Agenda - Big Data Technology Warsaw Summit | Big Data Technology Warsaw Summit

DESCRIPTION:

In this one-day workshop, you will learn how to create modern data transformation pipelines managed by dbt and orchestrated with Apache Airflow. You will discover how you can improve your pipelines’ quality and the workflow of your data team by introducing a set of tools aimed to standardize the way you incorporate good practices within the data team: version controlling, testing, monitoring, change-data-capture, and easy scheduling. We will work through typical data transformation problems you can encounter on a journey to deliver fresh & reliable data and how modern tooling can help to solve them. All hands-on exercises will be carried out in a public cloud environment (e.g. GCP or AWS).

Participants limit: 20

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SESSION LEADER:

Michał Soszko

Data Analyst / Analytics Engineer
GetInData | Part of Xebia

Andrzej Swatowski

Data Engineer
GetInData | Part of Xebia

DESCRIPTION:

In this one-day workshop, you will learn how to operationalize Machine Learning models using popular open-source tools, like Kedro and Kubeflow, and deploy it using cloud computing.

During the course, we simulate real-world end-to-end scenarios – building a Machine Learning pipeline to train a model and deploy it in Kubeflow environment. We’ll walk through the practical use cases of MLOps for creating reproducible, scalable, and modular data science code. Next, we’ll propose a solution for running pipelines on Google Cloud Platform, leveraging managed and serverless services. All exercises will be done using either a local docker environment or GCP account.

Participants limit: 18

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SESSION LEADERS:

Michał Bryś

Machine Learning Engineer
GetInData | Part of Xebia

Marcin Zabłocki

MLOps Architect @
GetInData | Part of Xebia

DESCRIPTION:

In this one day workshop you will learn how to build streaming analytics apps that deliver instant results in a continuous manner on data-intensive streams. You will discover how to configure streaming pipelines, transformations, aggregations or triggers using SQL and Python in an user-friendly development environment using open source tools of Apache Flink, Apache Kafka and Getindata OSS projects.

Participants limit: 18

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SESSION LEADER:

Anna Naroska

Data Engineer
GetInData | Part of Xebia

Plenary session

TechPoint Panel

9.45 - 10.15 | 30min

Advances in Big Data Processing: Exploring the latest and evolving tools and solutions

This panel will bring together leading experts from chosen vendors of big data solutions. They will share a deeper understanding of the latest trends, technologies and methodologies driving the big data industry, and leave with practical insights they can apply in their organizations. We will ask series of deep tech questions to all panelist.

Moderator:

Marek Wiewiórka

Chief Data Architect
GetInData | Part of Xebia

Plenary session

10.15 - 10.40 | 25min

Terraforming hardened Azure Databricks environments

#databricks #terraform #devops #security #azure

Creating Azure Databricks environment is as simple as “click of button” but how to ensure platform is secured and protected from data exfiltration? How can Infrastructure as Code and Terraform support platform hardening? How can DevOps accelerate your Bigdata projects? What are key configurations options to consider? What are pitfalls and limitations? What could be improved?

Filip Rzyszkiewicz

Associate IT Architect Director
Iqvia

Parallel session 1

Real-time streaming

11.35 - 12.05 | 30min

Control your data distribution like a pro with cloud-native NiFi architecture!

#streaming #nifi #kafka #flink #cybersecurity #flow #cloud #datamovement In the context of a real-life cybersecurity use case at scale, we'll describe a modern streaming architecture providing the ability to enable real-time actionable insights and actions at scale in the event of a cyber attack. We'll especially focus on the role of NiFi in this architecture and how it is a key element for your data distribution & acquisition problems in modern environments where we have to deal with a wide range of systems, interfaces, formats, etc.

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Pierre Villard

Director of Product Management - Data in Motion
Cloudera

Parallel session 2

Architecture, Operations and Cloud

11.35 - 12.05 | 30min

Volvo – Data Journey towards Cloud – Large scale data platform implementation

#Azure, #Analytical Platform, #Data Platform, #BI, #AA, #Cloud Architectur The Volvo Group has a very ambitious strategy that assumes 50% of its revenue by 2030 comes from services and solutions. Most of those services will be digital and will have data needs. We want to deep-dive into the large-scale data ecosystem and share the challenges we faced while developing the cloud analytical platform. We will share the journey from the high-level architecture phase to the practical implementation and operational cases in the context of the MS Azure stack.

