BIG DATA CONFERENCE

EUROPE 2022

November 21-24

Vilnius and Online

SCHEDULE

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