Crunch Data Engineering and Analytics Conference Budapest October 28-30, 2015

CRUNCH is a use case heavy conference for people interested in building the finest data driven businesses, no matter the size of their business or the volume of their data.

If you are a Data Engineer, Data Scientist, Product Manager or simply interested how to utilise data to develop your business, this conference is for you. No matter the size of your company or the volume of your data, come and learn from the Biggest players of Big Data, get inspiration from their practices, from their successes and failures and network with other professionals like you.


Speakers

Doug Cutting

Doug Cutting

Chief Architect at Cloudera, Founder of Hadoop
Challenges for the Data Ecosystem

There's been a revolution in data technology. New systems are now predominantly built using an ecosystem of open-source tools that has developed around Apache Hadoop. However much work remains to be done before the full benefits of these new technologies can be realized. I will discuss these challenges, both technical and social, and how we might address them.

Bio

Doug is the founder of numerous successful open source projects, including Lucene, Nutch, Avro, and Hadoop. Doug joined Cloudera in 2009 from Yahoo!, where he was a key member of the team that built and deployed a production Hadoop storage and analysis cluster for mission-critical business analytics. Doug holds a Bachelor’s degree from Stanford University and sits on the Board of the Apache Software Foundation.

Alistair Croll

Alistair Croll

Entrepreneur, author of Lean Analytics
Silver linings and dark clouds: the pros and cons of a data-driven world

Big Data helps us decide better, making good use of scarce resources and creating entirely new industries by radically improving the efficiency of organizations. But it has a dark side, too, widening the digital divide, enabling manipulation, and sliding us down a slippery slope to a surveillance state.
In this talk, Alistair Croll, chair of O’Reilly’s Strata and Next:Money conferences and author of Lean Analytics, looks at the course our always-on, data-driven world is charting, offering some thought-provoking examples of what the future might hold, and what we need to watch out for as individuals, organizations, and even as a species.

Bio

Alistair has been an entrepreneur, author, and public speaker for nearly 20 years. He’s worked on a variety of topics, from web performance, to big data, to cloud computing, to startups, in that time. In 2001, he co-founded web performance startup Coradiant (acquired by BMC in 2011), and since that time has also launched Rednod, CloudOps, Bitcurrent, Year One Labs, and several other early-stage companies.

Alistair is a chair for Strata + Hadoop World conferences and the International Startup Festival, and the founder of the Bitnorth conference. He’s written four books on analytics, technology, and entrepreneurship, including the best-selling Lean Analytics which has being translated into eight languages. He lives in Montreal, Canada and tries to mitigate chronic ADD by writing about far too many things at Solve For Interesting.

Stephen Brobst
Scott Gnau

Stephen Brobst

Chief Technology Officer,
Teradata Corporation

Scott Gnau

Chief Technical Officer,
Hortonworks
Big Data Exploitation with a Unified Data Architecture

The talk introduces a framework for extracting value from Big Data with an optimized ecosystem for data storage, value-added processing, discovery, and exploitation. We will discuss next generation technologies using a combination of open source and commercial software components to maximize value and minimize total cost to value for Big Data exploitation. The software services and a reference architecture for next generation ecosystem deployment will be proposed and described in detail:
How to properly deploy the data lake concept using Hadoop and other advanced data platforms.
How to perform best practices data integration in a big data ecosystem.
How to leverage SQL, NoSQL, and NewSQL execution engines for maximizing value from big data.

Stephen Brobst bio

Stephen Brobst is the Chief Technology Officer for Teradata Corporation. Stephen performed his graduate work in Computer Science at the Massachusetts Institute of Technology where his Masters and PhD research focused on high-performance parallel processing. He also completed an MBA with joint course and thesis work at the Harvard Business School and the MIT Sloan School of Management. Stephen has been on the faculty of The Data Warehousing Institute since 1996. During Barack Obama's first term he was also appointed to the Presidential Council of Advisors on Science and Technology (PCAST) in the working group on Networking and Information Technology Research and Development (NITRD). He was recently ranked by ExecRank as the #4 CTO in the United States (behind the CTOs from Amazon.com, Tesla Motors, and Intel) out of a pool of 10,000+ CTOs.

