BIG DATA CONFERENCE

EUROPE 2021

Online Edition

September 28-30

Online

SCHEDULE

Workshops

1st Conference Day

2nd Conference Day

Workshops (September 30)

Online		Online
08:40 - 09:00	Registration	Developing Performant Data Streaming Applications Using Kafka Carlos Manuel Duclos-Vergara Spark and HADOOP Lidor Gerstel ONNX runtime to serve AI models Mauro Bennici Improving Performance and Security in MySQL Lukas Vileikis An introduction to FluxLang Riccardo Tommasini
09:00 - 10:30	Workshop part I
10:30 - 10:40	Coffee Break
10:40 - 12:00	Workshop part II
12:00 - 13:00	Lunch
13:00 - 14:20	Workshop part III
14:20 - 14:30	Coffee Break
14:30 - 16:30	Workshop part IV

Online
Developing Performant Data Streaming Applications Using Kafka Carlos Manuel Duclos-Vergara Spark and HADOOP Lidor Gerstel ONNX runtime to serve AI models Mauro Bennici Improving Performance and Security in MySQL Lukas Vileikis An introduction to FluxLang Riccardo Tommasini
Workshops Program
08:40 - 09:00	Registration
09:00 - 10:30	Workshop part I
10:30 - 10:40	Coffee Break
10:40 - 12:00	Workshop part II
12:00 - 13:00	Lunch
13:00 - 14:20	Workshop part III
14:20 - 14:30	Coffee Break
14:30 - 16:30	Workshop part IV

1st Conference Day (September 28)

Time	Accenture Booth
14:30 - 15:00 (GMT+3)	ALL about Baltic Accenture Data & AI Team / Q&A Accenture Booth

Time	Track: Data	Track: Machine Learning	Track: Cloud and Streaming	Track: Varia
08:30 - 09:00 (GMT+3)	Registration
09:00 - 10:00 (GMT+3)	OPENING KEYNOTE: Trust Your Data Mark Grover Stemma Data Discovery Metadata Amundsen
10:05 - 10:50 (GMT+3)	Rethinking Ingestion: CI/CD for Data Lakes Einat Orr Treeverse Data Lake Data Versioning Ingestion Track: Data	ML in Production – Serverless and Painless Oliver Gindele Datatonic MLOps Serverless Containers Tensorflow Track: Machine Learning	Designing Robust Processing System With Redis Paško Pajdek Mediatoolkit Realtime Data Processing Queueing Redis Track: Cloud and Streaming	Building a Serverless GraphQL API in 25 Minutes Maxime Beugnet MongoDB Serverless API MongoDB Realm Track: Varia
10:50 - 11:05 (GMT+3)	Morning Break
11:05 - 11:50 (GMT+3)	Data Observability Gerard Toonstra Datafold Data Observability Data Lineage Catalog Track: Data	Machine Learning Helping the Economy Diana Gabrielyan Stockmann ML Text Mining Economics Inflation Track: Machine Learning	The Honest Review of Amazon SageMaker Wojciech Gawroński Pattern Match ML Cloud Amazon SageMaker Track: Cloud and Streaming	DataSecOps: Why You Should Care Ben Herzberg Satori Cloud DataOps Security Data Engineering Track: Varia
11:55 - 12:40 (GMT+3)	Cloud Computing Anomaly and Threat Detection Using Big Data Analytics and Machine Learning Ibrahim Muzaferija Maestral Solutions Cloud ML Anomaly Detection Support Vector Machines User Behavior Modeling Track: Data	A Friendly Introduction to Codeless Deep Learning Kathrin Melcher Knime Deep Learning CNN Keras KNIME Track: Machine Learning	The Importance of Performance in Open Source Databases Lukas Vileikis Severalnines Databases MySQL Performance Security Track: Cloud and Streaming	Expanding the Data Team: Analytics Engineers Victoria Perez Mola Tier mobility Team Management Data Team Analytics Engineer Track: Varia
12:40 - 13:40 (GMT+3)	Lunch Break
13:40 - 14:25 (GMT+3)	Graph Data Science: from Theory to Application Julien Genovese Data Reply Graph Data Science MLlib Track: Data	In-Database Machine Learning with Jupyter Paige Roberts Vertica ML Data Architecture Jupyter Track: Machine Learning	Best practices for ETL with Apache NiFi on Kubernetes Albert Lewandowski GetInData ETL Kubernetes NiFi Track: Cloud and Streaming	How to Fail in AI Business Mohammad Hossein Noranian Esra Tech AI Business Case Study Track: Varia
14:30 - 15:15 (GMT+3)	The Unbreakable Data Pipeline Herminio Vazquez IOVIO Data Engineering Data Pipeline PySpark Track: Data	The Intuition Behind Machine Learning In Marketing Mario A Vinasco Credit Sesame ML Marketing Advanced Segmentation Cross Sell Predictions Track: Machine Learning	Real-Time Streaming in Any and All Clouds, Hybrid and Beyond Timothy J Spann StreamNative Streaming Flink Pulsar Nifi Track: Cloud and Streaming	Trends in 2021 - CRPA, AutoML & the Role of DataOps Barry Walsh Pairview Group ML DataOps Trends Track: Varia
15:15 - 15:30 (GMT+3)	Afternoon Break
15:30 - 16:30 (GMT+3)	CLOSING KEYNOTE: Embracing #AiFirst Enterprise-Wide Alex Sanginov ServiceNow ML Enterprise AI Data Science

