February 23, 2021

BIG DATA TECHNOLOGY WARSAW SUMMIT WORKSHOPS DAY 1

9:00 – 13:00

3 WORKSHOPS - DAY I

19.00 - 21.00

EVENING MEETING
(speaker’s presentation + discussion)

Pandemic, data and analytics – how do we might know what happens next with Covid-19?

Special evening meeting prior to the BigData Technology Warsaw Summit.

There are smart people and great research teams working on the forecasting models for pandemic developments. What data do they use, which models, how it could be approached, how accurate it is, what are the major challenges here? How does the bigdata community contribute to fighting the Covid-19 disease? These are the questions we would like to address during this unique online meeting. We have invited the very special guests (including experts from MOCOS and ICM UW) – everyone is encouraged to participate in discussion and ask questions!

♦ What makes the field of pandemia modelling and simulation so interesting and challenging? How to predict risks using available data and proper modelling?

♦ How is it done – large scale geographical microsimulation model for pandemics?

♦ What can we do for a better pandemic forecasting and predicting efficiency of various countermeasures to slow it down? Is an AI/ML enough?

In the meeting agenda:

18.45 – 19.00

Networking online

19.00 – 19.05

Opening remarks

What makes the field of pandemic modelling and simulation so interesting and challenging.

CEO & Meeting Designer
Evention

19.05 – 19.25

How it is done – large scale geographical microsimulation model for pandemics.

Project Manager
ICM University of Warsaw

19.25 – 19.30

Short Q&A

19.30 – 19.45

What can we do for better pandemic forecasting and predicting efficiency of various countermeasures to slow it down. Is AI/ML any good for it.

Founder
MOCOS

19.45 – 20.00

Computational side of the algorithm used by MOCOS Group

Software Researcher and Data Sciencist
MOCOS Group

20.00 – 20.10

Q&A

20.10 – 20.30

Collaborative forecasting of COVID-19: Assembling, comparing and combining short-term predictions.

Postdoctoral Researcher
Heidelberg Institute for Theoretical Studies (HITS), Karlsruhe Institute of Technology (KIT)

20.30 -21.00

Open discussion for everybody

February 24, 2021

BIG DATA TECHNOLOGY WARSAW SUMMIT WORKSHOPS DAY 2

9:00 – 13:00

3 WORKSHOPS - DAY II

19.00 - 20.00

EVENING MEETING
(speaker’s presentation + discussion)

All about the jobs in the BigData industry in the (post)-COVID-19 world. Special evening meeting prior to BigData Technology Warsaw Summit. Let’s talk about the current situation at the job market for BigData Professionals. What is hot and what is not? Who is now searched by the employers and how do they do it? What are the expectations and requirements? Does the pandemic change a lot here at jobs landscape? What does the ‘remoteness’ of work makes difference? What are the future trends in the way we work together? During the meeting there will be a discussion with top managers from companies actively acquiring new talents on the BigData market  as well as managing Big Data teams.

The meeting is organized in partnership with ING Tech Poland.

In the meeting agenda:

19.00 - 19.10

Welcome Address Speech

Senior Area Lead of Platform Engineering and Network Area (IT Director)
ING Tech Poland

19.10 - 19.30

Data in the labour market – salaries and trends

Head of IT Perm Recruitment, Key Accounts Director
Hays Poland

19.30 - 20.00

Panel discussion with representatives of BigData or AI enterprises recruting technical people

Data Analytics Platform Lead
ING Banking Technology Platform, ING
 
Managing Director of Data Science and Recommendations
Disney Streaming Services
 
Head of Big Data and Machine Learning
Allegro

Panel chair:

CEO & Meeting Designer
Evention

February 25, 2021

BIG DATA TECHNOLOGY WARSAW SUMMIT DAY 1

12.30 - 13.00

TIME FOR NETWORKING ONLINE

13.00 - 13.10

CONFERENCE OPENING

CEO and Co-founder
GetInData
CEO & Meeting Designer
Evention
PLENARY SESSION

13.10 - 13.35

5 big data trends that redefine Edge to AI journey

During the session we will discuss the key trends redefining the way companies manage data and analytics lifecycle. The presenters will explain:
♦ the importance of disaggregation of compute and storage,
♦ advancements in stateful processing in Kubernetes,
♦ growing role of cloud and real-time processing for businesses in Poland.

