Crunch Data Engineering and Analytics Conference Budapest October 18-20, 2017

Tickets

CRUNCH is a use case heavy conference for people interested in building the finest data driven businesses. No matter the size of your venture or your job description you will find exactly what you need on the two-track CRUNCH conference. A data engineering and a data analytics track will serve diverse business needs and levels of expertise.

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.

18
October
WORKSHOP DAY

Our full-day workshops will be announced soon. You need to buy separate workshop tickets to attend them.

19
October
CONFERENCE DAY #1, THURSDAY

The day will start at 9AM and the last talk will end around 6PM. After the sessions there will be an Crunch party at the conference venue.

20
October
CONFERENCE DAY #2, FRIDAY

The day will start at 9AM and the closing ceremony will end around 6PM.


Speakers

Charles Smith

Charles Smith

Manager - Big Data Platform Architecture, Netflix
Working hard to build an easy data platform at Netflix

Here is a problem: You would like to buy the next great show for Netflix. The dream is that, given your data and a question, you can find the next House of Cards with a click of the mouse. But is that the reality? Why does it seem like data engineers and analysts spend so much time talking about memory requirements and stack traces? This talk will explore the past, present, and some of the future of the Netflix data platform, as well as how we are prioritizing work that will make it easier to focus on data problems rather than the complexities of the platform.

Bio

Charles Smith leads the Big Data Platform Architecture team at Netflix, whose mission is to make using data easy and efficient. He and his team are responsible for envisioning how the data platform allows data scientists to make Netflix's service even better.

Gyula Fóra

Gyula Fóra

Data Warehouse Engineer, King
Real-time analytics at King

This talk gives a technical overview of the different tools and systems we are using at King to process and analyse over 30 billion events in real-time every day.
The core topic of this talk is RBEA (Rule-Based Event Aggregator) , the scalable real-time analytics platform developed by King’s Streaming Platform team. RBEA is a streaming-as-a-service platform built on top of Apache Flink and Kafka which allows developer and data scientists to write analytics scripts in a high level DSL and deploy them on the live event streams in a matter of few clicks.
The distinguishing feature of this platform is that new analytics jobs are not deployed as independent Flink programs, but instead, a fix number of continuously running jobs serve as backends for the RBEA platform. By streaming both the events and new scripts to the backends, scripts share both the incoming data and the state they may build up when analyzing user activity in the games. This design makes new deployments very lightweight and the whole architecture highly efficient without sacreficing expressivity.
We push the Apache Flink framework to it’s full potential in order to provide highly scalable stateful and windowed processing logic for the analytics applications. We will show how we have built a high-level DSL on the abstractions provided by Flink that is more approachable to developers without stream-processing experience and how we use code-generation to execute the programs efficiently at scale.
In addition to our streaming platform we will also introduce other tools that we have developed in order to make deployment and monitoring of real-time applications as simple as possible at scale.

Bio

Gyula is a Data Warehouse Engineer in the Streaming Platform team at King, working hard on shaping the future of real-time data processing. This includes researching, developing and sharing awesome streaming technologies. Gyula grew up in Budapest where he first started working on distributed stream processing and later became a core contributor to the Apache Flink project. Among his everyday funs and challenges, you find endless video game battles, super spicy foods and thinking about stupid bugs at night.
Gyula has been a speaker at numerous big data related conferences and meetups, talking about stream processing technologies and use-cases.

Shirshanka Das

Shirshanka Das

Principal Staff Software Engineer, Linkedin
Bio

Shirshanka is a Principal Staff Software Engineer and the architect for LinkedIn’s Data & Analytics team. He was among the original authors of a variety of open and closed source projects built at LinkedIn, including Databus, Espresso, and Apache Helix. He is currently working with his team on simplifying the big data analytics space at LinkedIn through a multitude of mostly open-source projects: Pinot, a high-performance distributed OLAP engine; Gobblin, a data lifecycle management platform for Hadoop; WhereHows, a data discovery and lineage platform and Dali, a data virtualization layer for Hadoop.