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Sebastian Stulik

Manager of Cloud Monitoring Team, Data & AI Foundation
Volvo Group Digital & IT

Bartek Wierzbicki

Manager Design Authority & Platform, Data & AI Foundation
Volvo Group Digital & IT

Parallel session 2

AI, ML and Data Science

12.10 - 12.40 | 30min

If data platforms are dead, then what next?

#DataPlatform #DataAnalysis #AWS #DataScience #DataLake #Jupyter #DataDrivenBusiness To be strict they are not dead... yet, but they will be if we don’t change our approach. Firstly, now it takes around 6 months from the start of building a data platform until analytical work becomes possible. Second thing, very often companies don’t know how to monetize data, or they don’t have the resources to use it. But what if we change our approach and data platform will be built in an analysis-oriented way? Start by thinking about how data can help your organization, then do the analysis, and the platform will be created "by the way" as a response to analytical needs. Join our presentation and see how we do it at BlueSoft.

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Paweł Piotrowski

AWS Cloud Solutions Architect
BlueSoft

Piotr Ziętek

Head of Data Services
BlueSoft

Parallel session 1

12.10 - 12.40 | 30min

Data Mesh! Just a catchy concept or more than that? - Our Journey at Roche on building an enterprise level Data Mesh Platform.

#bigdata #datamesh #snowflake #dataops #selfservedataplatform

Data Mesh! Just a catchy concept or more than that? In this session we would like to guide you through our Data Mesh Journey at Roche. Starting from the very first idea and motivation up to the actual evolution to an enterprise-level self-serve data platform including insights into multiple capabilities (e.g. Snowflake, DataOps.live etc.). We will explain the Data Mesh Concept and how the combination of cloud services enables teams to fulfill all characteristics of a Data Product.

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Katarzyna Królikowska

IT Expert
Roche Informatics

Mehmet Emin Oezkan

Platform Architect
Roche Informatics

Parallel session 3

AI, ML and Data Science

12.45 - 13.15 | 30min

Living in Perfect Harmony - Where Music and Machine Learning Meet

#machine_leaning #NLP #DSP #music_industry The revolution of machine learning is reaching every aspect of our lives - including art and music. In this talk, we will dive into the world of song analysis and the extraction of lyrical and musical features. We will discuss existing approaches, both in machine learning - Natural Language Processing, Digital Signal Processing - and in music theory & linguistics. We will see how we can use these features in different kinds of machine learning models, and how these models can be used to solve problems in the music industry, such as song tags and song similarity. Attend this talk to learn how your technical skills can be useful also to your hobbies.

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Yama Anin Aminof

Data Sciencist
Meta

Parallel session 3

Streaming and Real-Time Analytics

12.45 - 13.15 | 30min

Apache Flink: Introduction and new Features

#stream processing, #streaming sql, #real time, #apache flink, #open source In this presentation, we will explain the fundamentals of Apache Flink: What are the common use-cases, how do you build applications on top of it, how does it integrate with other systems and how does it help solve operational challenges. Also, we will discuss new features added in the last Flink releases, such as the Kubernetes operator, batch & streaming unification and more.

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Robert Metzger

Staff Software Engineer
Decodable

Parallel session 1

Architecture, Operations and Cloud

14.00 - 14.30 | 30min

Ask Questions First, How We Flipped The Data Lake On It's Head

#data lake, #data architecture, #data swamp, #BI We all start with the same dream of an amazing well curated data lake. A place where we could store all the company's valuable data indefinitely, a lake of insights just waiting to be found. But reality has proven us again and again, that these lakes of insights we seek, more often than not turn out to be chaotic, unstructured data swamps. But how about a different approach? What if we could solve for schema, cataloging, ownership, governance and so many other issues - before the data was even created. In this talk we will discuss how we've done exactly that in Wix's data platform. We will review how we've built the tools, culture and development ecosystem to make BI data a first class citizen company wide, and how that enabled us to build a fully structured and curated data lake in petabytes scale.

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Josef Goldstein

Head of R&D, Data Engineering Infrastructure
Wix.com

Parallel session 2

Data Engineering, Data Strategy and ROI

14.00 - 14.30 | 30min

What data engineering approach and culture fits your organisation better

#dataengineering #bigdataanalytics #bigdatatechnologies Data engineering has become a more complex environment and means different things to different persons/organisations Understanding this is useful when hiring/looking for a job/assessing solutions and vendors In this presentation, we will go though different approaches, taking into account engineering culture, data landscape, expected outcome and hiring/contracting history For each we will look at examples, pros and cons

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Pierre de Leusse

Engineering Director
PepsiCo

Parallel session 2

Data Engineering

14.35 - 15.05 | 30min

How to build reliable, observable and maintainable Data Pipelines based on Datavault built to last and serve BI and ML use cases alike

