Your business data contains critical information about customer behaviours, operational decisions, and many factors that have financial impact on your organisation. Increasingly though, this data is too big, too fast, and too complex for existing systems to handle. AWS Data and Analytics services are designed to ingest, store, analyse, and consume information at record-breaking scale. In this session you will learn how these services work together to deliver business automation, enhance customer engagement and intelligence.

Craig Stires, APAC Head of Analytics, Big Data, and AI, Amazon Web Services


Data Lake allows an organisation to store all of their data, structured and unstructured, in one, centralised repository. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand. In this session we will explore the architecture of a Data Lake on AWS and cover topics such as storage, processing and security.

Tom McMeekin, Associate Solutions Architect, Amazon Web Services


Visualisation is an essential part of analysing your data to gain insights and communicate them across your organisation. In this session we will explore Amazon QuickSight which makes it easy to build visualisations, perform ad-hoc analysis, and quickly get business insights from your data. You'll see a live demonstration of how you can easily connect to your data, perform advanced analysis, and create visualisations and dashboards that can be accessed from any browser or mobile device.

David McAmis, Big Data Consultant, Amazon Web Services


Processing streaming data is becoming increasingly important to many organisations who need to analyse incoming data both in near real-time and in batch. In this session we will look at the best practices and patterns for analysing streaming data with AWS Kinesis Streams, Kinesis Firehose and Kinesis Analytics.

Johnathon Meichtry, Principal Solutions Architect, Amazon Web Services


Apache Spark is the fast, open source engine that is rapidly becoming the most popular choice for big data processing. Running it on AWS is especially powerful as you get scale, elasticity and agility from the AWS platform coupled with the rich functionality that Spark provides.In this session we will explore how to get the most out of Spark on AWS.

Nam Je Cho, Enterprise Solutions Architect, Amazon Web Services


Relational databases are the core engines of many workloads. In this session we will start off by exploring the options and best practices for running relational databases on AWS and then take a deeper dive into Amazon Aurora and show how it can be used to run OLTP workloads at scale.

Johnathon Meichtry, Principal Solutions Architect, Amazon Web Services


The benefits of running databases in the cloud are compelling but how do you get the data there? In this session we will explore how to use the AWS Database Migration Service and the AWS Schema Conversion Tool to help you to migrate, or continuously replicate, your on-premise databases to AWS.

Jarrod Spiga, Solutions Architect, Amazon Web Services


Organisations involved in Big Data and Analytics spend a lot of time preparing data for analysis which often involves large-scale movement and transformation. In this session we will explore AWS Glue, a new service designed to assist with the process of cataloging, transforming and scheduling for your data pipeline.

Cassandra Bonner, Solutions Architect, Amazon Web Services


Machine Learning is increasingly being used by organisations to move from analysis to prediction. Co-founders of Jemsoft, a computer vision and machine learning company have worked on providing industry leading technology to global enterprise and developers. In this session they will discuss how they utilise AWS and open source technology to perform both Deep Learning and Machine Learning.

Jan Haak, Solutions Architect, Amazon Web Services

Emily Rich, Co Founder, Jemsoft, Jordan Green, Co Founder, Jemsoft


A large proportion of the data we generate is highly structured and hence SQL is often the natural choice for analysing that data.There are many SQL engines to choose from for large scale analytics and in this session we will compare some of of the options including Amazon Redshift, Amazon Athena, Presto, Spark SQL and Apache Hive.

Russell Nash, Big Data Solutions Architect, Amazon Web Services