About the conference

We create an event targeted at people who use Big Data in practice in their daily work. We want to share knowledge and exchange experience in shaping scalable and distributed computing solutions. The main idea behind the conference is to promote knowledge and experience in designing and implementing tools for the analysis of big data volumes.

Playing an active role in the BigData world, we can see that a big technical conference focused on the exchange of knowledge and experience in this field is needed in Poland. The aim of the conference is to create a synergy between businesses, focused on the creation and implementation of Enterprise class solutions, and experience and knowledge of academic environment. The Data Science is a great example showing this is currently possible.

Location:

Centrum Stocznia Gdańsk

The Conference will be held in the post-industrial venue of the CSG Centrum Stocznia Gdańska. The event activities will cover the area of 1,000 m2. Two lecture rooms, workshop space and exhibition space will be available.

About the place

Date of the conference

29.09.2017

DataMass

Topics

Hadoop Application Development

Creation of distributed computing solutions using Hadoop framework. Architecture, implementation and testing of software based on a cluster or a distributed system of files. Creation of ETL processes and reporting systems.

Technologies: Hadoop (Cloudera, Hortonworks, MapR), Talend, Spark, Scala, Avro, Sqoop, Flume, Ozie, Pig, HBase, Hive, Impala ...

Hadoop Administration

Management and administration of clusters based on BigData technologies. In this subject, we focus on issues related to the installation and maintenance of advance cluster solutions (installation, configuration, decomposition and update of the cluster). This cycle also touches upon the methods of automating maintenance processes within the cluster.

Technologies: Cloudera Manager, Ambari, Cloudera Director, Chef, Puppet, Cloudera Altus..

Data Science, Analytics and Reporting

Data analysis systems using machine learning and artificial intelligence. Creation and optimisation of analytically sophisticated solutions. Methods of modelling and verifying developed solutions.

Technologies: H20.ai, Python Anaconda, SAS, SPSS, WPS, Mahout, R ...

Real-Time Processing

Real-time processing of data going to a cluster. This form of data analysis enables an immediate response to any generated activity. It is not only about the activity generated by users themselves, but also about devices communicating with each other within the IoT standard.

Technologies: Spark, Storm, Kafka..

Hadoop in business

Business application of Big Data tools. Typical using case studies showing why the world of big data wins the market all over the world. Pros and cons of solutions in terms of efforts and costs related to the implementation and maintenance of such environments. Reference to commercial vs. open-source technologies.

Hadoop in R&D centers

Cluster solutions in R&D centres. One of the examples is the use of the platform for computing in grant-funded activities at technical universities. We show how a cluster may be used for advance computing and modelling.

Speakers

  • Łukasz Grala

    Data Scientist as a Service

    Data Architect in TIDK - Data Scientist as a Service. PhD in Computer Science at Poznan University of Technology in the field of data mining and machine learning. Since 2010 Microsoft Most Valuable Professional (MVP) in the Data Platform category. Certified Microsoft Trainer, has for years all Microsoft certifications in data analysis, BI and Big Data. Speaker at numerous national and international conferences. Data Community Leader (formerly PLSSUG), member of the PTI board of the Wielkopolska Region.

  • Michał Żyliński

    Microsoft

    During past 10 years Michal was working closely with Polish start-up community, supporting local Microsoft partners and ignited first Microsoft Azure-based projects in Central and Eastern Europe. Nowadays he helps customers identify business value for small and big data alike.

  • Gaspar Balazs

    Cloudera

    Balázs Gáspár is a Sales Engineer at Cloudera, covering the countries of Central and Eastern Europe. He evangelizes Big Data solutions in the region, helps customers identify use-cases and find the right technical solution to turn their data into business value. Balázs has eight years international pre-sales experience in enterprise IT solutions.

  • Jakub Nowacki

    SigDelta/Hypermedia

    Jakub is University of Bristol graduate where he obtained PhD in Engineering Mathematics. On the daily basis he utilizes his analytical and development skill working as (Big) Data Scientist. He is mostly interested in distributed processing and analysis of big data sets. Jakub originally has C/C++ background but currently works mostly in Java, Scala and Python.

  • Mirosław Walukiewicz

    Intel

    Miroslaw Walukiewicz is a Solution Architect in Intel working on defining use-cases for FPGA inside DataCenter. He spent 22 years working in different companies (Crosscomm, Olicom, Intel) on accelerating DataCenter workloads focusing mainly on computer networking and Network Function Virtualization.

  • Lukasz Cmielowski

    IBM

    PhD, is a Automation Architect and Data Scientist in IBM with a track record of developing enterprise-level applications that substantially increases clients' ability to turn data into actionable knowledge.