#analyticsengineering #datavault #dbtlabs #snowflake #mlops Ingesting sources by generating Airbyte Connections from DBT sources Business Model Based Data Vault Integration Layer DBT Hardrules to prepare Source Data for Datavault or Raw Consumption Mapping Source Data to the Business Model DBT Softrules to implement Data Products and re-usable components based on the Business Model Combining Raw and Business Model based data to reduce and operationalise Data for ML Use Case

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Torsten Glunde

CEO
Alligator Company

Parallel session 3

AI, ML and Data Science

14.35 - 15.05 | 30min

Quality Over Quantity - Active Learning Behind the Scenes

#activelearning #machinelearning #deeplearning #wisesampling Active Learning - a way for collecting labeled data wisely, thus achieving equivalent or better performance levels with fewer data samples and saving time and money. Background - what type of methods there is? What is the motivation behind using active learning? Methods - two different methods will be describe, be demonstrating them on a specific real world use case Results - what are the results of those methods compared to regular random sampling? Validations - how can we validate the results? How can we be sure that all the components behaves as we want?

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Sivan Biham

Machine Learning Lead
Healthy.io

Parallel sesion 4

Architecture, Operations and Cloud, Streaming and Real-Time Analytics

14.35 - 15.05 | 30min

Near-Real-Time streaming applications monitoring and automated maintenance at billions of records/day scale: the architecture allowing us to sleep at night

#Data engineer, #data architect, #head of data, #CTO, #CDO Handling billions of messages/day is not an easy challenge for streaming applications: the streaming world never sleeps, while humans … well, they should! In this talk, a real-world practical solution is presented for Near-Real-Time streaming applications monitoring and automated maintenance, including: a flexible and scalable architecture to be resilient against extreme volume and velocity dynamics an actionable monitoring system to enable automated recovery processes a customizable triggers system an interoperable data model for health metrics a decoupled and application-agnostic design

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Giuseppe Lorusso

Big Data Engineer
Agile Lab

Parallel session 1

Architecture, Operations and Cloud

15.10 - 15.40 | 30min

On-prem to the cloud: challenges, common pitfalls, and lessons learned!

#OnPremExit #CloudMigration #LessonsLearned #IfIWouldHaveKnown How GE Healthcare has transitioned effectively from on-prem analytical ecosystem to the cloud. Migration options and approaches Best Practices for re-platform & redesign of legacy solutions to cloud Pitfalls: What people do not say... Retrospective and lessons learned

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Ireneusz Chwała

Principal - Data & Information Architect
GE Healthcare

Philippe Peloquin

Staff Data & Information Architect
GE Healthcare

Parallel session 2

Data Engineering

15.10 - 15.40 | 30min

Modern Big Data world challenge: how to not get lost among multiple data processing technologies?

#TechnologicalChoices #DataProcessingEngines #BigDataArchitecture #ProsAndCons #DataDrivenBusiness During the presentation we will try to help you not get lost among the most popular Data Processing Engines. Our presentation is an attempt to anser following questions: What Data Processing Engine should I use? What are the key points to consider while making such a decision? We will present these technologies into your ETL processes from different perspectives - starting from the complexity of data pipelines, through amount of data, maintainability and required expertise. We will focus on the leading open-source and cloud technologies like Apache Spark, Apache Beam, BigQuery/Snowflake/Hive, and many more.

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Jakub Demianowski

Senior Software Engineer
Allegro.pl

Yevgeniya Li

Software Engineer
Allegro.pl

Parallel session 3

AI, ML and Data Science

15.10 - 15.40 | 30min

Common issues with Time Series and how to solve them

#timeseries, #datascience, #forecasting, #preprocessing, #python Time series data is fundamental for most model businesses — however its handling involves a variety of common issues that can lead to serious consequences when not treated with care. In this talk, I will introduce the audience to several such common issues, their causes and effective solutions. This will include: (1) stabilising complex time series dynamics; (2) imputation of time-clustered missing values; (3) reducing impact of noise on forecasting models.

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Vadim Nelidov

(Lead) Data Science Consultant firma: GoDataDriven
Xebia Data (Netherlands)

Parallel session 4

Data Strategy and ROI

15.10 - 15.40 | 30min

How to build a data community: The fine art of organizing Meetups

#data-meetups #meetups #data-community Meetups are a great way to upskill your team, create internal/external data communities, improve your leadership skills, and hire new talent. In fact, the tech world is famous for its vibrant meetups and conferences. In this talk, demystify the organization of meetups. From finding sponsors, getting world-class speakers, and attracting a relevant audience.

