Managing Database Indexes: A Data-Driven Approach
Managing Database Indexes: A Data-Driven Approach

Abstract: 

Database indexes can make or break the performance of a database. Efficient indexes need to be tailored to the specific queries that are sent to a database, but since query patterns can vary a lot and change over time, it is often a painful process to manually manage indexes. In this session, I will talk about our data-driven approach to automatically estimate optimal indexes from log data of our MongoDB databases. You will learn how we use Google Cloud Functions to stream log data from Stackdriver to Google BigQuery, how we use BigQuery to scale our data analysis, and how we use Python’s Jupyter Notebooks to visualize and monitor our results.

Bio: 

Having studied, researched and worked in the field of neuroscience, Amadeus Magrabi is a scientific expert at machine learning, experimental design and data analytics. Now the lead data scientist at Commercetools, Amadeus is responsible for the development of machine learning applications and data-driven optimizations for the company’s SaaS e-commerce platform. Together with his team, he works on features like product recommendations, image similarity search, anomaly detection, and forecasting.