Keywords: #DataArchitecture #Kubernetes #Streaming #MachineLearning #Cloud #BusinessAgility

13:35 - 13:50 Q&A Session

Chief Technology Officer
3Soft
Senior Solutions Engineer
Cloudera

13.35 - 14.00

High-Performance Data Analytics in a Hybrid and Multi-Cloud World

Many enterprises are re-thinking their data analytics strategy. Some plan to stay on-prem for GDPR reasons. Others are all in for full-cloud but want to stay agnostic. And still others require a hybrid approach: run certain workloads on-prem and move others to the cloud to capitalize on cloud economics. With object stores emerging as the main winners in the post-Hadoop era for cost-effective storage, enterprises are adopting them independently from the evolution of their EDW, Data Lakes, and Data Science platforms. Finally, there’s a convergence movement underway, causing enterprises to unify their data analytics platforms (EDW, Data Lakes and Data Science platforms) and supporting the broadest deployment models. Join us for this session to learn how Vertica can support your vision with a new era of data analytics in a hybrid and cloud-agnostic fashion, supporting a variety of object store technologies.

14:00 - 14:15 Q&A Session

Chief Product Officer
Vertica
SIMULTANEOUS SESSIONS PART I

14.05 - 14.35

Architecture Operations & Cloud

Data Engineering

MLOps

AI, ML and Data Science

ntArchitecture Operations & Cloudn
The Scalable Gaming Analytics Pipeline at Outfit7: The Next Generation Keywords: #googlecloud #bigquery #events #scalable 14.35 - 14.50 Q&A session
Principal software engineer
Outift7
ntData Engineeringn
Data Quality with 100+ PB: Solved Challenge at Criteo Keywords:#dataquality #dataplatform #metrics #hive 14.35 - 14.50 Q&A session
Data Engineer
Criteo
Master Data Manager
Doctolib
nMLOpsn

MLOps journey in H&M

Keywords: #MLOps #AIAtScale #MachineLearning #Engineering #DataScience

14.35 - 14.50 Q&A session

Competence lead , Machine learning engineer
H&M
nAI, ML and Data Sciencen

Building recommender systems: from algorithms to production

Keywords: #machinelearning, #recommendations, #production, #architecture

14.35 - 14.50 Q&A session

Strategic Consultant

14.40 - 15.10

Architecture Operarations & Cloud

Data Engineering

MLOps

AI, ML and Data Science

ntArchitecture Operarations & Cloudn

Welcome to MLOps candy shop and choose your flavour!

Keywords: #MachineLearning #MLops #FeatureStore #Kubeflow #Kedro #OpenSource #Feast

15.10 - 15.25 Q&A session

Google Certified Professional - Cloud Architect
GetInData
Senior Machine Learning Engineer
GetInData
ntData Engineeringn

Popmon - population shift monitoring made easy

Keywords: #

15.10 - 15.25 Q&A session

Chief Data Scientist
ING WBAA
Data Scientist
ING WBAA
nMLOpsn

ModelOps – Operationalizing Modern Analytics & AI

Keywords:#AI #Analytics #ML #MachineLearning #DeepLearning #ModelOps #DevOps #XOps #MLOps

15.10 - 15.25 Q&A session

Senior Business Solutions Manager
SAS Institute
Senior Pre-Sales Solutions Architect
SAS Institute

nAI, ML and Data Sciencen

Thrive in the Data Age how Siemens and BMW Group leverage machine learning for cybersecurity, operations and business use cases using Splunk 

Keywords: #datascience #ai #machinelearning #deeplearning #cybersecurity #operations #analytics

15.10 - 15.25 Q&A session

Principal Machine Learning Architect
Splunk

15.10 - 15.15

TECHNICAL BREAK

ROUNDTABLE SESSIONS PART I

15.15 - 16.05

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 roundtable sessions, hence every conference participants can take part in 2 discussions, one each day of the conference.
You can choose among such roundtable subjects:

1.  Managing a Big Data project – how to make it all work well together?

Head of Product of Data Platform Stream
Kambi

 

2. Big Data on Kubernetes

Kubernetes - it was created for ‘Stateless’ apps but not for ‘Statefull’…so why we should consider this for BigData?. ‘Statefull’ app together with persistent volume support, but many databases are not supporting it yet – so how Companies can overcome this challenge? Is the Spark + HDP is the only reasonable solution for data transformation on K8s? what about other solutions? Does it make sense to consider any other?. Lets refer to a Telco 5G – requirements – all must go on K8s – but where Hadoop is being replaced by Object Storage solution that could be orchestrated by K8s - to simply the overall architecture. Finally, so what about K8s On-Prem/On-Cloud – to which direction is a way to go?

