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ABOUT THE CONFERENCE
Big Data Conference Moscow is a conference with technical talks in the fields of Big Data, High Load, Data Science and Machine Learning.
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!
WHY TO ATTEND?
Meet 30+ international speakers who work in top data-driven companies.
Join 4 technical tracks that cover the most important and up-to-date aspects of Big Data, including deep learning, real-time stream processing, data science, predictive analytics and cloud.
Hear carefully selected purely technical and independent content.
Network with 350+ participants from various companies that use Big Data in production.
Select among 5 full-day technical pre conference Workshops.
TRACKS
SPEAKERS
WORKSHOPS
ATTENDEES
CALL FOR PAPERS
Have your say - speak at the conference!
Submissions are welcomed from Developers, IT Professionals, IT Managers, Consultants and especially those who would like to experience themselves as trainers.
BECOME A SPONSOR
Big Data Moscow conference is a great opportunity to meet and network with a thriving, growing and exciting testing community. We invite you to participate in the creation of this unique event through financial contributions and associate your brand with this high-level conference.
To become a sponsor, please take a look at the available sponsorship plans or contact tatjana@bigdatadays.ru.
SPEAKERS
KEYNOTE
SPEAKERS
Miel Hostens
UGhent, Belgium
Pablo Doval
Plain Concepts, UK
Flow Bohl
Bloomberg New Energy Finance, UK
Hilda Kosorus
Runtastic, Austria
Alexey Anikin
IBM, Russia
Máté Marjai
The Payment Works, Ireland
Krzysztof Adamski
ING, Poland
Andrei Bashchenko
Russian Post, Russia
Nelson Arapé
Spotify, Sweden
Vladimir Shtanko
Rambler Group, Russia
CONFERENCE SCHEDULE (October 11)
Simultaneous interpretation
Big Data Moscow – the only international conference of that type that provides simultaneous interpretation from Russian to English and from English to Russian.
*Conference program might change.
Simultaneous interpretation will be provided in Hall 1, Hall 2.
| Hall 1 | Hall 2 | Hall 3 | Hall 4 | |
|---|---|---|---|---|
| 09:00 - 10:00 | ||||
| 10:00 - 10:55 | OPENING KEYNOTE: A Novel Approach to Data Mining And Prediction Modelling in Dairy Cows (ENG) Hall 1
|
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| 11:05 - 11:50 | Managing High Load on Low Budget (ENG)Máté Marjai Hall 1 Erlang Elixir |
Look-a-likes – How Internet Giants Reach the Most Relevant Users at Scale (ENG) Moran Gavish Hall 2 Online Marketing |
Data Analytics Platform or How to Make Data Science in a Box Possible (ENG)Krzysztof Adamski Hall 3 Kubernetes Spark
| Spring RabbitMQ for High load (ENG)Martin Toshev Hall 4 Spring RabbitMQ
|
| 11:50 - 12:05 | ||||
| 12:05 - 12:50 | Real-Time Data Processing at RTB House – Architecture & Lessons Learned (ENG) Bartosz Łoś Hall 1 Architecture |
To be announcedHall 2 |
Lie to Me… or Demystifying Spark Accumulators (RU)Sergey Zhemzhitsky Hall 3 Hadoop Spark ETL |
Simulation-Driven Digital Twins (RU)Pavel Bruk Hall 4 Digital Twin Simulation IoT |
| 12:50 - 14:00 | ||||
| 14:00 - 14:45 | Multi-tenant Deep Learning and Streaming as-a-Service with Hopsworks (ENG)Theofilos Kakantousis Hall 1 Deep Learning Streaming TensorFlow Spark |
Anomaly Detection in Telecommunications (ENG) Valentina Djordjevic Hall 2 Isolation Forests Autoencoders |
Machine learning in the film industry (RU)Vladimir Shtanko Hall 3 Computer Vision ML |
Analysis of the effectiveness of advertising videos using deep learning methods and large data (RU)Mark Popov Hall 4 Deep Learning |
| 14:55 - 15:40 | The events must flow: lessons learnt from evolving the Spotify’s event delivery system (ENG)Nelson Arapé Hall 1 Event Delivery DevOps |
Data Science for Industrial Sector R&D and Industrial Solutions (RU)Alexey Anikin Hall 4 Industrial Sector |
Geolocation Role in Big Data Concept (RU)Victor Rudoy Hall 3 Geolocation |
Holistic Customer Profiles: Bring the Meanings Back to Marketing Data (RU)David Vachadze Hall 2 |
| 15:40 - 16:00 | |
|||
| 16:00 - 16:45 | Data Orchestration with Apache Airflow (ENG)Gerard Toonstra Hall 1 ETL Airflow Architecture Agile |
Artifitial Intelligence in the field of Business Travel. IT Products development using Big Data Analysis (RU)Varvara Novozhilova & Jakov Goldfarb Hall 2 Travel Analytics Chat Bots |
Geospatial Analytics at Scale (ENG)Milos Milovanovic Hall 3 |
Predictive analytics in the management of (informational)-technological landscape (RU)Nikita Kardashin Hall 3 |
