Agenda 2020

February 27, 2020

 

We are doing our best to create the agenda as soon as possible. We want to make it appropriate to every Participant, so we will build 5 simultaneous sessions:

8.00 - 9.00

Registration and welcome coffee

9.00 - 9.15

Conference opening

Przemysław Gamdzyk

CEO & Meeting Designer, Evention

Adam Kawa

CEO and Co-founder, GetInData

9.15 - 10.45

Plenary Session

10.45 - 11.15

Coffee break

11.15 – 15.30 Simultaneous sessions

Architecture, Operations and Cloud

This track is dedicated to architects, administrators and experts with DevOps skills who are interested in technologies, techniques and best practices for planning, building, installing, managing, containerising and securing their Big Data infrastructure in enterprise environments – both on-premise and the cloud.

Data Engineering

 

 

This track is the place for engineers to learn about tools, techniques and battle-proven solutions to collect, store and process large amounts of data. It covers topics like data collection and ingestion, ETL, job scheduling, metadata and schema management, distributed processing engines, distributed datastores and more.

Streaming and Real-Time Analytics

 

This track covers technologies, strategies and valid use-cases for building streaming systems and implementing real-time applications that enable actionable insights and interactions not previously possible with classic batch systems. This includes solutions for data stream ingestion and applying various real-time algorithms and machine learning models to process events coming from IoT sensors, devices, front-end applications and users.

Artificial Intelligence and Data Science

This track includes real-world case studies demonstrating how data & technology are used together to address a wide range of complex problems in the domain of machine learning, artificial intelligence and data science. It also covers topics related to prototyping and operationalizing ML/AI models in production, data visualisation and A/B tests.

Data Strategy and ROI

 

This track is a quite new thing at BigData Techology Warsaw Summit. It is aimed at data and business professionals who are interested in learning how data and analytics can be used to generate growth, value added, and positive financial impact. It will contain presentations about real-world use cases that cover useful data-focused solutions, new business models and various data monetization strategies. Since most of the Big Data projects are difficult to complete on time and in budget, presentations will also explain necessary technical, cultural and leadership aspects that are key to successful Big Data initiatives at enterprises – also during the phase of proper preparation and ensuring longer term sustainability of the project – avoiding wasting money and getting a positive return on investment (ROI).

11.15 - 11.45

To be confirmed

11.15 - 11.45

To be confirmed

11.15 - 11.45

Creating an extensible Big Data Platform for advanced analytics - 100s of PetaBytes with Realtime access

Reza Shiftehfar

Engineering Management & Leadership, Uber

11.15 - 11.45

Building Recommendation Platform for ESPN+ and Disney+. Lessons Learned

Building Recommendation System is a real challenges, as one needs to deal with many complex problem, like: volume and variety of data, scalability, different recommendation methods,ability for experimentation, changing environments and proper evaluation of recommendation algorithms. During presentation we will go through problems and lessons learned that our team dealt with while working on Recommender System providing personalized recommendations for ESPN+ and Disney+.
Show more
Show less
Keywords: #recommendersystems  #ML #cloud #experimentation

Grzegorz Puchawski

Data Science and Recommendation, Disney Streaming Services

11.15 - 11.45

To be confirmed

11.45 - 11.50

Technical break

11.50 - 12.20

Replication Is Not Enough for 450 PB: Try an Extra DC and a Cold Store

When you have 450 PB of Hadoop data you need a cold storage backup. Protect against operator error and file corruption. How did we choose our cold backup technology. How we help our users to backup all the required data but not all data. How we copy to the cold backup system and restore when required.
Show more
Show less
Keywords: #Hadoop #datasecurity #resilience #in-house #storage

Stuart Pook

Senior Site Reliability Engineer, Criteo

11.50 - 12.20

Data Platform at Bolt: challenges of scaling data infrastructure in a hyper growth startup

Introduction of Data Team at Bolt, Initial Data Warehouse setup and its limitations, Biggest challenges to solve, Data Infrastructure evolution and its trade-offs, Learned lessons and conclusions
Show more
Show less

