November 19-22

Onsite & Online

Confirmed Talks


Rob van Elburg

Ravecruitment, The Netherlands

Big Data in Career

A career in Big Data? What does that look like? What are the 10 most wanted profiles? What are the career paths? How many jobs are there? How to level up? And what are the salary ranges? So many questions about your career in Big Data. Rob van Elburg, Global Principal Big Data Recruiter will explain all in this Talk about your career in Big Data

Session Keywords
🔑 Career
🔑 Big Data
🔑 Global Tech

Sonal Goyal

Zingg.AI, India

Open Source Entity Resolution Using Zingg

This talk will describe Entity Resolution, which is a technique to identify data records in a single data source or across multiple data sources that refer to the same real-world entity and to link the records together.

Session Keywords
🔑 Master Data
🔑 Deduplication
🔑 Spark
🔑 ML

Brian O’Neill

Designing for Analytics, US

Why Customers Can’t or Won’t Use Your Technically-Right Data Products

Brian will discuss strategies that leaders should know about designing effective data products. He’ll discuss the mindshift change around outputs and outcomes, the role of data product management, how separating “operationalization” from core ML and analytics work leads to failures, and how human-centered design provides teams a step-by-step method for “doing innovation” that leads to better data products.

Session Keywords
🔑 Product
🔑 Design
🔑 Analytics
🔑 ML

Juan Pan

SphereEx, China

PostgreSQL Distributed & Secure Database Ecosystem Building

This session will focus on introducing how to empower PostgreSQL thanks to the ecosystem provided by Apache ShardingSphere – an open-source distributed database, plus ecosystem users and developers need for their database to provide a customized and cloud-native experience.

Session Keywords
🔑 Databases
🔑 PostgreSQL
🔑 Middleware

Radu Vunvulea

Endava, Romania

Keeping Your AI/ML Data Secure

Who owns, has access and manages our data? Nowadays, we integrate AI/ML services into our business so fast that we don’t have enough time to analyze where our data are stored and how we can secure them. In this session, we tackle a part of Azure core AI/ML services from the data security point of view.

Session Keywords
🔑 Data Security
🔑 ML
🔑 AI

Ricardo Ferreira

Elastic, US

Building Debugging-Enabled Data Pipelines

Observability technologies provide a handy way for data engineers to trace, troubleshoot, and fix data pipeline problems. This session will explain why this practice is important and its benefits. It will also show in practice how to apply this in a complex-enough data pipeline built using Apache Kafka, Debezium, MySQL, and ksqlDB.

Session Keywords
🔑 Tracing
🔑 Kafka
🔑 Pulsar
🔑 Flink
🔑 Streaming

Shalvi Mahajan

SAP SE, Germany

Gender Bias in Artificial Intelligence

Good ML algorithms have bad gender biasing and go sexist most of the time. There are many challenging problems that we implicitly face but tend to ignore. These small biasing in minds do result in a big disparity in the crowd all over the world. In this conference, we will tackle this problem and try to explore ways to solve it.

Session Keywords
🔑 Gender Bias
🔑 Translation
🔑 ML