Justin Bozonier

Justin Bozonier

Lead Data Scientist, Finance & Analytics, GrubHub
Science the shit out of your business

The mission of my data science team is to make a science out of our business at GrubHub. We work on understanding how every initiative our company undertakes affects our bottomline. I will discuss how we analyze every feature shipped to production, marketing programs, customer service, and more using a variety of statistical, machine learning, and decision theoretic tools and techniques. Most importantly, I will cover how we have learned to tune these tools, not with just abstract or theoretical scores, but by connecting model error with bottom line impact.

Bio

Justin Bozonier is the author of Test-Driven Machine Learning (published by Packt) and Lead Data Scientist in GrubHub's Financial Planning & Analytics group. The founding data scientist of GrubHub's split testing efforts, his team runs the company's experiment analysis platform, develops experiments and models to tune larger business operations, and data mines experiments and operational data to look for new business opportunities and value existing programs. He has spoken previously at PyData Seattle, Kellogg at Northwestern, PyData Chicago's monthly meetup, and more.
He lives in Lake Villa, IL (just outside the greater Chicago area) with his wife Savannah and soon, their first child. In his spare time he studies math, video game development, and enjoys running.

Evan Miller

Evan Miller

Statistician, Programmer, author of the Wizard statistical analyzer
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.

Sean Kross

Sean Kross

Programmer Analyst, The Johns Hopkins Bloomberg School of Public Health
Lessons from teaching data science to over a million people

Over past few years Jeff, our colleagues, and I have taught data science to over million people online. We’re currently in the process of analyzing all of the data from those courses - so I could discuss where students succeed and fail, what lessons we’ve learned, and maybe what we could all try to improve upon as the data science community.

Gio Fernandez-Kincade

Gio Fernandez-Kincade

Co-Founder @ RelatedWorks.io. Formerly Staff Engineer @ Etsy
AI in Production

Read enough Hacker News and you will quickly become convinced that building AI products looks something like:

  1. Fire up Tensore Flow
  2. Choose your favorite network architecture (or better yet, generate one!)
  3. Pipe in tons of data
  4. Profit

That couldn’t be farther from the truth. In this talk, we’ll figure out what it really takes to ship AI products in production.

More speakers will be announced soon

If you want to be one of the speakers at Crunch 2017 submit your application via Papercall. Deadline for submission is 15th of May, 2017


Workshops

Workshops will be announced soon


Location

Meet Budapest, a really awesome city

Here are a few reasons why you need to visit Budapest

BUDAPEST IS CLOSER THAN YOU THINK

BUDAPEST IS CLOSER THAN YOU THINK

Sponsors

Platinum

Gold

Silver

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
Research & Data Science Project Manager, Realeyes
Balázs Szakács
Balázs Szakács
Business Intelligence Manager, IBM Budapest Lab
Dániel Molnár
Dániel Molnár
Senior Data & Applied Scientist, Microsoft Deutschland GmbH / Wunderlist Team
Katalin Marosvölgyi
Katalin Marosvölgyi
Travel and accommodation manager, Prezi
Medea Baccifava
Medea Baccifava
Head of conference management, Prezi
Tamás Imre
Tamás Imre
Lead Analyst, Prezi
Tamás Németh
Tamás Németh
Data Engineer, Prezi
Zoé Rimay
Zoé Rimay
Software Developer, Morgan Stanley
Zoltán Prekopcsák
Zoltán Prekopcsák
VP Big Data, RapidMiner
Zoltán Tóth
Zoltán Tóth
Big Data and Hadoop expert, Datapao; Teacher, CEU Business School
Ryan McCabe
Ryan McCabe
Data Analyst, Prezi
Gergely Krasznai
Gergely Krasznai
Data Analyst, Prezi

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