Keywords:  #aws #datalake #datawarehouse #preprocessing #machinelearning

Łukasz Grądzki

Engineering Manager, Bolt

11.50 - 12.20

Interactive Analytics at Alibaba

In this talk, I will talk about solutions for realtime data warehouse. I will cover the typical scenarios and its architecture. We use Flink to do realtime data process. The output is stored into a realtime data warehouse which we developed in-house at Alibaba. It provides efficient storage as well as performant query abilities.
Show more
Show less

Yuan Jiang

Senior Staff Engineer, Alibaba

11.50 - 12.20

Building a Factory for Machine Learning at Spotify

Spotify has been using Machine Learning in our product for nearly a decade, but it’s only been in the last few years that we’ve invested in building tools aimed at making the lives of internal Machine Learning practitioners easier and more productive. In this talk I’ll cover the main problems our Machine Learning Infrastructure teams have been focusing on, highlight some of the ML used in the Spotify product, and talk about the Machine Learning Productivity tools our Machine Learning Platform team has produced internally.

 

Amongst these tools are:

* A feature store that standardizes feature production and encourages collaboration between teams

* Kubeflow Pipelines as an end-to-end pipeline authoring tool

* Tensorflow and Tensorflow Extended as a toolset for the different phases of Machine Learning Development

* Libraries that ease the Model Serving process

* A UI that provides an overview of ML Projects at Spotify

Show more
Show less

Josh Baer

Product Lead, Machine Learning Platform, Spotify

11.50 - 12.20

The Big Data Bento: Diversified yet Unified

What is a Bento? Evolution of data platforms: packaged -> build your own -> cloud. Why did people started building their own platforms using solutions outside the Hadoop ecosystem (e.g. Spark, Flink, Druid)? Why are people moving to the cloud now? Faster time to value through a diversified yet unified platform.
Show more
Show less
Keywords: #bigdatabento #cloud #unifiedanalyticsplatform #unifieddataanalyticsplatform #spark

Paulo Gutierrez

Solutions Architect, Databricks

12.20 - 12.25

Technical break

12.25 - 12.55

To be confirmed

12.25 - 12.55

To be confirmed

12.25 - 12.55

Adventure in Complex Event Processing at telco

We would like to share our experience of 3 years doing Complex Event Processing on BigData solution at Orange Polska. What difficulties we did overcome using Kafka, Storm, integration with non-bigdata systems, security, business and technical monitoring (Influx/Grafana/Superset), multiple sources with different formats JSON, XML, etc.
Show more
Show less

Keywords:

Jakub Błachucki

Big Data Engineer, Orange

Maciej Czyżowicz

Technical Leader for Analytics Stream, Orange

Paweł Pinkos

Big Data Engineer, Orange

12.25 - 12.55

To be confirmed

12.25 - 12.55

To be confirmed

12.55 - 13.50

Lunch

13.50 - 14.20

To be confirmed

13.50 - 14.20

Presto @ Zalando: A cloud journey for Europe’s leading online retailer

At Zalando we have been using Presto as an analytical system for both ad-hoc and ETL use cases for over three years. This talk will describe how Presto
changed in that time and how the Zalando setup changed with it. What are the biggest learnings, and how are we providing fast SQL capabilities to our analysts over an ever growing S3 data lake in a multi tenant environment with fine grained access control.
Show more
Show less

Keywords:  #CloudAnalytics #Presto #DataVirtualization #SQL-on-Hadoop #DWH

Wojciech Biela

Co-founder & Senior Director of Engineering, Starburst

Max Schultze

Data Engineer, Zalando SE

13.50 - 14.20

To be confirmed

13.50 - 14.20

To be confirmed

13.50 - 14.20

Omnichannel Personalization as example of creating data ROI - from separate use cases to operational complete data ecosystem

Journey of Orange Polska from single use cases to advanced eco-system of data. Example of implementing the environment to personalize realtime customer relationships. Technology mix of personalization environment and examples of Use Cases. How to bridge technical and business perspective. Organizational approach.
Show more
Show less

Keywords:  #ROI #real-timeomnichannelpersonalization #scalingdataecosystem #businessengagement #harvesting

Tomasz Burzyński

Business Insights Director, Orange

Mateusz Krawczyk

Personalization Solutions Product Owner, Orange

14.20 - 14.25

Lunch

14.25 - 14.55

DevOps best practices in AWS cloud (Big Data stack)

Presentation will concentrate on three topics: Distributed data processing, costs optimisation and security. In particular we are going to introduce EMR as big data service, SageMaker for model training, LakeFormation for managing granular access to data. All of that developed as infrastructure as a code in secure environment, automated with best devops practices and keeping within reasonable costs inside AWS cloud. You will know how to control expenses and pay only for well utilised services in serverless environment.

