Datanova On-Demand

Starburst is on a mission to enable next generation analytics anywhere. In this session, hear from Starburst’s Co-Founder and VP of Product, Matt Fuller, as he gives an overview of our 2022 product roadmap, and how it can help advance your journey towards your Data Mesh vision.

Providing better patient care all comes down to having access to the right information at the right time. Hear Richard Jarvis, CTO at EMIS Health, discuss how he and his team are making new scientific discoveries with Starburst and changing the way we treat COVID-19.

On the surface, the idea of a Data Mesh and a Data Fabric can sound similar. But they involve vastly different technical approaches. This session, moderated by former Gartner analyst Sanjeev Mohan, will start by analyzing the technical implementation details of a Data Mesh, and then comparing and contrasting that to Data Fabric. Ultimately both solutions are valid approaches and can co-exist. Sanjeev will be joined by Daniel Abadi, professor of Computer Science at the University of Maryland, and Zhamak Dehghani, founder of the Data Mesh framework, for this technical discussion.

Building & sharing data products is at the heart of the Data Mesh concept. Data products are the future of better & faster insights that drive new monetization opportunities. In this Master Class, Teresa Tung, Accenture’s Cloud First Chief Technologist will teach you how to identify a data product opportunity, how to sell it within your organization, and how to get started once your concept is sold. This session will include a hands-on workshop so you leave with personal next steps unique to your companies’ data product opportunity.

The Data Mesh is poised to replace data lakes and data warehouses as the dominant architectural pattern in data and analytics. By promoting the concept of domain-focused data products that go beyond file sharing, Data Mesh helps you deal with data quality at scale by establishing true data ownership.In this talk, Zalando’s Max Schultze and Thoughtworks’ Arif Wider will share their findings and insights from O’Reilly’s latest market research report, Data Mesh in Practice. You will gain an in-depth understanding of the steps involved in a Data Mesh journey, along with common pain points and best practices.

Since 2018, AWS has started releasing new generation of instances using custom design silicon and introducing the Graviton series of processors. During this session, you’ll learn more about why AWS has chosen to design its own silicon, how customers can benefit from better performance at a lower cost, and how Starburst leverages those processors to bring the best level of price/performance to its customers.

In 2014, British Telecom launched its big data platform not as a centralized enterprise analytics platform but as a self-service data infrastructure. In four years, over 100 data products were developed in 7 business domains. But how did they do it? In this session, Phillip Radley, who spent 30+ years at British Telecom, will share key lessons learned through establishing and scaling a self-service data infrastructure. Learn how to fund and bootstrap a MVP, secure executive buy-in, and overcome challenges as you get ready to build your own!

Vista’s mission is to be the design and marketing partner for millions of small businesses around the globe. Learn from Sebastian Klapdor, EVP & CDO at Vista, how they leverage the Data Mesh to build scalable data products to generate sustainable value for their customers and business stakeholders. Hear how the company dramatically increased efficiency and velocity ramping up 25+ autonomous data product teams with end-to-end responsibility working in the mesh and delivering on its vision to make Vista one of the world’s most iconic data-and-analytics-driven companies.

Learn how TSYS, a leading global payments provider in more than 100 countries worldwide, is building a next-generation data platform leveraging Starburst and Delta lake. In this session, hear Mahesh Lagishetty, Vice President of Data Engineering, and Hariharan Banukumar, Director/Lead Data Architect at TSYS share their journey and how they’re building a next generation cloud-agnostic data platform, moving from a centralized Data Lake ownership to embrace a distributed Data Mesh architecture backed by Starbust and built on Delta Lake. The talk will outline the ongoing efforts to make the creation of data products as simple as applying a template.

In this session, Tableau’s Vlad Usatin will give a demo by connecting Tableau directly to a Starburst instance. It will launch by creating a data model while talking to it using natural human language. After having a question and answer dialogue with Tableau, it will build a dashboard with clicks, not code. For every interaction, you’ll witness, Tableau generate dynamic SQL queries which will output dashboards rendered by the Tableau VizQL graphics engine (VizQL was designed under guidance from three-time Academy Award winner, Pat Hanrahan).

Learn about the state of data, uncover emerging trends, and get ahead on your data strategy. In this session, Starburst will unveil the results from custom research conducted by EMA on the most important data and analytics topics with commentary from Tom Nats, Director of Solutions at Starburst, Karl Eklund, Principal Architect at Red Hat, and William Schoeppner, Director, Research and Consulting at EMA. You’ll walk away with valuable insights.

If you’re struggling with data quality, data ownership, and data governance, this session is for you. Starburst’s VP of Data Mesh Consulting Services Adrian Estala will offer a fresh perspective on driving ROI with Data Mesh. Take the Data Mesh readiness assessment in real time and learn how to create demand for data sets that will solve business problems you’ve faced for years.

Today, metadata is everywhere. Every component of the modern data stack and every user interaction on it generates metadata. This in turn has led to an explosion in the size and scale of metadata making it difficult for centralized data engineering and reporting teams to fully leverage it. Today teams struggle to keep pace with the “service requests” and ramp up on the much needed domain expertise.