Solution Engineer
Vertica

 

3. Data discovery – building trust around your data

Building trust in data falls under one of the four main pillars of a good data setup - Data Governance. In this roundtable we will do a quick overview of the 4 pillars and how to go about building a trustworthy setup. Topics will cover data lineage, accuracy and completeness vs cost, toolkit available, tactics on recording 'truth' vs business interpretation and how to build a setup that will improve over time, rather than degrade.

Senior Solutions Design
Tesco Bank

 

4. Stream processing engines – features, performance, comparison

Stream processing and real-time data processing is nowadays more and more popular and important. There are a lot of use cases: data capturing, marketing, sales and business analysis, monitoring and reporting, troubleshooting systems, and real-time machine learning like customer/user activity (personalization and recommendation), fraud detection, real-time stock trades. There are a lot of stream data processing frameworks, like Spark (Structured) Streaming, Flink, Storm, Amazon Kinesis, … Let’s talk about them, try to compare, list pros and cons in terms of various problems and challenge like throughput, performance, latency, system recovery and so on.

Solution Architect
BAE Systems Applied Intelligence

 

5. From on-premise to the cloud: an end to end cloud migration journey

Data Analyst
GetInData

 

6. MLOps - how to support the life-cycle of ML models

MLOps is a hot trend concerning the end-to-end lifecycle of ML models from conception to model building and monitoring to decommissioning. How do you govern this lifecycle? Which methodologies and solutions are worth using? What mistakes should be avoided? Let's exchange experiences!

Principal Data Scientist at Samsung SRPOL, associate professor
Warsaw University of Technology

 

7. Transactional Data Lakes with Apache Spark (and Delta Lake, Apache Hudi and Apache Iceberg)

There is a trend in big data management space to add features we all know from relational databases, most notably ACID transactions and versioning. That's the main focus of open source projects Delta Lake, Apache Hudi and Apache Iceberg. They are storage layers on Hadoop DFS-like file systems and object stores that together with Apache Spark's capabilities allow building "reliable data lakes at scale". You're invited to discuss the pros and cons of each and how to use them effectively in our big data projects. All are equally welcome regardless of their experience and expertise. Let's share what we've already learnt and further deepen our understanding learning from others.

IT freelancer

 

8. Operationalizing Analytics – sharing experience and best practices

The promise and potential business value of analytics is endless, which is why companies have spent the last decade investing in the right people, data, processes, and enabling technology. Yet studies show that less than 50% of the best models get deployed, 90% of odels take more than three months to deploy and 44% of models take over seven months to be put into production.

Data Science & Engineering Team Leader
SAS
Analytical Consultant
SAS

 

9. Monitoring performance of ML models

Monitoring of the ML model running online on production data can be a challenge. Let's discuss what are the biggest difficulties and how to manage them. What kind of tools you are using to detect any problems with the input data and the results.

Chief Data Scientist
ING WBAA

 

10. We've got a model! What are the next challenges of deploying it at scale?

Training a good ML model is only the beginning of the journey. The next question is: how to integrate it with production systems robustly and effectively? Let's discuss your experience with deploying ML models challenges like continuous model training, training-serving skew, data drift, and model serving infrastructure.

Data Scientist / Machine Learning Engineer
OpenX

16.05 - 16.10

TECHNICAL BREAK

SIMULTANEOUS SESSIONS PART II

16.15 - 16.45

Data Engineering I

Data Engineering II

MLOps/ AI, ML and Data Science

Data Strategy and ROI

ntData Engineering In

Casting the Spell: Druid in Practice

Keywords: #BigData #ApacheDruid #RealtimeAnalytics #DataArchitecture #DataEngineering

16.45 - 17.00 Q&A Session

SVP R&D and GM Israel
Nielsen Identity
Principal Solutions Architect
Imply
ntData Engineering IIn

BigFlow – A Python framework for data processing on the Google Cloud Platform

Keywords: #gcp #python #dataflow #dataproc #bigquery

16.45 - 17.00 Q&A Session

Senior software engineer
Allegro
ntMLOps/ AI, ML and Data Sciencen

Training and deploying machine learning models with Google Cloud Platform

Keywords: #mlops #gcp #python #nlp #computervision

16.45 - 17.00 Q&A Session

Machine Learning Engineer
Sotrender
ntData Strategy and ROIn

How to optimize time needed to find and understand the data as a part of BigData project.

