicks our links
 Views our pixels
 Calls our postbacks
Our customers use us:
 Inside a famous app the day of the big release ✔
 Advertising on an extremely big video portal ✔
 A tiny travel blog ✔
 A physical device for advertising ✔
Ingesting clicks data for analytics
Getting the data

6. We need to:
 Try not to lose the events we receive (duh)
 Show customers data for better insight on their campaigns
 Scale up/down according to the incoming fluxes
 Improve the product by using the data we get
 Do it as fast as possible (wasn’t this ready a week ago?)
 Do it as cheap as possible
Ingesting clicks data for analytics
The challenge

7. Find the size of the problem you’re trying to solve
 How much data do you expect? Rate?
 What do you have to do with it?
 Do I have to do something with ALL of it?
 How fast do I have to do it?
Answers to these questions are a starting point.
Ingesting clicks data for analytics
Size

8. Once we know how big and bad the beast is, we
need to design the ranch that will keep it in check.
Iterative process and prone to a lot of failures, but
the world is out there to help us.
Think, write and draw a lot.
Ingesting clicks data for analytics
Design

9. … draw too much ...
Ingesting clicks data for analytics
Design

10. Most of us will never have the joy (and the horror) of
creating a new stack, novel in theory and practice.
Still we need to understand the theory behind every
brick.
Read the info, read the opinions, try little proof of
concept of the moving parts, it helps a lot!
Ingesting clicks data for analytics
Which bricks should I use

11. A very important brick.
Elasticity of computation power, many *aaS, managed solutions are
really a great help in terms of saved manpower and fast iterations.
It comes at a great cost to consider:
• $$$ (ymmv)
• Possible lock-ins
Ingesting clicks data for analytics
The cloud is a brick too

12. … well it’s never definitive ...
Ingesting clicks data for analytics
Design with bricks

13. Obviously we haven’t followed those guidelines.
One becomes savvy after crashing and burning
many times.
But still thanks to those errors we got there and
built, at every iteration, a better infrastructure.
Ingesting clicks data for analytics
How we did it

14. ClickMeter was already live and growing
It needed an overhaul in its infrastructure/backend.
The growth fueled the need to be ready for more power to handle more data.
Obviously this had to be a tablecloth trick migration 
Ingesting clicks data for analytics
How we did it

15. Already on the cloud (AWS), we thought of having a hybrid approach but it didn’t
make sense.
Review of old components already in production to see what to kill, keep or
update.
Kept good stuff and designed some new layers to make them work flawlessly in
the new infrastructure.
Ingesting clicks data for analytics
How we did it

16. Ingesting clicks data for analytics

17. Pretty important, they need to:
• Stay up
• Scale up/down depending on the incoming traffic
• Never lose anything
• Be as fast as possible in processing
They’re a custom web app application that undergoes a lot of testing.
We used stuff like Beanstalk, Scaling groups, Load Balancers and Health routing
offered by our cloud provider to manage the webapp scaling/availability
Ingesting clicks data for analytics
Redirect engine
aka events collector

18. Pipeline
Most of this part uses our cloud provider
technology.
This simplifies maintenance and provisioning,
keeping the focus on the value of our product.
Some moving parts are custom made by us to
interact with the cloud technology (might be
proprietary or just repackaged known one).
Ingesting clicks data for analytics
Tracking engine
and friends

19. SQS Pipeline
Kinesis
• Events • Preprocessing
• Postprocessing
• DynamoDB
Ingesting clicks data for analytics
Tracking engine
and friends

20. Combination of real-time and batch
technologies.
One of the scaling parts that actually provides
value to the customers.
Computes analysis on events data from a
simple count to some predictions.
Check the data produced by your processing
system to improve the pipeline step-by-step!
Ingesting clicks data for analytics
Pipeline

21. Ingesting clicks data for analytics
Pipeline

22. We employ different storage based on speed of delivery and data type.
All the data is accessible via a REST API.
This permits to develop a frontend layer with relative ease and allows customers
to take control of the data and use it in a way we may have not considered.
Ingesting clicks data for analytics
Storage and data delivery

23. Managed services on the cloud help us a lot!
Most of the team can focus on improvements
and shipping (users are happy, so is the CEO).
Some of us (me) still have to be the
CloudOp/DevOp.
p.s.: always prepare a Plan B for when you’ll
break things!
Ingesting clicks data for analytics
Operations

24. Cloud is typically more expensive of your own metal.
This extra money you have to spend is actually well spent:
• Flexibility
• Easier provisioning
• Easier management
• Easier operations
There are different types of clouds, so choose wisely.
Ingesting clicks data for analytics
Cloud co$t$

25. Creating and managing a “big data” ready infrastructure is no easy task,
but it can be done step-by-step also by startups.
The cloud is a cool starting ground providing you with many of the toys
you need, so you can focus on what part of “big data” gives you value!
Use the wisdom shared by the big/medium players that have already
been there (and built most of the stuff you’re using).
Ingesting clicks data for analytics
Conclusions

26. Thank You
Any questions?
@il_furio
francesco@clickmeter.com