Azure Synapse Analytics Overview
James will talk about the new products and features that make up Azure Synapse Analytics and how it fits in a modern data warehouse, as well as provide demonstrations.
CEO & Co-Founder of Data/Analytics Startup Backed by Top Silicon Valley Investors
USA, Monte Carlo
Making Data Downtime a Pillar of Your Data Strategy
Barr will introduce the concept of “data downtime” — periods of time when data is partial, erroneous, missing or otherwise inaccurate. Data downtime is highly costly for organizations, yet is often addressed ad hoc. She’ll discuss why data downtime matters to the data industry and tactics best-in-class organizations use to address it — including org structure, culture, and technology.
Processing Billions of Events a Day Using Kafka and Kafka Streams
Designing a system to cope with loads of billions of events is harder than it seems. In this talk the presenter will go through the most common use cases and pitfalls and provide tips and good practices about how to design systems to avoid them.
Real-Time Stream Processing for Insurance & Health Care With Kafka, Kafka Streams and Multi-Runtime Microservices
As a service provider for insurance companies, pension & healthcare funds we rolled out a resilient stream processing platform running in kubernetes that we can scale out horizontally to integrate different microservices developed in different languages like java, scala or python.
Adding AI Cloud Services to Your On-Prem Data Workflows for NLP & Content Enrichment
In this presentation Daniel will show how on-premise data processing or indexing pipelines can be extended by cloud services to get more out of your unstructured data while bypassing all the above-mentioned challenges saving time and money.
From the Earth to the Moon: Lessons from the Space Race to Apply in Machine Learning Projects
The space race was a EEUU – Soviet Union competition to conquer the space. This competence helped to develop space technology in an incredible manner, developing other derivative technologies as a side effect.
Data Versioning – What Does it Mean?
In this talk we will go over the difference between these solutions by clustering them according to 4 main use cases:
1. Collaboration over data: enabling teams to collaborate over data over time, while contributing to the data evolution.
2. Managing ML pipelines: allowing pipeline management of ML projects, from model creation to production.
The New ODPi – Moving from Standards to a Vendor-Neutral Home for Big Data Open Source
In this talk, learn about the new ODPi, how it’s leverage the expertise of the Linux Foundation in hosting vendor-neutral open source projects, and how you can bring your project to ODPi.
Data Science Case Studies and Formulation of AI Roadmap
From discussing what is AI to practical case studies of AI, Kane will discuss how companies in Hong Kong and world wide uses AI to create business values.
Graph Processing for Open Metadata and Governance
Learn how ODPi Egeria uses its distributed virtual graph to connect metadata about an enterprise’s data and IT services from many different tools and then apply governance across this landscape.
Real-Time Streaming with Python ML Inference
In this talk Marko will show one approach which allows you to write a low-latency, auto-parallelized and distributed stream processing pipeline in Java that seamlessly integrates with a data scientist’s work taken in almost unchanged form from their Python development environment.
The Importance of Good Data Quality and Understanding of Visitor Behavior
In the session, Mats goes through how to measure a person’s journey from being a person in an interesting segment to becoming a customer. He also tells you how to get high data quality on your web visitors so that you can use it in machine learning in Facebook and google.