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Juan Manuel Perafan

Analytics Engineer
Xebia

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 one roundtable sessions, hence every conference participants can take part in 1 discussion.

Roundtable discussion

2. Business applications of generative AI

What we can observe today is the rapid growth of new AI models that achieve astonishing results in generating text, audio, images, and even source code. It becomes clear that the generative AI revolution has begun. The question is: are companies ready for it? Join me for a discussion – everyone will have a chance to share their thoughts, discuss experiences, exchange ideas, ask and answer questions. Discussion points include e.g.: ● Which AI models have the potential for being applied by companies? ● How can they be used to gain profit? ● What are the risks of using them? ● How can we mitigate these risks?

Moderator:

Krzysztof Jędrzejewski

Director, AI R&D
Pearson

DESCRIPTION:

Newer platform technologies enable analysts to build pipelines using 2 to 3 weeks training, as opposed to existing technology stacks that require extensive experience in dedicated engineering tools and languages like Python. This change is revolutionary for larger organisations as it unlocks a large pool of resources and provides strategic flexibility. This workshop will cover the topic of the modern data platform architecture principles. The session is intended to be an open discussion around the design choices that we will be presenting.

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SESSION LEADERS:

Krzysztof Mikłasz

Senior Data Product Manager
Reckitt

Wojciech Wojtas-Niziurski

Data Solutions Architect
Reckitt

DESCRIPTION:

Designing scalable data platform on Azure. Using the most popular technologies for data ingestion, storage and processing. Technologies used: Azure Data Lake, Azure Synapse, Azure Data Factory, Azure Data Hub, data streaming, etc. Hands-on workshop to set up data platform on Azure from scratch. Creating sample data pipelines for batch & streaming data. Walkthrough different Azure components.

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SESSION LEADER:

Karol Bokszczanin

Lead BigData DW/BI Engineer
SoftServe

Parallel sesion 1

Data Engineering

14.15 - 14.45 | 30min

Column-level lineage is coming to the rescue

#OpenLineage #Column-Level Lineage #Observability #Spark OpenLineage is a standard for metadata and lineage collection that is growing rapidly in adoption. Column-level lineage is one of its most highly anticipated new features. In this talk, we: show the foundations of column lineage within the OpenLineage standard, provide a real-life demo of how it is automatically extracted from Spark jobs, describe and demo column lineage extraction from SQL queries, show how the lineage can be consumed on the Marquez backend. We aim to focus on practical aspects of column-level lineage that will be interesting to data practitioners all over the world.

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Paweł Leszczyński

Data Engineer
GetInData | Part of Xebia

Maciej Obuchowski

Data Engineer
GetInData | Part of Xebia

Parallel session 3

Data Engineering, Streaming and Real-Time Analytics

14.15 - 14.45 | 30min

Challenges for streaming delivery of ordered data on AWS

#streaming #bigdata #dataengineering #aws Delivering data at scale is not a problem anymore. Cloud services and distributed data processing frameworks make this task relatively easy, unless... unless you face an ordered delivery requirement. The talk sheds some light on the problems you may face and the possible solutions. Although the presentation covers AWS data services, the problems and solutions can be generally applied.

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Bartosz Konieczny

Senior Data Engineer
Free2Move

Parallel session 1

MLOps

14.50 - 15.20 | 30min

Is the MLOps tooling the same as the DevOps tooling?

#mlops, #devops, #engineering culture Monitoring, observability, scalability, reproducibility, CI/CD, logging, tracing, blah blah blah - all of the major SRE-ish words are present in ML powered solutions too. Can we just blindly apply the same tooling we know from the usual systems there? Well, if we apply the Betteridge's law of headlines as well as take into account that a separate MLOps term was coined, we know that the answer is probably "no". In this talk, I will present an arbitrary production ML system and focus on the tooling around it. You will immediately be struct by both the similarities and the differences between the DevOps world and the MLOps world. Suddenly the logging, monitoring or reproducibility tools and concepts you know will gain a lot of new features and responsibilities.

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Tomasz Dudek

Solutions Architect
Chaos Gears

Parallel session 2

AI, ML and Data Science

14.50 - 15.20 | 30min

Degenerative feedback loops - how they emerge and why should we care?

