22-25 April 2026 
SQLBits 2014
Column Store Index and Batch Mode Scalability Deep Dive
A deep dive into batch mode scalability with column store index and SQL 2014.
This session will take a deep dive into query scalability with column store indexes and batch mode, this presentation will illustrate how by leveraging vectorised processing and CPU L2/3 cache batch mode scales and compare this to row mode. Stack walking will be used to quantify the cost of conventional page and row compression, column store versus the new to SQL 2014 column store archive compression and row mode versus batch mode operators. The affect of storage that can and cannot keep up with the available CPU resource will be covered along with how well batch mode scales across 24 schedulers.
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.