Big Data Conference
Europe 2021
Online Edition
September 28-30
Online
Conference is over!
See you next year!
ABOUT THE CONFERENCE
Big Data Conference Europe is a three-day conference with technical talks in the fields of Big Data, High Load, Data Science, Machine Learning and AI.
Conference brings together developers, IT professionals and users to share their experience, discuss best practices, describe use cases and business applications related to their successes.
The event is designed to educate, inform and inspire – organized by people who are passionate about Big Data and Data Exploration. We look forward to seeing you there!
BIG DATA CONFERENCE EUROPE AS AN ONLINE
Attendees as well as the speakers will be able to attend workshops and listen to talks online.
The format of the conference will be online, therefore, even if the covid-19 situation will become worse and restriction will come in place again we will be able to deliver the event without any distortions.
Days
TRACKS
TALKS
WORKSHOPS
ATTENDEES
WORKSHOPS
CONFIRMED WORKSHOPS
CONFIRMED TALKS
CONFIRMED TALKS
TIMELINE
28
September
1st Conference Day
• Opening Keynote
• 4 Tracks
• 20+ Carefully Selected Talks
• Sponsor‘s Expo
• Networking Activities
• Closing Keynote
29
September
2nd Conference Day
• Opening Keynote
• 4 Tracks
• 20+ Carefully Selected Talks
• Sponsor‘s Expo
• Networking Activities
• Closing Keynote
30
September
Workshop Day
• Full-Day Workshops
• Small Groups
• Individual Trainer Assistance
SCHEDULE
Workshops (September 30)
| |
Online | |
|---|---|---|
| 08:40 - 09:00 | |
Developing Performant Data Streaming Applications Using KafkaCarlos Manuel Duclos-Vergara
Spark and HADOOPLidor Gerstel ONNX runtime to serve AI modelsMauro Bennici
Improving Performance and Security in MySQLLukas Vileikis
An introduction to FluxLangRiccardo Tommasini
|
| 09:00 - 10:30 | | |
| 10:30 - 10:40 | | |
| 10:40 - 12:00 | | |
| 12:00 - 13:00 | | |
| 13:00 - 14:20 | | |
| 14:20 - 14:30 | | |
| 14:30 - 16:30 | |
|
|
||
|---|---|---|
Developing Performant Data Streaming Applications Using KafkaCarlos Manuel Duclos-Vergara Spark and HADOOPLidor Gerstel
ONNX runtime to serve AI modelsMauro Bennici
Improving Performance and Security in MySQLLukas Vileikis
An introduction to FluxLangRiccardo Tommasini
|
||
| | ||
| 08:40 - 09:00 | | |
| 09:00 - 10:30 | | |
| 10:30 - 10:40 | | |
| 10:40 - 12:00 | | |
| 12:00 - 13:00 | | |
| 13:00 - 14:20 | | |
| 14:20 - 14:30 | | |
| 14:30 - 16:30 | | |
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 GraphQLAPI 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
|
|||
KEYNOTE SPEAKERS
EVENT HOSTS
Click on arrows to view speakers:
Priyanka Sharma
Day 1 and 2, Machine learning Track
Marta Paes
Day 1, Cloud & Streaming Track
Jonas Christensen
Day 1, Varia Track
Alexander Hertwig
Day 1 and 2, Data Track
Alessandro Festa
Day 2, Cloud & Streaming Track
Jan-Willem Middelburg
Day 2, Cloud & Streaming Track
Jörg Egretzberger
Day 2, Varia Track
SPEAKERS
Click on arrows to view speakers:
Albert Lewandowski
GetInData, Poland
Bas Geerdink
Aizonic, The Netherlands
Carlos Manuel Duclos-Vergara
Schibsted, Norway
Daniel Wrigley
SHI, Germany
SILVER SPONSORS
KNOWLEDGE PARTNER
INFO PARTNERS
2020 BigData KEYNOTE SPEAKERS
Barr Moses
CEO & Co-Founder of Data/Analytics startup backed by top Silicon Valley investors
USA, Monte Carlo
Click on arrows to view speakers:
Barr Moses
USA, Monte Carlo
Nicolas Fränkel
France, Hazelcast
John Mertic
USA, Linux Foundation
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