2nd Conference Day (September 29)

Time	Accenture Booth
10:05 - 10:50 (GMT+3)	Industrial Use Cases of Data Science Accenture Booth
14:30 - 15:00 (GMT+3)	ALL about Baltic Accenture Data & AI Team / Q&A Accenture Booth

Time	Track: Data	Track: Machine Learning	Track: Cloud and Streaming	Track: Varia
08:30 - 09:00 (GMT+3)	Registration
09:00 - 10:00 (GMT+3)	OPENING KEYNOTE: A Code-Driven Introduction to Reinforcement Learning Phil Winder Winder Research Reinforcement Learning Cyber Security
10:05 - 10:50 (GMT+3)	Use Visual Studio Code for Your Machine Learning Environments Kris van der Mast VaHa ML Visual Studio Python Azure Track: Data	Neural Networks on the Source Code Jameel Nabbo Cybersecurity Researcher, The Netherlands ML on Source Code Static Code Analysis Compilers Track: Machine Learning	Management of a Cloud Data Lake in Practice: How to Manage 1000s of ETLs Using Apache Spark Josef Habdank DXC Technology Data Governance Azure Spark Track: Cloud and Streaming	Industrial Use Cases of Data Science Sana Rasheed Accenture Data Science Predictive Models Industry Use Cases Track: Varia
10:50 - 11:05 (GMT+3)	Morning Break
11:05 - 11:50 (GMT+3)	Using Service Level Objective Theory to Design Great Data Products Emily Gorcenski ThoughtWorks Reliability Engineering Data Mesh AI Track: Data	Complex AI Forecasting Methods for Investments Portfolio Optimization Paweł Skrzypek Anna Warno AI Investments ML Forecasting Investing Track: Machine Learning	Development of a Kafka-Powered Advanced Stream Commerce Platform Andrea Spina Radicalbit MLOps Streaming Kafka Track: Cloud and Streaming	Machine Learning Security Karol Przystalski Codete ML Security Track: Varia
11:55 - 12:40 (GMT+3)	Riding the Second Wave - Open Source for Relational Databases Jan Karremans EDB Postgres Databases Open Source PostgreSQL Track: Data	Share Massive Amounts of Live Data with Delta Sharing Frank Munz Databricks Data Science Open Source Data Sharing Track: Machine Learning	Real Time Streaming Data from AWS MSK Kafka to Cloudera Lidor Gerstel Centerity Hadoop Databases ETL NoSQL Scala Track: Cloud and Streaming	Keyword search is dead! And so are Solr and Elasticsearch? Daniel Wrigley SHI Natural Language Processing (NLP) Vector Similarity Search Solr Elasticsearch Track: Varia
12:40 - 13:40 (GMT+3)	Lunch Break
13:40 - 14:25 (GMT+3)	Big or Small Data in the Food Industry? Antía Fernández Gradiant Big Data Data Analytics Food Industry Track: Data	The state of MLOps - machine learning in production at enterprise scale Bas Geerdink Aizonic MLOps Big Data Machine Learning Track: Machine Learning	Choosing the Right Abstraction Level for Your Kafka Project Carlos Manuel Duclos-Vergara Schibsted Streaming Architecture Event Processing Kafka Track: Cloud and Streaming	Architecture vs. Operating Model - A Cloud Conundrum Federico Fregosi Contino End-to-End Tests Developers Agile Test Automation Track: Varia
14:30 - 15:15 (GMT+3)	Building Data Science Products Valentina Djordjevic Things Solver ML Data Science Product Development Track: Data	Towards Human-AI Teaming: Challenges and Opportunities of Human in the Loop AI Training Clodéric Mars & Sagar Kurandwad AI Redefined ML Multi-Agent Systems Reinforcement Learning Track: Machine Learning	An Introduction to Streaming SQL with Materialize Marta Paes Materialize Databases Streaming SQL Track: Cloud and Streaming	Zoom Out: Building a Kickass Engineering Team Remotely Gad Salner Melio Team Management Agile Track: Varia
15:15 - 15:30 (GMT+3)	Afternoon Break
15:30 - 16:30 (GMT+3)	CLOSING KEYNOTE: Translating Data Into Powerful Stories Juan Venegas Growth Tribe Data storytelling Data Visualisation

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