Keywords: : #dataplatform #datamanagement #businessdata  #AI #DataGovernance #WatsonKnowledgeCatalog #KnowYourData #AIClariteAssistant

16.45 - 17.00 Q&A Session

Solution Architect
Clarite Polska
Analytics & Automation Director
Clarite Polska

16.50 - 17.20

Data Engineering I

Data Engineering II

MLOps/ AI, ML and Data Science

Data Strategy and ROI

ntData Engineering In

Data lineage and observability with Marquez and OpenLineage

Keywords: #lineage #observability #dataops

17.20 - 17.35 Q&A session

CTO, Co-Founder
Datakin
ntData Engineering IIn
Top 5 Spark anti-patterns that will bite you at scale! Keywords: #java #bigdata #spark #hadoop 17.20 - 17.35 Q&A session
Software engineer, author, speaker and blogger
ntMLOps/ AI, ML and Data Sciencen

Causal Mediation Analysis in the E-Commerce Industry

Keywords: #causalinference #A/Btests #informationretrieval #searchmetrics #evaluation

17.20 - 17.35 Q&A session

Senior Staff Data Scientist
Causal Inference and Experimentation, Udemy
ntData Strategy and ROIn

Foundations of Data Teams

Keywords: #managment #data teams #data engineers #data scientists #operations

17.20 - 17.35 Q&A session

Data Engineer, Creative Engineer and Managing Director
Big Data Institute
PLENARY SESSION

17.25 - 17.55

The Journey to Data Cloud

Snowflake is the leading data platform for the cloud era. I will present its features as a modern data warehouse, uniquely exploiting the cloud capabilities to meet growing users' needs. Then I will discuss how it became the foundation of the Data Cloud, a revolutionary solution that opens the world's data to all organizations.

Keywords: #cloud #SQL #analytics, #scalability #datasharing #datawarehouse

17.55 - 18.10  Q&A session

Co-Founder
Snowflake

17.55 - 18.30

SUMMARY OF THE DAY, PRIZE GIVEAWAY AND A SURPRISE*!

CEO and Co-founder
GetInData
CEO & Meeting Designer
Evention

+ *? Live DJ performance especially for the participants of the meeting - DJ Michał Stochalski

He sets trends, creates new DJ sets and constantly improves his music skills, according to his motto "Excellence is earned throughout life". As a Video DJ he presents a combination of image and sound mixing live music with corresponding clips.

February 26, 2021

BIG DATA TECHNOLOGY WARSAW SUMMIT DAY 2

09.00 - 09.05

OPENING OF THE SECOND DAY

PLENARY SESSION

09.05 - 09.35

Fast growth iteration via A/B testing

09.35-9.50 Q&A Session
Senior Data Scientist
Atlassian
SIMULTANEOUS SESSIONS PART III

09.40 - 10.10

Architecture Operarations &Cloud

AI, ML and Data Science

AI, ML and Data Science II

Data Engineering

nArchitecture Operarations & Cloudn

Management of a cloud Data Lake in practice: How to manage 1000s of ETLs using Apache Spark

Keywords: #DataGovernance #DataLake #DataQuality #Cloud #ApacheSpark #Azure #DataBricks

10.10 - 10.25 Q&A Session

Principal Solution Architect for BigData Analytics & Engineering, North & Central Europe
DXC Luxoft
ntAI, ML and Data Sciencen

Building an analytics platform from scratch while developing production solutions on top of it.

Keywords: #ModelProductization #MachineLearning #FeatureStore #KubeFlow #DataScience #OpenSource #OnPremise

10.10 - 10.25 Q&A Session

Head of Advanced Analytics
GetInData
Data Analyst
GetInData
ntAI, ML and Data Science IIn

When HR meets Artificial Intelligence.

