BIG DATA CONFERENCE

EUROPE 2021

Online Edition

September 28-30

Online

SCHEDULE

CONFERENCE CHECK IN!

If you have already obtained a ticket to the Big Data Conference Europe 2021 conference (September 28-29), you can proceed with CHECK IN to the event platform.

  • Go to Pine – virtual conference platform
  • Create account and login to the system (please use the same email as you have received the Pine invitation to)
  • Update your attendee card

If you have any issues, please submit the ticket.

WORKSHOP CHECK IN!

If you have already obtained a ticket to the Big Data Conference Europe 2021 conference workshop (September 30), find CHECK IN information that has been sent to your email from tickets@bigdataconference.eu

In case you cannot find our email, please check your spam folder.

If you have any issues, you can call us +370 695 65000 or drop an email to tickets@bigdataconference.eu

Workshops (September 30)

Time: 8:40 – 16:30 (GMT+3)

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

Spark and HADOOP

Lidor Gerstel

Centerity

Read More »

ONNX runtime to serve AI models

Mauro Bennici

You Are My Guide – GhostWriterAI

Read More »

An introduction to FluxLang

Riccardo Tommasini

University of Tartu – Titania Project OÜ

Read More »

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
Creating a Dwh From Scratch to Analyze 11 Million Kilometers Worth of Bike Rides
Sebastian Mehldau
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) 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