Scott Gnau bio

Scott has spent his entire career in the data industry, most recently as president of Teradata Labs where he provided visionary direction for research, development and sales support activities related to Teradata integrated data warehousing, big data analytics, and associated solutions. He also drove the investments and acquisitions in Teradata’s technology related to the solutions from Teradata Labs. Scott holds a BSEE from Drexel University.

Andrea Burbank

Andrea Burbank

Data Scientist, Pinterest
A/B Testing: building a culture of experimentation at Pinterest

When it comes to running a successful A/B testing platform, many people ask first about how to randomize groups or perform sound statistical analysis. But building an organizational culture that embraces experimentation, understands why novelty effects and long-term holdouts are important, and automatically incorporates an A/B test into any feature rollout is just as challenging as building the right framework and statistical comparisons in the first place. This talk will focus on the evolution of the A/B testing platform and culture at Pinterest, from tools to process to people, and how you can help your company embrace and capture the value of running experiments.

Bio

Andrea Burbank works as a data scientist at Pinterest, where she has led A/B testing for the past two years. The A/B testing program at Pinterest now includes over two dozen engineers trained in experimentation and embedded into product teams. Prior to Pinterest, she worked as a software engineer at Bing and as a natural language scientist on ranking and relevance at Powerset, a semantic search engine acquired by Microsoft in 2008. She has a BS in physics and a BA in linguistics from Stanford University.

Elena Verna

Elena Verna

VP of Growth and Analytics, SurveyMonkey
Make your KPI’s work for you

Defining your KPI’s is no easy task. What is a driver vs. core metric (and why is it important to differentiate the two)? How do you limit your numbers to the top 3 most actionable metrics without missing anything? Most importantly, how to understand and learn your KPI’s (and when it’s appropriate to sound an alarm). And don’t forget - it is crucial to re-evaluate your KPIs and know when it is time for a change. Join the session to learn about best practices, common pitfalls and misconceptions, and learn to track your business effectively. Avoidance of analysis paralysis is guaranteed.

Bio

Elena Verna is the VP of Growth and Analytics at SurveyMonkey. She leads both the Business Intelligence and Product Marketing teams where she focuses on understanding user behavior and improving product usability. Elena was born and raised in Russia, and as a result, has had enough of the long winters.Since moving to sunny California she’s now happily snow-free.

Esh Kumar

Esh Kumar

Data Scientist, Spotify
Real-time Personalization Platform @ Spotify

Spotify is a Music streaming service. In the past, we used Collaborative Filtering on Hadoop to generate Music Recommendations for our 75 million users. As we keep scale up, the number of songs & new users keep increasing. This means that there is an ever pressing need to recommend the right song to the right user at the right time. The time needed to process recommendation jobs on our Hadoop based infrastructure has gone up from 4-8 hours to 16 hours under ideal conditions. Furthermore, we find that personalization as high level product need is needed throughout the company; engineers keep reinventing the wheel in order to achieve this.

We use Apache Storm to append incremental updates to our batch Collaborative Filtering framework. Furthermore, we designed a personalization platform to expose a common recommendation API to the rest of the company. This has improved the experience for new users, especially on the Discover platform. Furthermore, it has decreased the number of overall Hadoop jobs on our system.

Bio

Esh works in Machine Learning @ Spotify. He conceptualized, built and leads an effort to personalize the experience for any user in real-time. One example being the Discover Page for new users. Furthermore, another project he drives is to provide Machine Learning as a Platform so that teams can be empowered to rapidly iterate and build features that involve Machine Learning.

Previously, he built Mobile content recommendations at StumbleUpon. He was in a machine learning PhD program at the University of Texas, Austin, focusing mostly on Deep Learning and Large scale machine learning.

Martin Kleppmann

Martin Kleppmann

Software Engineer, Entrepreneur, Author and Speaker
Patterns for real-time stream processing

You have some streams of data, such as user activity on a website, or sensor readings from devices. Now you want to process the data and make it useful with low latency: for example, generating real-time recommendations, detecting abuse, filtering spam or predicting demand. And you want it to scale well.

Perhaps you've heard of distributed stream processing frameworks such as Samza, Storm or Spark Streaming, which may do what you want, but you're not sure how to use them most effectively.

This talk will introduce some common design patterns for working with high-volume, real-time data streams. We will look at things like joining, enriching, filtering and aggregating streaming data, and we'll explore how you might break down an application into streaming operators that do what you want.