  • Claudio Villar

    AirHelp

    Claudio Villar has more than 10 years of experience in the Software Industry working for companies like IBM, Samsung and now Senior Product Manager for Automation initiatives at AirHelp. His areas of interest cover Theoretical Aspects of Software Engineering, Software Product Lines, Ubiquitous Computing, Distributed Computing and Machine Learning. His talk will cover the basic aspects Machine Learning and how such knowledge can be applied to real life products.

  • Mateusz Kurleto

    Neoteric

    Matt Kurleto is a tech entrepreneur that founded Neoteric. Software development company that helps customers leverage the digital transformation by building cloud services. Since 2015 Neoteric spun-off 3 startups raising pre-seed rounds from Inventity Foundation and FerberVC.

  • Jakub Dąbkowski

    Avaus

    Marketing Automation expert combining technological and programming knowledge with marketing and sales objectives. Currently, CTO of Avaus Poland where his goal is creating IT team supporting Swedish and Finnish Marketing Automation implementations. In his daily work he uses complex tools from the biggest companies such as Adobe Campaign + Adobe Marketing Cloud and Marketo. Graduate of Theoretical Physics at University of Gdańsk.

  • Maciej Bussler

    Acxiom

    Over ten years of experience in the design and construction of data processing systems based on commercial ETL platforms. Currently working as an Architect in Acxiom.

  • Wojciech Pituła

    Nordea Bank

    I'm Scala developer fascinated by functional programming, currently solving BigData problems for Nordea Bank. In my free time, I write even more software, go to conferences and try to contribute back to open source.

  • Mariusz Liksza

    Acxiom

    I graduated from the Financial Mathematics at Gdansk University of Technology and Computer Science and Econometrics at University of Gdansk. I'm interested in statistics and machine learning. By creating solutions I want to combine the power of mathematics with the current solutions from Big Data world. Currently Solutions Developer at Acxiom, dealing with the processing of large data sets for big US clients. On weekends lecturer at Postgraduate Studies in Data Science at Gdansk University of Technology. Privately, passionate about new technologies, mainly from the IT industry.

  • Tomasz Sosiński

    Scalac

    I am a programming enthusiast partial to functional programming and Scala with expertise in R and Python; fascinated by data (big and fast) and the ability to tell stories with information utilizing JavaScript. I utilize the whole set of technologies to bring the data experience from Hadoop clusters to your desktops. While searching for new challenges, I meander from rendering infographics on front-end to core data transformations. Photography lover, eager biker and a huge fan of Asaf Avidan.

  • Tomasz Szczechura

    Grupa Wirtualna Polska

    IT specialist by profession, education and passion. In GWP since 2011. Currently team leader of Data Systems team. He deals with the administration of the company's most valuable assets - Data Bases and Big Data.

  • Wit Jakuczun

    WLOG Solutions

    Founder and owner of a consulting company WLOG Solutions that is a strategic partner in R based large scale analytics. In company responsible for translating customer needs to mathematics and vice versa. Have 11+ years of experience in delivering predictive, simulation and optimization models.

  • Maciej Godek

    Functional programming expert

    Maciej Godek is a philosopher and a programmer. He believes that programming is as essential to participating in the culture of the XXI century as reading and writing was in the XX century. In his spare time, he works on a game that trains programming skills, and that is also a platform for the development and application of Artificial Intelligence methods. In his battle against the Babel tower of programming languages, he recently wrote "A Pamphlet against R", available (CC-BY-SA) from https://panicz.github.io/pamphlet

  • Piotr Czarnas

    Querona

    Piotr has over 16 years of experience as a software architect and a manager. His main area of expertise is solution architecture design and the internals of database engines. He has designed the architecture of Querona, a data virtualization engine. Piotr's daily activities are focused on helping companies utilize Big Data platforms for data analytics and Business Intelligence. He works with Apache Spark and various database engines on a daily basis.

  • Michał Adamski

    DNVGL

    I’ve been directly involved in end-to-end BI solution development and governance throughout my career. I am especially passionate about bringing business closer to data, and simplifying and enhancing business and IT collaboration.

  • Tomasz Żukowski

    GetInData

    Tomasz gained his experience during his work in logistics, medical services and IT (consumer apps). As a data analyst he was building reports by means of Microsoft Excel and VBA. He has been working with SQL and Python. Currently he is taking care of analyzing Big Data by using Hadoop ecosystem, Python and R. As a hobby, he enjoys taking part in Kaggle competitions.