Keywords: #artificialintelligence #nlp #digitaltransformation #futureofwork #hcm

10.10 - 10.25 Q&A Session

CTO
Talent Alpha
ntData Engineeringn
AppDynamics platform with massively scalable big data infrastructure components to handle large numbers of events, metrics, and metadata. Keywords: #APM #EUM #IoT #BiQ #Analytics #ML #AI #BizDevOps #Kafka #AWS #AIOps  #AD #ATD 10.10 - 10.25 Q&A Session
AppDynamics, Cisco Architect for the EMEA region
Cisco

10.15 - 10.45

Architecture, Operation and Cloud

Streaming and Real-Time Analytics

AI, ML and Data Science

Data Engineering

ntArchitecture, Operation and Cloudn

Expanding your data & analysis ecosystem with public cloud

Keywords: #hadoop #spark #airflow #gcp #bigquery #composer #dataproc #data analysis

10.45 - 11.00 Q&A Session

Data Platform Engineer
Allegro
ntStreaming and Real-Time Analyticsn

Streaming SQL - Be Like Water My Friend

Keywords: #streaming #streamingsql #flink #dataflow #flinksql

10.45 - 11.00 Q&A Session

Expert Software Developer Analytics
InnoGames GmbH
ntAI, ML and Data Sciencen

Make it personal: reinforcement learning for mere mortals

Keywords: #personalization #ecommerce #vowpalwabbit #reinforcementlearning #opensource

10.45 - 11.00 Q&A Session

Lead Data Scientist
eBay Classifieds Group
ntData Engineeringn

Pragmatic approach to data quality at OLX

Keywords:#data #data-quality

10.45 - 11.00 Q&A Session

Head of Data Services & Operations
OLX Group

10.45 - 10.50

TECHNICAL BREAK

SIMULTANEOUS SESSIONS PART IV

10.55 - 11.25

Architecture Operarations & Cloud

Streaming and Real-Time Analytics

AI, ML and Data Science I

Data Strategy and ROI

ntArchitecture Operarations & Cloudn

Presto: SQL-on-Anything & Anywhere 

Keywords: #presto #sql #analyticsanywhere #azure

11.25 - 11.40 Q&A session

Co-founder and CTO
Starburst
Co-founder & Senior Director of Engineering
Starburst
ntStreaming and Real-Time Analyticsn

Complex event-driven applications with Kafka Streams

Keywords: #data-streaming #kafkastreams #schemaregistry #domainevents #evolutionaryarchitecture

11.25 - 11.40 Q&A session

Staff data engineer
Simply Business
ntAI, ML and Data Science n

How to build a state-of-the-art weather forecasting AI service

11.25 - 11.40 Q&A session

AI Research Engineer
Peltarion
ntData Strategy and ROIn

Big Data Instruments and Partnerships - Microsoft ecosystem update

11.25 - 11.40 Q&A session

Technical Lead for Strategic Partners & Startups ,CIS, Lead of Open Source Community
Microsoft Russia, Microsoft

11.30 - 12.00

Architecture Operarations & Cloud

Streaming and Real-Time Analytics

AI, ML and Data Science I

Data Strategy and ROI

ntArchitecture Operarations & Cloudn

AWS Spot instances price prediction - towards cost optimization for Big Data

Keywords: #TCO #CloudComputing #ARIMA #AWS #Spot

12.00 - 12.15 Q&A Session

Lead Solution Architect
Nowa Era
Chief Architect, Founder and CEO, Assistant Professor,
CogniTrek Corp, MAGIX.AI, Saints Cyril and Methodius University
ntStreaming and Real-Time Analyticsn

Evolving Bolt from batch jobs to real-time stream processing - migration, lessons learned, value unleashed

Keywords: #kafka #streaming #data #realtime

12.00 - 12.15 Q&A Session

Data Architect
Bolt
ntAI, ML and Data Science In

How NoMagic robots improve thanks to software 2.0 improvement cycle supported by an in-house data engine?

12.00 - 12.15 Q&A Session

Research Engineer
NoMagic
ntData Strategy and ROIn

How to plan the unpredictable? 7 good practices of organisation and management of fast-paced large-scale R&D projects

Keywords: #datascience #ai #machinelearning #agile #projectmanagement

12.00 - 12.15 Q&A Session

Principal Data Scientist
Pearson
Senior Technical Project Manager
Pearson

12.00 - 12.05

BREAK

ROUNDTABLE SESSIONS PART II

12.05 - 12.55

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

You can choose among such roundtable subjects:

1. Building a world-class Big Data team during the COVID-19 pandemic - recruiting, training, collaborating.

Last year forced us to change a lot in how we work. A lot of us had to switch to working/studying from home, some needed to freeze hiring, others - to redefine onboarding. As hard as last year was, it was also a time of innovation. Join the session to exchange lessons learned and ideas for building a world-class Big Data team leveraging “the new normal”. Everybody is welcome - the more diverse experiences the better.