Keywords:  #aws_cloud #devops #best_practices #infrastructure_as_a_code

Adam Kurowski

Senior DevOps, StepStone Services

Kamil Szkoda

DevOps Team Leader and Product Owner , StepStone Services

14.25 - 14.55

To be confirmed

14.25 - 14.55

Monitoring & Analysing Communication and Trade Events as Graphs

Graph Analytics on Trade Events & Communication Events, Streaming Transactions as Edges, Visualising Graphs in Kibana
Show more
Show less
Keywords:  #graphAnalytics #transactionProcessing #FlinkGelly #Elasticsearch #Kibana

Christos Hadjinikolis

Senior Consultant, Lead ML Engineer, Data Reply UK

14.25 - 14.55

Neural Machine Translation: achievements, challenges and the way forward

Current state of the art in Machine Translation: what is possible, what is not possible, what are the common misconceptions about applying it to real world problems in companies, and the many challenges connected to performance testing. Adversarial examples: a real danger which fools Machine Translation models (and all other models, in fact!), that we need to know and take steps against. Sentence level vs document level: can Machine Translation models retain document coherence and meaning while translating whole documents? Technical issues connected to applying Machine Translation in companies.
Show more
Show less

Keywords:  #machinetranslation #deeplearning #adversarialexamples #datascience

Katarzyna Pakulska

Data Science Technology Leader, Findwise

Barbara Rychalska

Senior Data Scientist and Data Science Section Leader, Findwise

14.25 - 14.55

Data Science @ PMI – Journey from business problem to the data product industrialization

Introduction to Data Products, CI & CD, Portfolio Optimization, Modus Operandi and Agile Data Science on the Data Ocean, Best Practices: Kubernetes, Docker Containers, Project Templates, Programming Style Standards, Airflow etc., Reproducible Data Science
Show more
Show less

Keywords:  #UseCase #CI/CD #BestPracticesforData Science #DataProduct #Reproducibleresearch

Michał Dyrda

Senior Enterprise Data Scientist, Philip Morris International

Maciej Marek

Enterprise Data Scientist , Philip Morris International

14.55 - 15.00

Lunch

15.00 - 15.30

How to send 16,000 servers to the cloud in 8 months?

Case study based on the migration of 5 DC of one of the largest OpenX ad-exchange platforms to the GCP public cloud. Google’s role in the migration process and how to prepare for such migration. Lessons learnt
Show more
Show less
Keywords:   #Openx #gcp #scale #adtech #migration

Marcin Mierzejewski

Engineering Director, OpenX

Radek Stankiewicz

Strategic Cloud Engineer, Google Cloud

15.00 - 15.30

Optimize your Data Pipeline without Rewriting it

It is not fast enough! – one of the more common responses to a data engineer when putting a data pipeline in production. We will see a structured process to optimize our batch pipelines. Make our performance tuning process data-driven. Examples from real-world cases where its been applied.
Show more
Show less
Keywords:  #data-driven #optimize #data-pipeline #operation #improvement

Magnus Runesson

Senior Data Engineer, Tink

15.00 - 15.30

Flink on a trip - a real-time car insurance system in a nut(shell)

Automotive is 135 years old. Car insurance concept is 90 years old. Old enough to change the model of car insurance using the state of the art IT systems.The presentation shows innovative car insurance system built with Apache Flink. It goes in depth of the stream technology behind the framework. It presents a use case of detection of a car trip, that might looks trivial at glance, but expose some traps at the detailed view of the presented solutions. After this presentation you will learn some car insurance history and vistas of this industry, but you will learn definitely more on advantages and limitations of Apache Flink for real time streaming systems on example of the use case on the production system at Humn.AI.
Show more
Show less

Wojciech Indyk

Streaming Analytics and All Things Data Black Belt Ninja, Humn.ai

15.00 - 15.30

Reliability in ML - how to manage changes in data science projects?

