Relational databases were the persistence system of choice for decades, until the Web 2.0 in the 2000s required to process volumes of data so big it needed distributed systems running in parallel. A new type of databases (NoSQL) was adopted to solve this problem in different ways. https://www.bigdataspain.org/2017/talk/relational-is-the-new-big-data Big Data Spain 2017 16th - 17th November Kinépolis Madrid

2. RELATIONAL
is
the
new BIG DATA

3. Daniel Domínguez
Head of Data
Previously: CIEMAT / CERN
@danieluchi01
Miguel Ángel Fajardo
CTO
Previously: EA Games, Gilt, Shutterstock
@ma_bits

5. Distributed Processing Not Only SQL

9. A long time ago in a galaxy far,
far away...

10. 1960s
Information Management System (IMS)
by IBM
● Built for Saturn V moon rocket
● Hierarchical, tree structure

12. 1970
Relational Model Paper
● Base for IBM DB1 and DB2

14. 1980s-90s
Development of RDBMS
● Widely adopted
● Models easy to define
● ACID transactions
● Clients for all stacks
● ORMs

15. 2000s
Web 2.0
● Large volume (petabytes)
● Faster networks and devices
● Systems must scale

16. Problems scaling Relational DBs
● Sharding is hard
● Maintaining transactions ACID is hard
● Two-phase commit is hard
● Parallelizing is hard

18. Distributed Processing Not Only SQL

19. Relational databases
The CAP theorem

20. Key-value stores
○ User session data
○ Component configuration
○ Cached data, fast access
○ Complex queries
○ Interconnected data

21. Column-oriented DB
○ Real time analytics
○ Facebook Messenger
○ Queries against few rows
○ Flexible data schemas
○ Incremental data loads/deletes

22. ○ Records with different fields
○ Models with many layers
○ Joins
○ Flexible queries
Document-oriented DB

23. Graph DB
○ Routing
○ Social networks
○ Disease spreading
○ Hard to do aggregates
○ Analytics

24. No one magic database to rule them all
● Each of them fits a small number of use cases
● Often hard, complex and expensive to maintain
● Specific query languages
CQL

25. MEANWHILE
IN THE RELATIONAL BATCAVE

26. 2010s Relational strikes back
● Less structured data formats
● Partitioning
● Parallel execution
● Sharding
● C, A and P?

28. ● ACID for queries going to a single shard
● Open Source, DAAS
● PostgreSQL extension
● Interactive analytics
● Multi-tenant
● Fully ACID
● Open Source
● PostgreSQL fork
● Scaling intensive
● Multi-tenant

33. WHEN YOU HAVE A HAMMER...

34. Questions?
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