Bringing DevOps, Devs and Data Scientists together around Big Data
Malaga, 10th-12th May 2023
{Main: Topics}
Distributed Systems
DevSecOps+QA
General Sofware Engineering
Data Engineering
Data Science
Data Visualization
Microservices & Cloud
Others
{Speakers}
"No unicorns, no caticorns, just software development"
Heidi Howard
Senior Researcher at Microsoft
Heidi Howard
Senior Researcher at Microsoft
I am a Senior Researcher in the Confidential Computing group at Microsoft Research Cambridge. My research sits at the intersection between the theory and practice of distributed computing, with a focus on developing resilient and trustworthy distributed computer systems. Previously, I was a Research Fellow in Computer Science at Cambridge University’s Trinity Hall, an Affiliated/Visiting Researcher at VMware Research, and an Affiliated Lecturer at Cambridge University’s Department of Computer Science and Technology. I received my Ph.D. from Cambridge University in 2019 for my research on Distributed Consensus. I am probably best known for my work on the Paxos algorithm, and in particular, the invention of Flexible Paxos.
Confidential Consortium Framework: Building Secure Multiparty Applications in the Cloud (Without Handing Over the Keys to the Kingdom!)
In the pre-cloud era, computer systems were operated by the organizations which depended upon them. This on-premises approach gave organizations great power over their systems, however, “with great power comes great responsibility” and organizations were left with the ongoing burden of deploying and managing their own infrastructure. Today, Cloud computing has removed much of the responsibility of deploying systems, however, it has also removed much of the power that organizations once had. Organizations must place their trust in the cloud to secure the confidentiality and integrity of their data.
In this talk, I'll consider whether it is possible to regain control over data in the cloud (great power with none of the responsibility) and even enable multiple untrusted parties to compute together on untrusted infrastructure. I’ll introduce the Confidential Consortium Framework (aka CCF), an open-source framework for building a new category of secure multiparty applications with confidentiality, integrity protection, and high availability. CCF utilizes hardware-based trusted execution environments for remotely verifiable confidentiality and code integrity, backed by an auditable and immutable distributed ledger for data integrity and high availability. CCF even enables application developers to bring both their own application logic and a custom multi-party governance model, in the form of a programmable constitution. By the conclusion of this talk, I hope to have convinced you that distributing systems does not necessarily mean distributing trust in the era of confidential computing in the cloud. You can learn more about CCF today at: https://ccf.dev/
Andy Pavlo
Associate Professor of Databaseology at Carnegie Mellon University
Andy Pavlo
Associate Professor of Databaseology at Carnegie Mellon University
Andy Pavlo is an Associate Professor (Indefinite Tenure) of Databaseology in the Computer Science Department at Carnegie Mellon University. He is also the co-founder of the OtterTune automated database optimization start-up (https://ottertune.com). He is from the streets.
Why Machine Learning for Automatically Optimizing Databases Doesn't Work
Database management systems (DBMSs) are complex software that requires sophisticated tuning to work efficiently for a given workload and operating environment. Such tuning requires considerable effort from experienced administrators, which is not scalable for large DBMS fleets. This problem has led to research on using machine learning (ML) to devise strategies to optimize DBMS configurations for any application, including automatic physical database design, knob configuration, and query tuning. Despite the many academic papers that tout the benefits of using ML to optimize databases, there have been only a few major success stories in industry in the last decade.
In this talk, I discuss the challenges of using ML-enhanced tuning methods to optimize databases. I will address specific assumptions that researchers make about production database environments that are incorrect and identify why ML is not always the best solution to solving real-world database problems. As part of this, I will discuss state-of-the-art academic research and real-world tuning implementations.
Nadieh Bremer
Data Visualization Artist at Visual Cinnamon
Nadieh Bremer
Data Visualization Artist at Visual Cinnamon
Nadieh Bremer is a data visualization artist that once graduated as an Astronomer, started working as a data scientist before finding her true passion in the visualization of data. As 2017's "Best Individual" in the Information is Beautiful Awards, and co-writer of "Data Sketches", she focuses on visuals that are uniquely crafted for each specific dataset, often using large and complex datasets while employing vibrant color palettes. She's made visualizations and art for companies such as Google News Lab, Sony Music, UNICEF, the New York Times and UNESCO.
Visualizing Connections
Connections are a part of us, of the world. From the connections between people, between cultures, within language, and more. In these days when more data is collected daily than we could ever hope to explore, the variety in connections being gathered is opening up the possibility to visualize these (often complex) networks. During this talk, Nadieh will take you through the design process of several of her (interactive) data visualization works, from personal projects to client work. The common thread they all share, is that they all reveal connections, but all differently. From a family tree of 3000 people connected to the European Royal houses, to those existing between our Intangible Cultural Heritage created for UNESCO, to connections we have drawn in the night skies, something with cats and dogs, and more. Revealing that all types of connections are unique and revealing the intricacies that lie within them requires a creative, iterative and custom approach.
Santiago Valdarrama
Machine Learning Engineer at his own
Santiago Valdarrama
Machine Learning Engineer at his own
Santiago is a Machine Learning Engineer instructor. He has a Master's in Machine Learning from the Georgia Institute of Technology and two decades of experience building software for some of the largest companies in the world. He co-founded bnomial.com, where he publishes daily Machine Learning questions and competitions.
We made our robots talk!
Is there a better way to communicate with a robot than using natural language? In this talk, you'll learn how we integrated Boston Dynamic's Spot robot with ChatGPT. You'll learn how we use computer vision to solve impossible problems and how ChatGPT 10x'd Spot's capabilities overnight.
Diversity Programme
JOTB2022 is committed to supporting members of underrepresented groups who may not otherwise have the opportunity to attend the event for financial, social or additional reasons. This includes (but is not limited to): People with functional diversity ,women and LGBTQIA+ people.
Our goal for this year is to provide 5 free tickets, a 50% discount to each of these communities and if necessary will try our best to support their accommodation.
Use this link to apply for free tickets, discount and support.
{Sponsors}
The perfect event to be in touch with devs, DevOps & Data Scientists dealing with Big Data!
Location
FYCMA - Palacio de Ferias y Congresos
- Av. de José Ortega y Gasset, 201, 29006 Málaga, Spain
- info@jonthebeach.com
- (0034) 644 398 316
- 10-12th May 2023, 9:00 - 18:00