22-25 April 2026 
SQLBits 2019
Graph databases - What, how and why
Graph databases – Origins, how they work, strengths and weaknesses
Different database engines are built to be good at a specific set of operations.
Relational engines, for example, are typically optimised for transaction control and protecting data from damage and loss during update.
They are typically not optimised for detecting fraud and performing recommendations (“Customers who bought this book frequently bought….”). Graph databases are essentially the opposite, poor at transactions and good at tasks such as fraud detection and recommendations. The key to using graph
databases effectively is understanding not only how they work but why they were designed that way – in other words, understanding what underpins their strengths and weaknesses. So this talk will explore their origins and how and why they work.
Relational engines, for example, are typically optimised for transaction control and protecting data from damage and loss during update.
They are typically not optimised for detecting fraud and performing recommendations (“Customers who bought this book frequently bought….”). Graph databases are essentially the opposite, poor at transactions and good at tasks such as fraud detection and recommendations. The key to using graph
databases effectively is understanding not only how they work but why they were designed that way – in other words, understanding what underpins their strengths and weaknesses. So this talk will explore their origins and how and why they work.
Speakers
Mark Whitehorn's previous sessions
Graph databases - What, how and why
Graph databases – Origins, how they work, strengths and weaknesses
Inside the classic machine learning algorithms
Algorithms like Neural Nets, Deep Learning, SVM and KNN are dominating the Machine Learning world. You don’t need to know the maths behind them but you do need to know how they work in order to use them effectively. This talk will explain just that.
Evaluating Models: Get Ready to ROC
Introducing the ROC curve and its use in Azure ML.
Analytics Master Class-exploiting exotic patterns in data
There are some little-known but very useful ways of extracting information from data.
This session will cover:
• Monte Carlo simulations
• Nyquist’s Theorem
• Simpson’s paradox
• Benford’s Law
These will rock your world (they certainly rocked mine).
MDX and DAX-compare and contrast
This talk looks at MDX and DAX, examining their similarities and differences. It won’t turn you into an expert in either but it will help you to decide, given your particular career plans, if either or both are worth learning.