PgConf.Russia 2017

I will tell you about why Postgres is first-choice product as a foundation for your BI system with classical OLAP workload. Briefly it will be said about existing open source BI solutions.

I will also describe specific of our architecture, why we chose snowflake scheme and how we are doing extract, transformation and load procedures. It will be mentioned about special Postgres tuning for OLAP and massive data bulkload workloads. Also I will let you know about Postgres usage as a column database with cstore_fdw by Citus and results achieved. Cons and problems of our approach will be described in the end of the talk.

VIDEO

This talk is intended for Oracle DBAs whose are considering to start using PostgreSQL as another RDBMS engine in their DB portfolio or for PostgreSQL DBAs, who wants to learn some of similarities and differences between Oracle and PostgreSQL. This talk is not intended to be a migration guideline neither advocating one platform over another. General database structure overview for both platforms will be compared in terms of memory configuration parameters; logical to physical structure as well as we’ll touch some security point of view and backup & recovery tools available. Oracle container databases are not covered. The talk want’s to help create a bridge for DBA and avoid some frustrations at the first Oracle DBA step to discover, learn and start using PostgreSQL as reliable fully functional database.

VIDEO

Debugging, profiling, and tracing of the executed commands play an important role in development of any applications. This is also true for developing stored procedures in DBMS.

PostgreSQL offers various tools (both built-in and external) for these purposes.

In this talk, we will provide an overview of the available tools and their advantages and disadvantages, as well as a detailed demo of their use cases.

This talk is a part of a basic course for backend application developers (DEV1), which “Postgres Professional” company plans to announce in the near future.

Citus allows you to distribute postgres tables across many servers. It extends postgres to transparently delegate or parallelise work across a set of worker nodes, enabling you to scale out the CPU and memory available for queries.

One year ago, we began a long journey to allow Citus to scale out another dimension: write throughput. With writes being routed through a single postgres node, write throughput in Citus was ultimately bottlenecked on the CPUs of a single node. Citus MX is a new edition of Citus which allows distributed tables to be used from from any of the nodes, enabling NoSQL-like write-scalability.