  • Piotr Krewski

    GetInData

    Piotr Krewski has extensive practical experience in writing applications running on Hadoop clusters as well as in maintaining, managing and expanding Hadoop clusters. At Spotify, he was part of the team operating arguably the biggest Hadoop cluster in Europe. He is a co-founder of GetInData where he acts as architect and engineer helping companies with building scalable, distributed architectures for storing and processing big data. Piotr serves also as Hadoop Instructor delivering GetInData proprietary trainings for administrators, developers and analysts working with Big Data solutions.

  • Agnieszka Potulska

    Intel

    Software engineer with strong mathematical background. Graduated in Computer Science as well as Applied Mathematics from Gdańsk University of Technology. Currently working at Intel Datacenter Group, where her main areas of focus include data acquisition and analytics pipelines.

  • Piotr Tylenda

    Intel

    Software Engineer at Intel specializing in microservice architecture and data mining, graduated in Computer Science from Gdańsk University of Technology and University of Gdańsk. Passionate about big data technologies and DevOps.

Program

29th September

  • Participants registration and coffee time

    8:30 - 9:20

  • The official start of the conference

    9:20

  • Currently more and more system produce data with high frequency, which needs to be processed often in real-time. On the other hand machine learning is well-established tool for data analytics and predictions, but historically most of the work has been done offline. This talk is about implementing different types of machine learning to real-time streams, along with potential pros and cons of the particular approach.

    Real-Time Machine Learning in Streaming Data Pipelines

    9:40 Jakub Nowacki

  • SQL Server 2017 has many features to be a great tool for Big Data Analytics. We will see what data can be source and how to build models using R and Python in SQL Server.

    Machine Learning for Big Data in SQL Server

    10:50 Łukasz Grala

  • We may think that BigData is an utltimate goal for every B2C company, and every one should use it. Unfortunately, it is not our reality. But why? Let me share my point of view on it. Let me discuss steps that a company has to take to implement a successful program that will allow us to reap rewards of BigData and MachineLearning in everyday marketing and sales.

    Dreamer's guide to big data

    11:50 Jakub Dąbkowski

  • Real-time event processing is becoming nowadays crucial part of many big data systems. It does require different architectural approach when compared to - historically - batch-oriented Hadoop workloads. During this session we’ll discuss the basics of lambda architecture, options of deploying it in cloud-based environments and real-world examples of such implementations.

    Hadoop and its alternatives: deploying Lambda architecture on top of Microsoft Azure platform

    12:40 Michał Żyliński

  • LUNCH

    13:20-14:20

  • Handling BigData workloads is one of the most demanding jobs made in data centers today. Processing that kind of data using SW only solutions could be improved using HW accelerators based on FPGA. This process requires a new breaking-down HW and SW architecture. Data Center providers face many challenges using that technology like flexible workloads management, virtualization and security. The presentation presents the Intel solution for HW accelerators based on close linkage of the Intel Xeon® with integrated FPGA technology.

    Accelerating BigData workloads by closely linking of CPU and FPGA

    14:20 Mirosław Walukiewicz

  • More info soon.

    Data Science with Cloudera

    15:10 Balazs Gaspar

  • From model creation to running application in production is a huge step. Moving excellent model to production very often fails because of the complexity and maintainability issues. Is it possible to have web application consuming machine learning model up&running in production within 15 minutes? Within this presentation we would like to show how it can be achieved by answering data scientist and developer needs within IBM Watson Machine Learning offering.

    From Spark ML model to production application in minutes

    16:00 Lukasz Cmielowski

  • Machine Learning is a trend now, not just in the academy but through out various companies, so this talk aims to discuss how can you get the value out of this topic and how can it be applied in your business case. Some experiences regarding data culture, pipelines and product development will be shared here!

    Machine Learning - the basics for building a product

    16:50 Claudio Villar

  • Functional programming is a technique for both increasing software’s scalability and decreasing the number of bugs. Recently it has been receiving more attention due to the fact that it is a reliable way of utilizing the power of multicore processors. As the cost of this increased attention is a large number of confusions and misconceptions, the talk will be devoted to explaining this paradigm from the first principles.

    Principles of Functional Programming

    17:40 Maciej Godek

  • The official end of the conference

    18:30

29th September
  • True Software Scandinavia is one of best known Swedish start-ups that created application for identifying unknown callers and blocking spam calls. These functionalities are realized via global phonebook which is based on input from tens of millions of Truecaller app users. We’ll describe how Truecaller, with the help of Getindata team, transitioned into a company, where business decisions are based on everyday analysis of terabytes of users’ activity data. We’ll also explain why they had to switch to modern big data architecture to achieve this. Presentation consists of two parts. First we’ll show big data platform which is currently in use, with key decisions that were made and challenges that occurred during transition phase. In the second part we’ll focus more on data analyst’s perspective, giving examples of tasks that help Truecaller understand behavior of its users and react accordingly.