Engineering Manager
Zendesk

 

2. Big Data on Kubernetes

Big Data DevOps Engineer
GetInData

 

3. Best tools for alerting and monitoring of the data platforms

Have you ever been woken up in the middle of the night by a screaming PagerDuty alert on your mobile, 99+ notifications on {YOUR_PIPELINE_NAME}_alerts Slack channel and tens of graphs in Grafana looking like an undreamt art of van Gogh? If yes, welcome, me too. For an engineer working on a Data Platform it is easy to create a new pipeline, a new dataset or add any new integration, especially now in the cloud era. But it is not easy to have a proper monitoring and alerting system ensuring that any potential issues/incidents can be solved as quickly as possible, so offering of our Data Platform is always top quality. In this session we will discuss tools for building a monitoring and alerting system that is efficient, easy to understand, supervises exactly what we want, notifies the ones we want, is not too noisy and scales well with always growing data.

Big Data DevOps Senior Data Engineer
Bolt

 

4. Stream processing engines – features, performance, comparison

Stream processing and real-time data processing is nowadays more and more popular and important. There are a lot of use cases: data capturing, marketing, sales and business analysis, monitoring and reporting, troubleshooting systems, and real-time machine learning like customer/user activity (personalization and recommendation), fraud detection, real-time stock trades. There are a lot of stream data processing frameworks, like Spark (Structured) Streaming, Flink, Storm, Amazon Kinesis, … Let’s talk about them, try to compare, list pros and cons in terms of various problems and challenge like throughput, performance, latency, system recovery and so on.

Solution Architect
BAE Systems Applied Intelligence

 

5. Using the public cloud effecitively and cost-efficiently

According to Unisys's Cloud Barometer study, only a third of organizations have seen great improvements to their organizational effectiveness as a result of Cloud adoption. What are good practices to be part of those organizations? Let's discuss how to use the public Cloud effectively and cost-efficiently.

Cloud Team Leader
TCL Research Europe

 

6. Building AI/ML systems: from algorithms to production

We're facing very different challenges when writing a scientific paper and when building a production ML system. Things get even more complex when a single project involves both research and application. It's generally understood yet often overlooked: let's get talking! How to scope an ML project? How to get the data yet avoid biases and that multi-million-euro GDPR penalties? What models work in the real world scenarios? How to handle model deployment? And who do you need in your team to succeed?

ML Engineer
Twitter

 

7. MLOps - how to support the life-cycle of ML models

MLOps is a hot trend concerning the end-to-end lifecycle of ML models from conception to model building and monitoring to decommissioning. How do you govern this lifecycle? Which methodologies and solutions are worth using? What mistakes should be avoided? Let's exchange experiences!

Principal Data Scientist at Samsung SRPOL, associate professor
Warsaw University of Technology

 

9. Data Strategy. The Game.

The format of this round table discussion is the game, where you as Chief Data Officer, has a mission to implement strategic initiatives for $2.7B electronics manufacturer (please watch short video at Tech Zone for more details). You will have a chance to learn how to maximize business value from data, how to design and execute Data Strategy, which strategy approach is the best, how your decisions influences others within organization.

Director, Big Data & Analytics
SoftServe

 

10. Distributed Big Data processing in the cloud – is Hadoop still an option?

Joint Cloudera & 3Soft roundtable to discuss practical experience and highlights of providing self-service access to integrated, secured, multi-function analytics based on Hadoop, cloud-native offerings or custom-tailored solutions. Let us share our knowledge on how to enjoy consistent data security, governance, lineage, and control, while deploying the powerful, easy-to-use solutions business users require and eliminating their need for shadow IT solutions.

Head of Architecture
3Soft
Solutions Engineer
Cloudera

 

11. Snowflake Data Cloud – possibilities and limitations. How I can judge whether this is a value proposition for me and my organization?