Bio

Martin Kleppmann is a software engineer and entrepreneur, and author of the O'Reilly book Designing Data-Intensive Applications, which analyses the data infrastructure and architecture used by internet companies. He previously co-founded a startup, Rapportive, which was acquired by LinkedIn in 2012. He is a committer on Apache Samza.

Michele Chambers

Michele Chambers

Author, Speaker and CMO at Continuum Analytics
Modern Analytics Roadmap: How to infuse advanced analytics into every part of your business

In this energetic session, Michele Chambers, CMO of Continuum Analytics, the developer of Anaconda, the leading open source analytics platform powered by Python, and 3-time author, demonstrates how to create a modern analytics roadmap that helps infuse data-driven insights and forward looking predictions across the enterprise to drive substantial business value - from one-time wonder hits to repeatable & scalable solutions. In a world where advanced analytics is a business requirement, don’t miss this whirlwind presentation on how to systematically identify and create innovative solutions that move the needle for businesses.

Bio

Michele Chambers is an entrepreneurial executive with over 25 years of industry experience. Chambers has books published by Wiley and Pearson FT Press on Big Data and Modern Analytics. Prior to Continuum Analytics, Chambers held executive leadership roles at database and analytic companies, Netezza, Revolution Analytics, RapidMiner and MemSQL. Chambers has been responsible for strategy, sales, marketing, product management, channels and business development. Chambers holds a BS in Computer Engineering from Nova Southeastern University and an MBA from Duke University.

Yali Sassoon

Yali Sassoon

Analytics Lead, SnowPlow
Best practices in event data processing

Event data i.e. data that describes what has 'happened', is one of the most common categories of 'big data'. In this talk, I will explore some of the challenges associated with building data pipelines, how those challenges can be met with best-practice architectures, before diving into a couple of areas that are not much talked about, and best practices are only just beginning to emerge, namely:

  • Schemaing event data and and managing schema evolution
  • Modeling event data i.e. reshaping it into a format suitable for analysis by everyday data consumers and data processing tools in your company

I will examine how we approach both the above two challenges today at Snowplow and how we plan to evolve our approach over the next few months.

Bio

Yali is a passionate data analyst, which is just as well because he has been crunching data for his entire working life: to improve operations at AP Moeller Maersk and Vestas Wind Systems, to drive strategic decision making for clients at PwC Strategy and Keplar LLP, and both strategic and operational analytics at OpenX Technologies. Yali has an MPhil in the History and Philosophy of Science and a BA in Natural Sciences, both from the University of Cambridge.

At Snowplow, Yali leads on the developing ways to use Snowplow data: including to drive decision-making, understand audience, optimize budgets and deliver personalized experiences.

Evan Miller

Evan Miller

Statistician, Programmer, author of the Wizard statistical analyzer
The Right Way To Run An A/B Test

Businesses are constantly trying to change and adapt to new challenges -- but when are these changes good for the business, and when are they just needless wheel-spinning?

A/B testing (also called split-testing, experiments, or controlled trials) is the art of putting a change to the test and seeing what the data says. In this talk, I'll discuss the fundamentals of designing a simple experiment and analyzing the data, including common design errors and analytic pitfalls, and also cover some of the recent advances in the world of web experiments. The talk won't require any formal background in statistics, but I will be discussing the differences between classical experimentation (where you collect all the data and analyze data once) and Bayesian experimentation (where you analyze the data as you collect it). At the end I will also provide an overview and assessment of some new tools for doing A/B testing the right way, including Optimizely's new Statistics Engine and Visual Website Optimizer's SmartStats. If the phrase "Big Data" makes you feel small, come find out how you can start making data-driven decisions immediately by asking the right questions, running well-designed experiments, and applying a little bit of statistics.

Bio

A graduate of Williams College and the University of Chicago, and a recognized name in Silicon Valley for applying math to business problems, Evan Miller works at the intersection of programming, statistics, and visualization techniques. His algorithms for sorting by average rating are in use at some of the most recognizable destinations on the Internet, and his articles on A/B testing are widely read throughout the industry. Evan's current project is Wizard Pro, a desktop statistics program that takes the pain out of predictive modeling.

David Weisman

David Weisman, Ph.D.

