SQLBits 2020
Advanced Azure DevOps With Containers and Pipeline As Code
You are a developer and you want to leverage the 'Power' features in Azure DevOps via YAML pipeline as code, then this session is for you !
Azure DevOps CI pipeline as YAML allows you version control your pipeline, store it alongside your application code and much much more, including:
- the ability to reuse code across pipelines via templates
- parallelism
- the ability to craft hybrid Linux / Windows build agent pipelines
- the ability leverage containers
- the ability to aggregate tests from multiple jobs into one single consolidated report
- artifact handling
This session will cover how to leverage all of these features in a single pipeline.
Speakers
Chris Adkin's previous sessions
Advanced Azure DevOps With Containers and Pipeline As Code
You are a developer and you want to leverage the 'Power' features in Azure DevOps via YAML pipeline as code, then this session is for you !
Probelm Solving Using The In-Memory Engine
The in-memory OLTP engine has been around since version 2014 of the database engine, do you need to be a SaaS provider processing billions of transactions a day to use this ?, the short answer is absolutely not and this session will show you why.
An Introduction To Column Store Indexes and Batch Mode
This session is a primer on column store indexes and SQL Server batch mode, it delves into the background behind the performance gains achieved by batch mode and includes demos on things that can be done to further enhance batch mode performance.
Superscaling SQL Server 2014 Parallel Insert
This session will take a look at how parallel select into can be scaled to the nth degree in SQL Server such that all available hardware resources are utilised as fully as possible.
Column Store Index and Batch Mode Scalability Deep Dive
A deep dive into batch mode scalability with column store index and SQL 2014.
Scaling Out SSIS With Parallelism
An introduction to scaling out packages using parallelism with the "Work pile" pattern, balanced data distributor and "Roll your own" techniques.