#feedback #loops #recommender #systems degenerative feedback loops are present not only in recommender systems anymore when data distribution shifts, model retraining will usually make the situation worse talk summarizes how to detect and prevent degenerative feedback loops in production environment when applied to a mobile game

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Martin Dlask

Data Science Tech Lead
King (Candy Crush Saga)

Parallel session 3

Data Strategy and ROI

14.50 - 15.20 | 30min

How to make money with free data

#monetization #strategy #semantics #blockchain Money does grow on trees. We are surrounded by an extreme amounts of public, free and open data that everybody can get. But in reality this is a very complex and technically challenging process of sourcing, integration and making sense of it. Please join us to see how to build a tech stack and sustainable business model for monetization of theoretically free big data sources.

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Tomek Mierzwa

Founder and Chief Data Wizard
Token Flow Insights SA

Parallel session 1

Architecture, Operations and Cloud, Data Engineering

15.30 - 16.00 | 30min

Smart manufacturing in life science. Real-life use case of cloud based project in IoT

#iot #azure #event-hub #devices #scalability The aim of this presentation is to provide insights about key factors and decisions that drive the technical and business aspects of the IoT data-driven project. We will present the initial setup, the challenges, the state of the project after changes, the future vision and the plan on how to achieve it. Points to consider when moving the data from the factory to the Azure Cloud Battle testing referential architecture vs real life use-cases Scaling the solution by onboarding new factories - how to design the best architecture Device Telemetry data analysis use-case - deep dive into the data

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Piotr Guzik

CEO
Datumo

Wojciech Winnicki

CTO
C&F

Parallel session 2

AI, ML and Data Science

15.30 - 16.00 | 30min

AI for Anomaly Detection and Root Cause Analysis in network area

#AI #AnomalyDetection #RCA #Network #PredictiveNetworkMaintenance #AI-EmpoweredNetwork Have you ever wondered how AI is used in the telco network area? Orange, as data-driven and AI-powered telco company, place Data and AI at the heart of our innovations for Smarter Networks. In that case, network management is supported by Machine Learning models for detecting anomalies based on different types of network data. During this presentation we will zoom on Predictive Network Maintenance based on system logs from network nodes. Our aim is to shorten root cause analysis with Explainable AI to improve network experts daily job.

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Marzena Ołubek

Data Scientist
Orange Polska

Parallel session 3

Streaming and Real-Time Analytics

15.30 - 16.00 | 30min

Where is my bottleneck? Performance troubleshooting in Flink

#ApacheFlink In this talk, we will cover various topics around performance issues that can arise when running a Flink job and how to troubleshoot them. We’ll start with the basics, like understanding what the job is doing and what backpressure is. Next, we will see how to identify bottlenecks and which tools or metrics can be helpful in the process. Finally, we will also discuss potential performance issues during the checkpointing or recovery process, as well as and some tips and Flink features that can speed up checkpointing and recovery times. In this presentation we will cover: Understanding Flink Job basics. Where to start performance analysis of records processing? What about analysis of the checkpointing or recovery process? Various Tips & Tricks

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Piotr Nowojski

Staff Software Engineer
Confluent

Parallel session 1

MLOPs

16.05 - 16.35 | 30min

Speedup your MLOps with Rust

#mlops #featurestore #rust Rust is known for its speed and safety that’s why it is widely used in operating systems, databases, virtual reality areas. But can we use in it the data science and machine learning areas to boost processes and lower infrastructure cost ? During the presentation I would like to show how we can use Rust libraries and components (integrated with Python) to speedup MLOps in several areas (data processing, feature stores, features serving and models serving).

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Kuba Sołtys

Solutions Architect
Bank Millennium

Parallel session 3

Data Engineering, AI, ML and Data Science

16.05 - 16.35 | 30min

What to do when there is no data? Anticipating BigData in MedTech project

#MedTech #DataGeneration Data generation of never seen distributions based on expert knowledge Strategies on how to effectively cooperate with the teams of experts from other fields Technical solutions on how to evaluate models and retrain them using new data from other distribution

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Tomasz Juszczyszyn

Senior Data Scientist
Chess Grow

Parallel session 2

AI, ML and Data Science

16.40 - 17.10 | 30min

Chronon - an open source feature engineering framework

Chronon is Airbnb's data management platform specifically designed for ML use cases. Previously, ML practitioners at Airbnb spent roughly 60% of their time on collecting and writing transformations for machine learning tasks. Chronon reduces this task from months to days - by making the process declarative. It allows data scientists to easily define features in a simple configuration language. The framework then provides access to point-in-time correct features - for both - offline model training and online inference. In this talk we will explore the problems that arise in industrial feature engineering context and explain how you can use Chronon to solve them.

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Nikhil Simha

Staff Software Engineer
Airbnb

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