Vice President, Data Sciences, Symbiota, Inc.
From Predictive Analytics to Optimal Business Decisions

For an enormous breadth of applications, machine learning classifiers make predictions such as which customers will enjoy a new film, which days will be sunny, which bank loans will default, and which airline passengers are likely terrorists. Predictions are naturally imperfect. Depending on the application, the financial or safety costs of incorrect decisions differ widely; for example, there is little negative consequence from poorly targeting a click advertisement, but there is a large consequence from misdiagnosing a serious disease. Choosing the optimal business action depends on the quality of the prediction, as well as the costs and benefits of each alternate decision. This highly practical session highlights the important relationship between predictive analytics and business management. We will examine the links between prediction accuracy, the costs and benefits of business decisions, and making optimal decisions based on predictive analytics. You will leave with highly valuable and immediately actionable information on maximizing the benefits of predictive analytics in your organization.

Bio

David is a data scientist and computational biologist with over 35 years of experience in computer science and information technology. He co-founded a consulting firm in 1980, focusing his practice around software strategy and design, quantitative finance and trading systems, compiler and language tools, and distributed system architecture. From 2005-2011, David paused his career to earn a Ph.D. in Molecular Biology at University of Massachusetts Boston (UMB), studying at the intersection of molecular biology, machine learning, and applied mathematics. He now leads the data science and computational biology initiatives at Symbiota, including microbial metagenomic analysis, systems biology, transcriptomics, proteomics, metabolomics, high-throughput screening, and field trial analysis. In addition, David is Adjunct Assistant Professor of Biology at UMB, actively performing research and teaching bioinformatics.

Carl Anderson

Carl Anderson

Director of Data Science, Warby Parker
Creating a Data-Driven Organization

What does it mean for an organization to be data-driven? How does an organization get there? Many organizations think that they are data-driven but the reality is that few genuinely are and that we could all do better. In this talk, I cover what it truly means to be data driven. The answer, it turns out, is not to do with the latest tools and technologies (although they can help) but having an appropriate data culture than spans the whole organization, where data is accessible broadly, embedded into operations and processes, and enables effective decision making. In this presentation, I dissect what an effective data-driven culture entails, covering facets such as data leadership, data literacy, and A/B testing, illustrating concepts with examples from different industries as well as personal experience.

Bio

Carl Anderson is the Director of Data Science at Warby Parker in New York overseeing data engineering, data science, supporting the broader analystics org, and creating a data-driven organization. He has had a broad-ranging career, mostly in scientific computing, covering areas such as healthcare modeling, data compression, robotics, and agent based modeling. He holds a Ph.D. in mathematical biology from the University of Sheffield, UK. He is the author of "Creating a Data-Driven Organization" (O'Reilly, 2015)

Tianhui Michael Li

Tianhui Michael Li

Founder - Executive Director at The Data Incubator
What makes great data scientists

Hiring - even for data scientists - is often not very data driven. At The Data Incubator, we run a fellowship to train and place data scientists in various industries and we regularly receive over 2000 applications per session. To cope, we have to rely on robust analytics and machine-learning for our admissions process to ensure we are finding the best talent for our hiring partner companies. We’ll explain why most traditional keyword-driven screening processes do not work for finding data science talent and how to both streamline and build an automated data-driven screening process that filters for skills and talent, not keywords and hype.

Bio

Michael has worked as a data scientist (Foursquare), quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves.

Scott Krueger

Scott Krueger

Data Architect at Skyscanner
Logging @ Skyscanner - a dreamer's guide to building and using a real self-service data platform at scale

Skyscanner provides a comprehensive and unbiased global travel service to over 35 million unique visitors per month. Constantly increasing visitors, new products, emerging markets and more devices all generate more data to log. Couple this with rapid organizational growth, an aging logging infrastructure and a need to measure everything - and you're left with big dreams, many many questions, lots of opportunity and a lot to do. In this session Scott Krueger, Data Architect at skyscanner, shares the technical and cultural learning’s of implementing an infinitely useful web-scale logging platform at skyscanner. This talk will focus on the technical details and cultural impact of building a self-service data platform. It will describe why the following technologies where chosen: Apache Kafka, Zookeeper, YARN, Samza, Spark, Elastic, Logstash, Kibana, Protocol Buffers. And discuss the important questions along the way: How does technical 'want' become business 'need' and what's the difference? What does implementation really involve? Who owns that data anyways? What about data quality? How will services interact with this thing? Is it a database? How do we get the whole business on-boarded and migrated while keeping the existing lights on?!

