Snowplow is an event analytics platform that empowers users to gain control over their data, allowing for real-time insights and custom data tracking across various channels. The document outlines Snowplow's approach to differentiating digital analytics, emphasizing data ownership, flexibility, and scalability while detailing new features and industry-specific solutions on their roadmap for 2017. Additionally, it highlights the ongoing development of real-time capabilities and enhanced data processing methodologies.

1. Snowplow Meetup Amsterdam #3
Introduction to Snowplow, new users and what we are
working on

2. 1. SNOWPLOW: EMPOWERING SMART PEOPLE
TO DIFFERENTIATE WITH DATA SINCE 2012

3. What is Snowplow?
Snowplow is an event analytics platform like GA, Adobe, Mixpanel, but we
take a radically new approach
We give you control of your data so you can:
• Ask and answer any question of the data
• Build data-driven applications, in real-time
• Evolve your analytics stack as your business and understanding grow
We set smart people free to do more with their data and differentiate from
their competition

4. A radically new approach to digital analytics
To differentiate your business with data,
you need to use data differently
Standard solutions
• Standard reports and dashboards, based
on standard data models
• One size fits all
• Your access to your data is mediated by the
vendor
Snowplow: a fresh approach
• You are in control of your analytics stack
• You decide what data to track
• You decide what questions to ask of that
data
• You decide what techniques and
technologies to use to analyze the data
• You own all your data in your own data
warehouse
• Standard reports and dashboards,
based on standard data models
• One size fits all
• Your access to your data is mediated
by the vendor
• You are in control of your analytics
stack
• You decide what data to track
• You decide what questions to ask of
that data
• You decide what techniques and
technologies to use to analyze the data
• You own all your data in your own data
warehouse

5. Transform your business with data
We empower analysts, engineers, product managers, marketers, operations and
other specialists to do transformative things with data
Ownership
You own your data in
your data warehouse
and your unified log
Control
You decide what data to
collect, what questions
to ask of the data, what
techniques and tools to
use to analyze it and
how to act on the insight
generated
Freedom
Do what you want with
your data. The only limit
is your imagination

6. Single customer view
Take control of your data: you own your data in your AWS data warehouse
Track events from everywhere: your own applications and marketing channels (web,
mobile, PC, smart home, smart car, Internet of Things, email, call center, etc.)
Stitch data together from each channel and platform to form a single customer view
Flexible: you decide the data structure, enrichment and modeling logic that suits your
business. Easy to change how you collect data as your business, and the questions you
ask of the data, evolve

7. Act on your data in real-time
All your data available in seconds. Act on it in real-time
Your data where you need it, including Amazon Redshift, Kinesis, Elasticsearch
and S3
Rich data: define your entities and events. Enrich them with first and third party
data
Built for analysis: highly structured data, easy to query. Clean separation
between events and business logic applied

8. Trust your data
Trust your data: auditable data pipeline. Systematic data validation and
error reporting
Scalable: track and process billions of events per day
Low cost: it runs on AWS. Much cheaper than the commercial alternatives
Data experts: tap into our data analytics and engineering teams, our
partners and our data-sophisticated community to do more with our
technology

9. 2. SNOWPLOW IS GROWING WITH EXCITING
NEW USERS AND USE CASES

10. Snowplow is widely used by companies across
sectors to take control of their data

11. 3. WE HAVE A PACKED ROADMAP FOR 2017

12. 3.1 PIPELINE PORTABILITY

13. Support for more clouds
Adopting Snowplow shouldn’t mean being tied to one cloud. Take Snowplow’s
core capabilities – and your data – with you when migrating
Late 2016 we released a Snowplow real-time on-premise beta with Kafka
Focus this year is getting real-time Snowplow running on Google Cloud Platform
– using Cloud Pub/Sub and Google Cloud Dataflow
Also actively brainstorming a port to Azure with Microsoft

14. Support for more storage targets
There is no single“hero” database for Snowplow – different databases
meet different analytical and operational needs
Currently support Redshift and Elasticsearch, plus limited Postgres support
Want to add Avro/Parquet/S3, AWS Athena, BigQuery, Snowflake
Also doing a lot of work implementing analytics-on-write with DynamoDB,
and want to restart R&D on graph databases

15. 3.2 TAILORING SNOWPLOW TO YOUR INDUSTRY

16. 3.2 TAILORING SNOWPLOW TO YOUR INDUSTRY

17. Tailoring Snowplow to your industry
Snowplow users in specific industries (publishing, retail, gaming) want
Snowplow to do more out of the box
1. Schema bundles for specific industries – based on our experiences with
customers
2. Event data models for these industries, using these event schemas
3. BI templates for these industries in specific tools e.g. Looker, Redash,
Superset

18. More intelligent event sources
Want to build a better “day zero experience” for analysts and developers
Working on more developer-friendly trackers – easier to get started,
fewer intimidating configuration options
Adding more auto-tracking of events, especially in web/mobile SDKs
Webhooks – currently support about 12 external platforms. We are
exploring schema inference so we can add another 100 sources!

19. 3.3 RE-ARCHITECTING SNOWPLOW FOR THE
FUTURE

20. Moving our batch pipeline to Spark
Have been using Hadoop (Cascading/Scalding) for 5 years - finally porting
the core batch jobs to Spark
Community-driven effort (Phil Kallos @ Hired, David White @ Nordstrom,
Gabor Ratky @ Secret Sauce)
Code complete, going into QA soon
Expect positive performance impact, plus a key building block for our real-
time loading of Redshift

21. Mega-scale Snowplow
Exploring a “mega-scale” version of Snowplow for 100m+ events a day,
event archives of 50+ billion
At that scale, you start running into problems with S3, Redshift, EMR
Ongoing R&D on a set of practices/patterns to make mega-scale Snowplow
less painful
Techniques include file compression & compaction, job flow
parallelisation, “reactive batch” approaches

22. 3.4 DECISIONING AND RESPONSE

23. Sauna lets you turn your customer insights into
actions in different marketing channels

24. After a batch beta, now working on real-time
support for Sauna
Express individual commands as self-describing JSONs in a Kinesis (or
Kafka or SQS or Cloud Pub/Sub) stream/topic
Sauna will read these commands and execute them using the appropriate
responder
At launch we will support sending Slack and HipChat messages, and firing
incidents to PagerDuty

25. QUESTIONS?