BIG DATA CONFERENCE

EUROPE 2021

Online Edition

September 28-30

Online

SCHEDULE

Workshops (September 30)

Spark and HADOOP

Lidor Gerstel

Centerity

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ONNX runtime to serve AI models

Mauro Bennici

You Are My Guide – GhostWriterAI

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Proactive and Polymorphic Adaptation of Multi-Cloud Deployments

Paweł Skrzypek & Alicja Reniewicz

AI Investments

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An introduction to FluxLang

Riccardo Tommasini

University of Tartu – Titania Project OÜ

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1st Conference Day (September 28)

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
Creating a Dwh From Scratch to Analyze 11 Million Kilometers Worth of Bike Rides
Enrico Berti
VanMoof
Data Warehouses
BigQuery
Predictive Models
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) The Importance of Performance in Open Source Databases
Lukas Vileikis
Severalnines
Databases
MySQL
Performance
Security
Track: Data
A Friendly Introduction to Codeless Deep Learning
Kathrin Melcher
Knime
Deep Learning
CNN
Keras
KNIME
Track: Machine Learning
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: 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
TBA
Track: Varia
15:15 - 15:30 (GMT+3) Afternoon Break
15:30 - 16:15 (GMT+3)
CLOSING KEYNOTE:
Embracing #AiFirst Enterprise-Wide
Alex Sanginov
ServiceNow
ML
Enterprise AI
Data Science

2nd Conference Day (September 29)

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 Luxoft
Data Governance
Azure
Spark
Track: Cloud and Streaming
TBA
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
NLP & Machine Learning Applied to the Analysis of Advertising Data and User Behavior on the Website for Marketing Purposes
Paolo Dello Vicario
Datrix
Machine Learning
NLP
Marketing
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 - 16:15 (GMT+3)
CLOSING KEYNOTE:
TBA