Things to consider when setting up your ETL pipeline, data warehouse, and choosing an analytics tool.

1. Metabase Ljubljana
meetup #1

2. Metabase ❤
github.com/metabase/metabase
• Open source analytics tool
• 11k companies use us daily
• Largest user: Go-Jek with 4000 DAU
• Other notable users: N26, Under Armour, Swisscom,
Bang & Olufsen, NuBank, Convoy …
• Focus on UX and friendliness

3. Collect as much different
types of data as possible

4. Invest in a proper ETL
• Repeatability
• Visibility
• Extensibility
• Scalability
• Recoverability (don’t loose data, ever!)

5. ETL in a box
• segment.com
• Snowplow

6. Data warehouse topology
• Big fat denormalised tables, or
• Star schema
• Use materialised views to tailor
the representation to your tools

7. Which DB?
• TL;DR: use Postgres
• Optimize for ease of ad-hoc querying
• Should be decently performant (waiting kills productivity) but is
unlikely to be the bottleneck
• Simple to deploy, connect to, and use
• Data validation/schemas where possible
• Sane handling of timezones/dates, numbers

8. Choosing an analytics tool
• Who will be using it?
• What are the deliverables?
• Size of data & scaling
• Will it be part of an existing proces or the core of a new one?

9. Deploying Metabase
• Switch app DB to Postgres (or MySQL)
• Docker
• Setup logging
• Upgrade to latest (but backup first)
• Ideally have 1 instance for all (if you need fine grained
access control talk to us about enterprise version)
• Have fun and fall in love! ❤

10. Coming soon
• Instance serialisation
• Reduced memory footprint
• More extensive support for joins (and a new query builder)

11. Questions
https://metabase.com
github.com/metabase/metabase
@sbelak