In this session, you’ll hear about the key components of a metadata platform, how it can help meet enterprise governance standards, and how it powers the Data Mesh architecture from Prukalpa Sankar, Co-founder of Atlan.

Get up close and personal in a lab with Trino/Presto’s original creators as they walk you through Starburst’s fully managed cloud offering, Starburst Galaxy. Follow along after setting up a Starburst Galaxy cluster with $500 worth of free credits. Dain will show you how to set up some catalogs in AWS to connect to a preexisting dataset and run a few queries. Then he will walk through amazing Galaxy features like Insights, auditing, security roles, cluster scaling, and much more! Don’t miss this exciting lab that shows you the easy button way of deploying and managing your Trino cluster. Those who attend this session and sign up for Galaxy get a free Commander Bun Bun plushie and other Trino swag!

By treating data as products, organizations have the ability to make more informed business decisions faster than ever before. In this lab, you will have the opportunity to learn from Starburst’s Director of Engineering, Colleen Tartow and Head of Data Products, Vishal Singh, how to use the Starburst Enterprise interface to create Data Products, providing business context around datasets so that data consumers can explore, discover, and analyze data with ease.

Migrating to a Data Mesh? Great! But how do you know you can trust your data? Barr Moses, CEO & Co-Founder of Monte Carlo, discusses how best in class data teams are applying data observability to their distributed data architectures to achieve end-to-end trust and reliability at scale. This 15-minute talk will cover key tenets of data observability and what this looks like in practice for companies adopting the Data Mesh.

Learn all about strategy, implementation, and execution of Data Mesh first hand from Zhamak Dehghani, the founder of the Data Mesh paradigm. She’ll cover how to approach executing Data Mesh as a component of the organization’s data strategy and how to implement Data Mesh through evolutionary milestones.

Gone are the days of monolithic data architectures. Are you Data Mesh ready? In this session, Andy Mott, Solutions Architect at Starburst will explain everything Data Mesh.

In this session, Accenture’s Cloud First Chief Technologist, Teresa Tung will highlight the key benefits of democratizing data and share how the Data Mesh paradigm can enable data access by unlocking the value of distributed data.

Moderated by Paul Gillin, a senior editor at SiliconANGLE, this forward looking panel featuring Zhamak Dehghani, Director of Emerging Technologies at Thoughtworks and Richard Jarvis, Chief Technology Officer at EMIS Health will explore the huge impact that industry leaders are witnessing and expecting across different verticals with Data Mesh.

Hear Valli Musti, Engineering Leader at Priceline, share their story of innovation and their data and analytics journey with Starburst with Data Mesh as their core architecture.

Siloed data obstructs your ability to make intelligent business decisions. In this session by Ken Seier, North American Strategic Lead, Chief Architect, Data & AI at Insight, learn how Data Mesh facilitates data sharing and helps with decision making.

In this session, hear Kirk Borne discuss how real-time analytics at the Intelligent Edge are being accelerated by AI and ML, the impact of COVID-19 on user behavior patterns, and how trends are being utilized by data scientists to yield deeper insights and build smarter models.

The creators of open source Trino (formerly PrestoSQL) will share best practices and customer use cases of how Trino is accelerating data science at the world’s leading organizations.

Paco Nathan, renowned data scientist, will moderate a session on data lakehouse, an emerging data architecture. Panelists will explore the meaning of data lakehouse, discuss industry trends, and identify areas of opportunity.

What does domain-driven ownership of data mean for the data scientist? This forward thinking panel will be moderated by Red Hat with panelists Zhamak Dehghani, who coined the Data Mesh term, Daniel Abadi, Darnell-Kanal Professor of Computer Science at University of Maryland, College Park, and a Starburst customer.

Come hear Mitchell Posluns, Senior Data Scientist, Assurance share how they accelerated their digital transformation journey, navigated challenges, and achieved results in a fireside chat with Jess Iandiorio, Chief Marketing Officer at Starburst.

Learn how Red Hat OpenShift Data Science can simplify and accelerate your AI/ML projects. Karl Eklund, Principal Architect at Red Hat will share how you can take advantage of a broad ecosystem of tools including Starburst, leverage cloud computing resources to have access to the processing power you need, and create and share projects without changing your existing tools or workflows.

Educator, television host, and best selling author Bill Nye (popularly known as Bill Nye the Science Guy) will talk science, space, and data. Buckle your seat belts as we take a ride into outer space and learn the many wonders of the universe.

X Analytics, a concept coined by Gartner, is delivering on the promise of the “big data” era by adding new revenue streams, removing technological barriers, and driving innovation at the world’s leading organizations. While the first 10 years were about storing and managing data, now it’s time to analyze it at speed and scale. In this session, Justin and Sudhir will discuss X Analytics, its building blocks, real world use cases, and more.

