About The Conference
What is DataMass?
It’s not just another conference. It’s an event created with passion – and targeted at those who use Big Data in practice in their everyday work. The main idea behind the conference is to promote knowledge and experience in designing and implementing tools for the analysis of big data volumes.
We connect the people who care
We believe that building a community of dedicated experts will help us share knowledge and exchange experience in shaping scalable and distributed computing solutions.
We value real-life expertise – is there any better way to learn than from actual specialists? Our speakers, true practitioners from top data-driven companies, will show you what they have learned and discovered about the Big Data world.
Answering your needs
Playing an active role in the Big Data world, we could see that a big technical conference focused on the exchange of knowledge and experience in this field was needed in Poland. The aim of DataMass is to create a synergy between businesses focused on the creation and implementation of Enterprise-class solutions, together with experience and knowledge of the academic environment. The Data Science meetup in Gdańsk is a tangible proof that this is currently possible.
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.
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.
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.
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.
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.
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.
Senior Data Engineer at Etsy
Emily Sommer is a senior data engineer at Etsy. She has worked on Etsy’s internal A/B testing and experimentation platform and is currently helping to build out Etsy’s streaming data analytics platform.
Marcin Siatkowski, PhD
Data Scientist at Roche
Whenever there is a need to answer difficult, data-driven business questions or build data science prototypes, Marcin with his IT savvy colleagues are ready to tackle the challenges with cutting edge technologies.
His role is to lead end-to-end proofs of concept that always have two things in common: data and innovation.
Prior to Roche, Marcin was an advanced analytics back-end in strategic consulting at McKinsey & Co. and a scientific researcher at Medical University in Rostock, Germany. He holds a PhD in applied mathematics.
Stanisław Raczyński, PhD
Senior Researcher at PICTEC
Stanisław Raczyński has been working with data and signal processing and analysis since 2006, where he experimented with ICA and NMF, and R as an early adopter of that language. He obtained his Ph.D. from the prestigious University of Tokyo in 2011 and since then he was working at different research institutions: the University of Tokyo (2011), INRIA (France; 2011-2013) and now Gdansk University of Technology and a non-public research institute PICTEC. He is mostly interested in applying novel methods in the fields of ML, optimization, signal processing, NLP and DLT to innovate business and industry in the region.
Kamil Folkert, PhD
Member of the Board, Head of R&D at 3Soft S.A.
In 3Soft Kamil is responsible for analyzing technological trends and implementing them into the projects realized at the edge of IT and business, with special concern for Big Data technologies (including Apache Hadoop and Apache Spark). Kamil is leading the teams of architects and analysts as well as is operationally engaged in architecture design and technology recommendation. He has hands-on experience in designing, implementing and delivering complex Big Data architectures that are leveraged to automate advanced analytics, including deep learning, for customers from financial services, telco, industry, social media and healthcare.
CEO at SaaS Manager, CTO at Neoteric
Creator and Chief Everything Officer at SaaS Manager – an enterprise software that uses Artificial Intelligence to help telecoms, insurance, and B2C subscription-based services reduce churn and multiply Customer Lifetime Value. 10+ years experience in the IT, portfolio built of Cloud, Big Data and AI for the Fortune 500 companies. Creator of a system scalable to thousands of machines and supporting 32 000 requests per second. Experienced in distributed systems, predictive analysis & AI, Cloud & API development. Enthusiast of microservices architecture and DevOps culture.
Head of Data Science at VirtusLab
Grzegorz Gawron is a lead software engineer/manager with interests in advanced analytics and a taste for theory (that makes him a computer scientist surely!).
He acts as the Head of Data Science at VirtusLab. Previously doing data engineering at Base CRM. Before that trading systems for banking (PRM). … It all started with a :joy: of having the first Commodore 64 program stored securely on a magnetic tape.
He holds an MSc in computer science and an MSc in economics from the University of Warsaw.
Interests in data, algorithms, software engineering, distributed systems, machine learning.
Data & Analytics Capability Lead at Kainos
Bill heads up the Kainos Data and Analytics Capability and has a leadership role across a number of engagements, as well as defining policy around data ethics. By profession he is an Enterprise Data Architect and has spent 20 years been wrestling with data challenges in commerce, government and the third sector.
Don't miss your chance and buy your Early Bird ticket now! Hurry up, this offer lasts only till 20.07.2018 23:59
selling since 29.06.2018 to 22.07.2018
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While many technical conferences present the theory of whatever technology they cover, it was great to see that both the organizers and the speakers of DataMass paid attention to the practical part above all. Theory is one thing, but the implementation of it is a whole new experience – and it’s great to have an event that gives big data professionals space to present their work.
TidK, MVP Microsoft
It’s amazing to see that Big Data is becoming more and more popular as numerous meetups and conferences spring up all across Europe. Gdańsk needed an event like that, and I believe that DataMass is a great occasion for those who use big data to meet other experts and enthusiasts and chat about their experiences and lessons learned.
Balazs Ferenc Gaspar
Being a speaker at the first-ever DataMass Summit was a fantastic experience. Not only was it an occasion to exchange knowledge with other professionals from the field, but also a great opportunity to see how the community of big data enthusiasts is growing and developing. Combining great fun with practical knowledge is the key to a successful event of this type – and the organizers managed to do it.
Jakub Nowacki, PhD
DataMass Summit 2017 was the first edition of the conference and definitely not just another conference of this kind. How was it different? First of all – the venue. It took place in a post-industrial shipyard building. Not a very typical place to hold a technical conference, but it worked! Secondly, the conference was purely practical. Speakers focused on their experience, and not just the theoretical part, which in turn gives a lot of space for others to learn.