Advanced Data Modelling techniques
Chris Travers works in Berlin as a database administrator, in a distributed PostgreSQL environment involving hundreds of TB of data in PostgreSQL. He has experience working with PostgreSQL in many areas from scientific computing in the life sciences, to ERP, from high-velocity ad-tech analytics to geo-spacial databases. He is the author of a number of extensions on pgxn.
His current experience involves mobile advertisement analytics over 400TB data sets and more short-terms log management environments handling PBs of data in PostgreSQL.
No video of the event yet, sorry!
This training focuses on leveraging PostgreSQL as a data modelling platform, rather than simply as a relational database. As a data modelling platform we have many techniques which can be used to more efficiently manage the data we have, sometimes adding features to applications which would be impossible to add through other means.
This training focuses on advanced data modelling techniques including non-first-normal-form designs, derivative data from semi-structured data types, functional indexes and advanced function considerations, and even cases where table inheritance is the right tool for the job.
Participants should bring laptops with PostgreSQL installed, a procedural language of their choice besides SQL and PL/PGSQL (PL/Perl or PL/Python recommended, though C programmers are welcome to work in C).
Participants should have a firm understanding of relational databases and SQL as a query language, as well as some software development experience.
- 7 h
- PostgresConf US 2018
- Postgres Internals
- Requires Registration:
- Yes (Registered: 15)