Bio

Scott is an experienced Data Architect who has lived through many internet economy data challenges over the last 15 years. Shaped by varying roles (development, operations, devops, management, architecture) across the Media and Travel sectors, Scott offers a well-rounded perspective on what it takes to operate high-growth web businesses. He is obsessed with lifestyle automation so more of his time can be spent doing better things.

Sergii Khomenko

Sergii Khomenko

Data Scientist, Stylight GmbH
Building data pipelines: from simple to more advanced - hands-on experience

Data is becoming one of the main decision-makers in an organisation. The more data we have the more challenges we face every day. Every decision we make will have long-term implications. In the talk we will go through different approaches to the data pipelines: from a simple in-house built, with comparison to open source solutions based on Apache stack(Apache Kafka, Apache Samza, Spark) and finally hosted auto-scaling solutions based Amazon(S3, Kinesis, Lambda, EMR) or Google(Pub/Sub, Dataflow, BigQuery). The talk covers the main aspects of data collecting processes altogether with further implications for data processing, highlighting appropriate solutions and architectures for the particular use-cases.

Bio

Data scientist at one of the biggest fashion communities, STYLIGHT. Data analysis and visualisation hobbyist, working on problems not only in working time, but in free time for fun and personal data visualisations.

One of co-organisers of Munich Wearable Data Hackathon.

Speaker at different conferences: Berlin Buzzwords 2014, ApacheCon Europe 2014, Puppet Camp London 2015, Munich Developer Camp, Berlin Buzzwords 2015, Tableau Conference on Tour - Berlin 2015.

Founder and speaker at Munich Golang User Group, Munich Tableau User Group Speaker at Munich UseR Group, Munich Search User Group, Munich Quantified Self Meetup, Munich Datageek.

Chris Stucchio

Director of Data Science at VWO
Multivariate Testing, Segmentation, and all that - a guaranteed way to find false positives to show your boss

Are you running out of ideas for improving conversions on your website, but your boss is demanding improvements? Have no fear, multiple comparisons are here. Multiple comparisons are a great way to find statistically significant results - clever targeting schemes, interesting user segments, and clever A/B test results - even if the real world won't cooperate and give you lift. There are many clever tools in the industry for exploiting the multiple comparisons problem, including multivariate testing, segmentation and the like, and I'll give an overview of these. If you put enough work in, you can get false positives out. If time permits, I'll also discuss some new techniques for mitigating this issue.

Bio

Chris is formally trained as a mathematical physicist, and did research in quantum physics and nanoscale simulation for his Ph.D. He has also studied medical imaging at NYU, worked as a high speed trader, launched several startups, and currently works on realtime number crunching (mostly in Scala). He's a strong believer in automated reasoning, formal methods, and the power of computers to liberate us from the tyranny of humans making decisions.


Workshops28 Oct, Wed

Lean Analytics: The Data That Will Make or Break Your Business
Speaker: Alistair Croll Place: Epam Time: 9:00-16:00

If you’re being methodical about growth, analytics matters. For startups, analytics is about measuring the right metric, in the right way, to produce the change the business needs most at that point in time. That’s harder than it sounds: you need a solid understanding of your business model; an awareness of what’s most at risk; and a clear idea of where to draw the line between success and failure. Metrics measure not only the health of your business, but also your journey to product/market fit; the value of your company; and the reliability of your underlying infrastructure. Join Lean Analytics co-author Alistair Croll for an all-day, in-depth look at analytics, measurement, and working with data. We’ll cover:

  • The five stages of growth every company goes through, and how they guide your choice of metrics
  • Six business-model archetypes and their unique measurement challenges
  • What “good enough” looks like for fundamental metrics
  • How to think about cohorts, segments, percentiles, and histograms
  • Measuring and aggregating infrastructure KPIs such as latency and availability
  • Using the Lean Analytics cycle to improve through experimentation

This workshop is relevant for people working in standalone startups and for corporate entrepreneurs. It will combine presentations, case studies, and interactive discussion of the audience’s specific measurement challenges. Attendees need not be technical but should come armed with a basic understanding of web analytics, business metrics, and their current business model, plus a willingness to share with one another.