Data consumers have waited too long. Data engineers have spent too much time in frustratingly complex ETL pipelines. Data leaders have been left without a clear path forward to get the business the data they need, when they need it, while achieving compliance & security goals. Attend the Starburst Product vision presentation to learn how we’re helping companies chart an immediate path forward bringing data teams of all sizes fast secure, and scalable access to all data.

Inquiring minds want to know: Who will win the battle for the center of data gravity? Will our future state have the majority of data residing in a warehouse, lakehouse, or data lake? This engaging panel will include three speakers, each who advocate the merits of these models and why they think it could win, and then we’ll have a fun audience vote on which model is likely to be crowned the winner.

We are all surrounded by – and creating – masses of data every single day. What you see with your eyes? That’s data. What you hear with your ears? Data. Images, text, documents, audio, video and all the apps on your phone, all the things you search for on the internet? Yet again, that’s data. Every single day, everyone around the world is using data. So, now we need to bring this value to our businesses. It is imperative that Data leaders, scientists, and analysts champion data literacy inside their organizations.

Leading data scientist Paco Nathan will moderate a panel with Dain Sundstrom, David Phillips, and Martin Traverso, the co-creators of the open source project, Trino (formerly known as Presto SQL). They will dive into the importance of having a “single point of access” irrespective of where the data resides and share how Trino is helping data science and data engineering teams analyze vast amounts of data from a variety of data sources and formats.

When the pandemic hit digital adoption leapfrogged 5 years into the future. All of our behaviors changed overnight – how we live, work, handle our finances, and get our groceries. This has had a massive impact on data strategies as analytics models were based on historical behavior patterns. The pandemic broke the models. What now? The data pandemic series, moderated by Tony Baer, includes 10-minute lightning talks from companies who detected the shift in data, rapidly figured out how to get new data sources into their analytics, and their outcomes.

When the pandemic hit digital adoption leapfrogged 5 years into the future. All of our behaviors changed overnight – how we live, work, handle our finances, and get our groceries. This has had a massive impact on data strategies as analytics models were based on historical behavior patterns. The pandemic broke the models. What now? The data pandemic series, moderated by Tony Baer, includes 10-minute lightning talks from companies who detected the shift in data, rapidly figured out how to get new data sources into their analytics, and their outcomes.

Learn about the state of data, uncover emerging trends, and get ahead on your data strategy. In this session, John and Michael will unveil the results from custom research conducted by Starburst in collaboration with Red Hat and Enterprise Management Associates. From data storage trends to the impact of the current pandemic and the most important analytical capabilities, you’ll walk away with valuable insights.

Many enterprises are investing in their next-generation data lake, with the hope of democratizing data at scale to provide business insights and ultimately make automated intelligent decisions. Data platforms based on the data lake architecture have common failure modes that lead to unfulfilled promises at scale. To address these failure modes we need to shift from the centralized paradigm of a lake, or its predecessor data warehouse. We need to shift to a paradigm that draws from modern distributed architecture: considering domains as the first-class concern, applying platform thinking to create self-serve data infrastructure, and treating data as a product.

While the technology advances of the past decade have addressed the scale of volume of data and data processing compute, they have failed to address scale in other dimensions: changes in the data landscape, proliferation of sources of data, diversity of data use cases and users, and speed of response to change. Data mesh addresses these dimensions, founded in four principles: domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance. Each principle drives a new logical view of the technical architecture and organizational structure.

The data consumer population has transformed to include almost everyone within any given organization. These new circumstances have pressed the traditional approach of “a single source of truth” to a breaking point. Like many in the cloud, they follow conventional advice and centralize their data into a semi-structured and highly available data lake. Many large analytics environments have EDW, CDW, HDFS, and dozens of other systems required for their analytics workload. Some architectures work better on-premise instead of running in the cloud and SLAs sometimes require you to host this data in either on-premise or cloud servers. This has thrust a new and intimidating challenge upon data teams and the businesses they support; how to execute on the demand for hybrid analytics? In this talk, Bryan Aller discusses Comcast’s journey of moving their analytics infrastructure to the cloud and why they now choose to use a hybrid architecture for their analytics.

Ten years ago Dave McCrory coined the term ‘Data Gravity.’ He had the vision to understand that while more and more data sources would be created, more teams would start accessing those data sources, and it would be increasingly challenging to move that data. Join this panel to hear from Dave his view of what’s happened since the term was coined, and dive deep on data gravity opportunities at challenges at Scotiabank.

Cloud migration goes beyond “lift and shift” from on-prem to cloud. Organizations move for a variety of reasons including agility, elasticity, reliability, availability, and accessibility. It involves thoughtful planning and careful execution. In this session, moderated by Blue.cloud, Ivan and Michelle will share their cloud migration journeys and lessons learned.

Governance has always been a core component of data management. But now it’s more important than ever with the rise of regulatory compliance requirements and the complexities presented by disparate data sources. In this session, Sandipan will highlight the evolution of the global data platform at Rakuten, talk about their governance strategy built on their SuperDB platform, and share how they ensure the right level of access to critical data while ensuring compliance.