EEA Sales Director
Snowflake
Senior Sales Engineer
Snowflake

12.55 - 13.00

TECHNICAL BREAK

PLENARY SESSION

13.00 - 13.30

Lessons from building large-scale, real-time machine learning systems

13.30 - 13.45 Q&A Session

Data Science Lead
Unity

13.30 - 13.45

CLOSING & SUMMARY, PRIZE GIVEAWAY
CEO and Co-founder
GetInData
CEO & Meeting Designer
Evention

*Times may vary.

We have great set of presentation available in the CONTENT ZONE that would be available pre-recorded as Video on Demand for conference participants in advance:

Flink SQL in 2021: Time to show off!

Keywords: #flink #flinksql #streamprocessing #unifieddataprocessing #apache

Senior Software Engineer
Ververica
Managing Big Data projects in a constantly changing environment - good practices, use cases Keywords: #agile #teammanagement #goodpractices #usecases
Senior IT Project Manager / Scrum Master
GetInData
Business Executive
GetInData

Artificial Intelligence - Building in-house AI capabilities from scratch at Philip Morris International

Keywords: #AI #DL #AIBusiness #Innovation #Productivity #Disruption

IT Manager AI
Philip Morris International
IT Solution Architect AI
PMI

Simplifying Stateful Serverless Architectures

Keywords: #ApacheFlink #Serverless #Kubernetes #Event-Driven #Scale

Staff Product Manager
Ververica
CICD Pipeline and delivery of Apache Spark Applications on the cloud using AWS Keywords: #Automation #CICD #HigherEd #Spark
Director, Cloud Data Engineering, Office of the Chancellor
California State University
CSU
Data Warehouse Development Lead.
Building scalable and testable data pipeline through a data pipeline domain specific language Keywords: #DataEngineering #ComposableDataPipeline #SOLIDPrincipleInDataPipelineDesign #GHERKINandDataPipelineSpecification #BDDTDDinDataPipeline
Senior Engineering Manager
Independent speaker

Creating Confidence in Data at Klarna - A Case Study in Automatic Data Validation

Keywords: #data-transformation #transformation-improvement #data-confidence #automatic-data-validation #data-validation-tool

Senior data engineer
Klarna Bank AB
Software engineer
Klarna Bank AB
Modern radars: from simple signal processing towards modern complex data analytics with deep learning Keywords: #radars #deeplearning #bigdata #opensource
Lead Data Scientist
SGPR.TECH
Senior Data Scientist
SGPR.TECH

Organising the chaos - metadata in action

Keywords: #metadata

Technical Account Manager
Ab Initio Software
Building Data Ingestion Platform using Hadoop Keywords: #dataingestion #hadoop #nifi
Software Developer
ING
AWS Serverless Pipelines How to use AWS managed services in near real-time data processing. Recommendations system design. Keywords: #serverless #cloud #AWS #microservices #recommendedsystems
Team Leader
StepStone Services
Battle lessons for machine data in an Oil Refinery Keywords: #OPC #timeseries #cloudopex #networksniffing #Excel
Industry 4.0 Expert
CEPSA
Founder & CEO
Datumize

Data Strategy. The Game!

This is an extension and further context of Taras Bachynskyy roundtable “Data Strategy. The Game!”. Very inspiring approach how to unlock the hidden value of a data. How to maximize business value? Which strategy approach is the best? How your decisions influences others within organization in context of data?

Director, Big Data & Analytics
SoftServe
Predicting effectiveness of marketing campaigns on Facebook platform Keywords: #WideandDeep #XAI #Explainability #Facebook #Marketing
Data Scientist
Sotrender
Data Scientist
Sotrender
Head of AI
Sotrender
Common mistakes that make your chart hard to understand with practical solutions to avoid them. Keywords: #datavisualization #datadesign #dataliteracy #uidesign #graphicacy
Data Visualization Specialist
Freelancer
Top 10 Big Data Systems Pitfalls - war stories and lessons learned Keywords: #BigDataWarStorries#DataAsAService#BigDataROI#LessonsLearned
CEO and Co-founder
Datumo

BIG DATA TECHNOLOGY
WARSAW SUMMIT 2022

April 26th-28th, 2022
Let's go virtual!

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Evention sp. z o.o
Rondo ONZ 1 Str,
Warsaw, Poland
www.evention.pl

CONTACT

Weronika Warpas
m: +48 570 611 811
e: weronika.warpas@evention.pl

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