Data analytics clusters with one-click
Speaker: Lajos Papp, HortonWorks Place: Dealogic Time: 9:00-16:00

You are a Data Scientist, and just heard about Pig, Spark, Hive, Zeppelin. You would like to give them a try, and use an “Interactive Notebook” on top of your data. For that you need a Hadoop cluster, but provisioning it is known to be non-trivial.

We will prove it wrong and learn how to create an analytics cluster with one-click (actually one line in the terminal). You can use the same process on your laptop, or in any of the public clouds: Amazon, Google, Azure.

Under the hood there will be some Docker Containers involved, but “it just worksTM”.

Building Recommendation Products 101
Speaker: Esh Kumar, Spotify Place: Emarsys Time: 9:00-16:00

In this workshop, we will build a recommendation system from scratch using Collaborative Filtering. On the way, we will explore some gotchas involved that you have to look out for.

Algorithmic recommendations arise as soon as human curation becomes unscalable. Perhaps you would like to suggest wine pairings for someone buying Cheese & Crackers. The 1954 Godzilla movie for someone who is clearly obsessed about Jurassic Park.

Building recommendation features sometimes involves playing developer, designer & data scientist. This is not as overwhelming as it sounds. Most of the problem is understanding what data you already have; how little you can get away with asking from the user. Our goal is to build a similarity graph, something that takes all this data, recommend timely and right matches when the user needs them. Another problem is to be sensitive to user feedback, learn from your mistakes and recover.

One incredibly powerful tool that helps with this is Collaborative Filtering. It works well with both Netflix style ratings or more subtle implicit data like skips, how much of a song you played etc. We will go over a few classic product features that can be powered by Collaborative Filtering, get into some machine learning gotchas that can arise. Some drawbacks like the cold start problem and how to tackle them.

Introduction to Data Exploration and Predictive Modeling
Speaker: Evan Miller Place: Prezi - Silent Area Time: 9:00-16:15

You can't dive into Big Data without first understanding the fundamentals of Small Data, any more than you can prepare a meal for 200 people without first knowing how to cook yourself dinner. In the first part of this all-day, hands-on workshop, you'll learn how to visualize, summarize, and explore an unfamiliar data set right on your laptop -- and ask questions of the data in a statistically rigorous way.

Are two things correlated with each other? What do pictures tell us? How can we confirm our visual discoveries with statistics? In the second part of the workshop, you'll learn how to leverage correlations in the data into powerful predictive models that deliver concrete business value. With this foundational knowledge you'll be able to ask the right questions of any data set -- and start getting relevant answers.

This workshop requires a Mac running OS X 10.8 or later. No programming of any kind is required. A free copy of Wizard Pro for Mac is included in the workshop price.

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CRUNCH is a non-profit conference. We are looking for sponsors who help us make this conference happen.
Take a look at our sponsor packages and contact us at hello@crunchconf.com


Contact

Crunch Conference is organized by

Ádám Boros
Ádám Boros
Marketing Intern, Prezi
Attila Balogi
Attila Balogi
Event manager, Prezi
Attila Petróczi
Attila Petróczi
Data Analytics Manager, Prezi
Balázs Szakács
Balázs Szakács
Business Intelligence Manager, Ustream
Bernadett Otterbein
Bernadett Otterbein
Sponsor manager, Ustream
Dániel Molnár
Dániel Molnár
Senior Data & Applied Scientist, Microsoft Deutschland GmbH / Wunderlist Team
Gergely Hodicska
Gergely Hodicska
VP of Engineering, Ustream
Julianna Göbölös-Szabó
Julianna Göbölös-Szabó
Data Engineer, Secret Sauce Partners
Katalin Marosvölgyi
Katalin Marosvölgyi
Travel and accommodation manager, Prezi
Mihály Hazag
Mihály Hazag
Engineering Manager, Ustream
Medea Baccifava
Medea Baccifava
Head of conference management, Prezi
Richárd Gazdik
Richárd Gazdik
UI Designer, Ustream
Tamás Németh
Tamás Németh
Data Engineer, Prezi
Zoé Rimay
Zoé Rimay
Data Analyst, Prezi
Zoltán Prekopcsák
Zoltán Prekopcsák
VP Big Data, RapidMiner
Zoltán Tóth
Zoltán Tóth
Big Data and Hadoop expert, RapidMiner; Teacher, CEU Business School

Questions? Drop us a line at hello@crunchconf.com

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