February 05 – 07 , 2018
PGConf.Russia 2018
PGConf.Russia 2018
PGConf.Russia is a leading Russian PostgreSQL international conference, annually taking together more than 500 PostgreSQL professionals from Russia and other countries — core and software developers, DBAs and IT-managers. The 3-day program includes training workshops presented by leading PostgreSQL experts, more than 40 talks, panel discussions and a lightning talk session.
Thems
- PostgreSQL at the cutting edge of technology: big data, internet of things, blockchain
- New features in PostgreSQL and around: PostgreSQL ecosystem development
- PostgreSQL in business software applications: system architecture, migration issues and operating experience
- Integration of PostgreSQL to 1C, GIS and other software application systems.
Talks
Talks archive
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Andrey Borodin YandexWAL-G is simple and effective disaster recovery tool for PostgreSQL using cloud storages. In its core functionality, WAL-G is the successor of WAL-E rewritten in Go. But there is one new neat feature - delate-backups. WAL-G delta-backups, whenever possible, stores only pages, changed since the previous backup. In this talk, I'm going to describe development process of this feature.
Surprisingly, most important and complicated question was the design of the interface: WAL-e is simple and comprehensive, keeping these properties was goal #1. Technical details of implementation were covering some underwater stones too. Besides these, I want to discuss the perspective of technological development and future coordination of recovery tools developers.
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Ivan Frolkov PostgresProApart from its main purpose of scheduling tasks, pgpro_scheduler can also deal with chained transactions. It can be used in various scenarios of asynchronous data processing.
This tutorial demonstrates pgpro_scheduler features that ensure secure processing of chained transactions. We'll be using cryptocurrency transactions as an example.
pgpro_scheduler is included into Postgres Pro Enterprise as an extension.
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Olivier Courtin DataPink- Spatial and advanced spatial analysis with pure PostGIS (including cutting edge PostGIS functions available)
- How could we mix and tied efficiently PostgreSQL and Python data types (as NumPy ndarray, and Pandas DataFrames)
- Tools to improve our data manipulation environment (Jupyter tricks, easy dataviz...)
- How to go further throught GeoDataScience, with Python libs and framework tied with PostgreSQL/PostGIS (including Machine and DeepLearning)
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Dmitriy Pavlov ArenadataIn the pitch I will talk about the most important nuances of deployment and operations of the distributed analytical open-source database based on PostgreSQL - Greenplum. I will analyze the typical mistakes in its use, give the best practices and warn about bottlenecks.
Photos
Photo archive