Why management of changes in data science projects is so difficult? What kind of problems with simultaneous tracking of all elements of a data science project (e.g. code, data extracts, model) may appear? The presentation is going to answer these questions and discuss different approaches to implementation of revision control in data science projects. The approaches will be presented and compared on real use-cases and scenarios.
Show more
Show less
Keywords: #datascience #datamanagement #revisioncontrol #datapipeline

Kornel Skałkowski

Senior AI Engineer, Consonance Solutions

14.25 - 14.55

Using data to build Products

 It’s quite challenging to get ideas for new products and build them from scratch. I will be sharing my experiences on how data and machine learning helped us at Booking.com in finding what to build from scratch to solve a user problem with efficiency and scalability.

Ketan Gupta

Product Leader, Booking.com

15.30 - 16.00

Coffee break

16.00 – 17.25 Roundtables sessions

16.00 - 16.05

Intro

Parallel roundtables discussions are the part of the conference that engage all participants. It has few purposes. First of all, participants have the opportunity to exchange their opinions and experiences about specific issue that is important to that group. Secondly, participants can meet and talk with the leader/host of the roundtable discussion – they are selected professionals with a vast knowledge and experience.

There will be 2 rounds of discussion, hence every conference participants can take part in 2 discussions

 

16.05 – 16.45    1st round

16.50 – 17.30    2nd round

 Snorkel Beambell – Real-time Weak Supervision on Apache Beam 

Suneel Marthi

Principal Technologist - AI/ML, Amazon Web Services

Real-life machine learning at scale using Kubernetes and Kubeflow

How to build a machine learning pipeline to process 1500 TB data daily in a fast and cost-effective way on Google Cloud Platform using Kubeflow? How to serve TensorFlow model with almost 1M requests per second and latency < 10ms on Kubernetes? Is Kubernetes and Kubeflow ready to serve data scientists?

Michał Bryś

Data scientist, OpenX

Michał Żyliński

Customer Engineer, Google

Managing workflows at scale

How to build and maintain thousands of pipelines in the organisation? What are the biggest pain points in orchestrating hundreds of ETLs? What open source and managed solutions are available?

Paweł Kupidura

Data Engineer, Bolt

Practical application of AI

Industry 4.0 and AI – are we ready for the 4th industrial transformation? Who should be the beneficiary of the Industry 4.0? Key barriers to the implementation of AI projects in organizations. Real cases of AI in Industry.

Natalia Szóstak

Head of R&D, TIDK

The need for explainable AI
With the spread of AI-based solutions, more and more organizations would like to understand the reasons for system decisions. It’s especially interested in regulated industries. The session will cover so-called white-box methods, as well as modern approaches to AI explainability which allow understanding more complex models.

Kacper Łukawski

Data Engineer and Tech Lead, Codete

A new reference point for natural language processing

Sebastiano Galazzo

CTO, eeve.ai

Deploying ML models in real-time using stream processors

Andrea Spina

Team Lead Data Engineer, Radicalbit

Data discovery – building trust around your data

Damian Warszawski

, ING

OTHER ROUNDTABLES TOPICS

  • Challenges of building a modern & future-proof data processing platform
  • AI, Machine Learning and Big Data in the enterprise
  • Data visualization – how to visualize large, complex and dirty data and what tools to use
  • Choosing a right BI solution for a large data and a quick response time
  • Analytics and Customer Experience Management on top of Big Data
  • IoT in production – use-cases, data, tools and challenges
  • Stream processing engines – features, performance, comparison
  • Being an efficient Data Engineer. Tools, skills and ways of learning
  • Data privacy, personal integrity and GDPR
  • Data ingestion technologies, techniques and challenges
  • Big Data – the cloud way
  • From on-premise to cloud: an end to end cloud migration journey
  • Big Data on Kubernetes

17.30 - 17.45

Coffee break

17.45 - 18.15

Panel discussion

Host:

Adam Kawa

CEO and Co-founder, GetInData

18.15 - 18.30

Closing & Summary

Przemysław Gamdzyk

CEO & Meeting Designer, Evention

19.00 - 22.00

Networking party for all participants and speakers

At the end of the conference we would like to invite all the attendees for the informal evening meeting at “Dekada” Club , which is located at the Grójecka 19/25, 02-021 Warszawa.