    Truecaller - towards a data-driven company

    9:40 Tomasz Żukowski & Piotr Krewski

  • R is a great tool for data scientist. Being very dynamic and popular is now one of the most important technology on the market. Unfortunately out-of-the-box R is not suited for large scale applications. I will present R Suite that is an open-source solution developed by us for us to manage R development process.

    Managing large (and small) R based solutions with R Suite

    10:50 Wit Jakuczun

  • An efficient event streaming environment must cover two use cases: streaming and full reprocessing. The process will mainly work on newest events (this is streaming) but when the business rules are changed, a full reprocessing of all events must be done. Kappa Architecture was created internally by LinkedIn and it's goal is to satisfy both those use cases. This presentation will show how Apache Kafka can be included in an event processing architecture.

    Kappa Architecture for event processing

    11:50 Piotr Czarnas

  • Currently more and more systems producing more and more data. This talk is about how to ingest terabytes of data in real time. Query this data with sub-second OLAP queries and how to scale this architecture to petabytes of data.

    Druid - Scalable OLAP

    12:40 Tomasz Szczechura

  • LUNCH

    13:20-14:20

  • You have heard about Apache Spark, everyone has. But what about all the others, like Flink, MapReduce, Storm and Samza. What are the differences between them? When should you choose one over another? Why people try to do the same thing in so many ways? And maybe the most important: why comparing such tools is so hard.

    Why do we need 9 different data processing engines and what makes Spark 2.32x better than Flink?

    14:20 Wojciech Pituła

  • In my speech, I will try to show the attendees that performing ML on streaming is not only possible but actually very straightforward. I will depict some advantages as well as disadvantages of streaming ML applications and point to treacherous traps. Moreover, I will try to convince the audience that employing some functional programming techniques and conditions may lead to a simpler reasoning about whole ML domain. The code examples will utilize Scala programming language and Akka Streams technology, which is not directly related to ML and distributed computing (aka BigData) but is perfectly capable of undertaking those challenges.

    ML on Akka Streams - implementing simple ML algorithms on a streaming technology

    15:10 Tomasz Sosiński

  • Self-service BI is not a new concept. There are many opportunities as well as challenges that face self-service BI today. So, how can we truly create a self-service experience that is smooth and value-adding to the end users, whilst balancing the technical effort and complexity needed in our semantic modelling? Is it possible and where do we draw the line? In this session, we will take a look.

    Designing for business: Customer-centric self-service BI

    16:00 Michał Adamski

  • Data center workloads produce a significant amount of log data which has to be analyzed in order to discover any potential issues. We present an automated text mining approach for workload monitoring and data analytics, which is a combination of machine learning and big data processing. This session provides an overview of a data pipeline based on key components such as Apache Kafka, Apache Spark and generalized version of k-means algorithm.

    Log Analytics in Datacenter with Apache Spark and Machine Learning

    16:50 Agnieszka Potulska & Tylenda Piotr

30 September
  • Microsoft R Server and data analysis. Microsoft tools for the R language. Data processing, statistical functions, and models of machine learning with Microsoft libraries

    R language with Microsoft libraries

    Łukasz Grala

  • Do you like building models using machine learning algorithms? Do you think that distributed computing is on your list of interests? And finally, your favorite language for data manipulations and analysis is R?If even one or two of those questions have answer ‚yes' then you should come to this workshops and spend a few hours by using great framework to machine learning which is H2O!

    Machine Learning in R using H2O framework

    Mariusz Liksza

  • More details soon.

    Kylo - opensource Datalake platform. Custom Spark transformations based on Scala

    Jakub Wszołek

Tickets

Hurry up!
Limited quantity of early birds tickets available until August 17th.

Buy ticket

VENUE

All around the world the post-industrial areas are becoming the places where the most impressive pieces of architecture are erected and prestigious business, entertainment and art events are hosted.

Img
Img
Img

The magnificent building 89A stands right in the middle of downtown Gdansk, near the famous Solidarity Square. It is a gem among the post-industrial architecture of Europe. U-boots were manufactured here before World War II, but later it has been used for peaceful purposes only. In the middle of 2004 it was called Centrum Stocznia Gdanska (Gdansk Shipyard Centre) and became the seat of the European Culture and Education Centre.

Partners

Sponsors

Gold
Gold

Sii

PASSION for TECHNOLOGY

Acxiom

The Data Foundation For The World’s Best Marketers
Silver
Silver

Bronze
Bronze

Patrons
Patrons
Media Partners
Media Partners

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Contact

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Company address

DataMass
Szafarnia 10/26
80-755 Gdańsk

Phone

+48 516 960 028
+48 517 461 507

E-mail

office@datamass.io
info@datamass.io





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