| 16:55 - 17:40 | The Future of Data Visualization (ENG)Flow Bohl Hall 1 Visualization Dataviz |
Embracing Polyglot Persistence Via CQRS (RU)Vladik Khononov Hall 2 Polyglot NoSQL CQRS |
Machine Learning for Agriculture today: Agronomist 2.0 is coming (RU)Anna Kudinova Hall 3 Agriculture ML |
Take Control over Your KPIs (ENG)Hilda Kosorus Hall 4 KPIs Time Series Forecasting Trend Change Detection Parameter Optimization |
| 17:50 - 18:35 | Case Study: Big Data Platform for Russian Post on Hadoop stack (RU)Andrei Bashchenko Hall 1 Hadoop Spark Kafka Vertica |
To be announcedHall 2 |
Functional Programming in Serverless World (ENG)Wojciech Gawroński Hall 3 AWS Serverless Functional Programming |
Realtime Blockchain Analytics (RU)Aleksey Studnev Hall 4 Blockchain Cryptocurrency Ethereum |
| 18:40 - 19:30 | CLOSING KEYNOTE: A look at the past and future of AI (ENG) Hall 1 |
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| 19:30 - 21:30 | |
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Confirmed talks
The Future of Data Visualization
Flow Bohl
Geospatial Analytics at Scale
Milos Milovanovic
Managing High load on Low Budget
Máté Marjai
Data Analytics Platform or How to Make Data Science in a Box Possible
Krzysztof Adamski
Data Orchestration with Apache Airflow
Embracing Polyglot Persistence Via CQRS
Realtime Blockchain Analytics
Simulation-Driven Digital Twins
Lie to Me… or Demystifying Spark Accumulators
Machine Learning in the Film Industry
Multi-tenant Deep Learning and Streaming as-a-Service with Hopsworks
Theofilos Kakantousis
Introduction to Deep Learning
Pablo Doval
Spring RabbitMQ for High load
Martin Toshev
Geolocation Role in Big Data Concept
Victor Rudoy
Anomaly Detection in Telecommunications
Valentina Djordjevic
Take Control over Your KPIs
Using Arbitrary ML Models for Big Data Classification
Functional Programming in Serverless World
Case Study: Big Data Platform for Russian Post on Hadoop stack
Artifitial Intelligence in the field of Business Travel. IT Products development using Big Data Analysis
Simultaneous interpretation
Big Data Moscow – the only international conference of that type that provides simultaneous interpretation from Russian to English and from English to Russian.
WORKSHOPS
The workshops will take place on Wednesday, October 10, the day before the Conference, at Deworkacy.
Simultaneous interpretation will not be provided during workshops.
Each participant should be equipped with a personal computer.
#1
Essentials for Apache Hadoop and Hadoop Ecosystem
(RU)
Ernestas Sysojevas
DATA MINER, Lithuania
Learn how Apache Hadoop addresses the limitations of traditional computing, helps businesses overcome real challenges, and powers new types of big data analytics. This workshop introduces Apache Hadoop ecosystem…..
#2
Traffic Management
with Elixir
(ENG)
Máté Marjai
The Payment Works, Ireland
Learn the basics of traffic management (literally and figuratively) with a fun day of Elixir! We’ll get familiar with the syntax of Elixir, how to get started in general with a new language. Along the way we’ll touch on the origins of Elixir, and basics of Erlang OTP…..
#3
Time Series
Forecast
(ENG)
Valentina Djordjevic
Things Solver, Serbia
This workshop will cover basic concepts of time series analysis, like time series decomposition, stationarity analysis, trend and seasonality smoothing. Afterwards, some of the most popular algorithms used for time series forecasting will be ….
#4
Generating Business Value from A/B Testing
(ENG)
Hilda Kosorus
Runtastic, Austria
A/B testing can be a highly valuable tool when making data-driven business decisions. It is easy to use and efficient in terms of resources. However, it can also be very easily misused and, when not conducted properly, resources invested in experim…
#5
Amazon SageMaker
in Practice
(ENG)
Wojciech Gawroński,
Paweł Pikuła,
Sebastian Feduniak
Pattern Match, Poland
At re:Invent in 2017 Amazon announced new service – SageMaker, that allows you to build, train and deploy ML models at any scale. Sounds fantastic, but is it true? We challenged the marketing taglines and decided to test a specific use case which…
TICKETS
For more information please contact us by email at tickets@bigdatadays.ru or by phone +7 (966) 378-54-44.
OUR UPCOMING CONFERENCES
TestCon Moscow 2019 on April 2-4 | Software Testing and QA Conference | www.testconf.ru
SPONSORS
MEDIA PARTNERS
Red October
Bersenevskaya Haberezhnaya 6с3
Moscow, Russia, 119072