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How data engineering fails

Benn Stancil is a cofounder and Chief Analytics Officer at Mode, a modern BI platform that is designed for data teams. Benn also regularly writes about data and technology at At Datanova, Benn will explore what, if anything, might slow the meteoric rise of data and analytics engineering. Over the last several years, these roles have become the new must-hire experts and must-have jobs, and the success of the industry’s standout companies—Snowflake, dbt, Fivetran—now seems all but inevitable. But could the music stop? Benn will explore a world where we aren’t at the beginning of a data revolution, but as the peak of its popularity, and where we remember data engineering not as an enduring career, but as a passing fad.

The data lies (and truths)

Albert Einstein said, “The definition of insanity is doing the same thing over and over again and expecting a different result.” At Starburst, we believe the pursuit of trying to centralize all of your data is the definition of data insanity. It cannot be achieved, and the continual pursuit of a single source of truth has wasted millions, created complexity, and delayed decisions. In this keynote, Starburst co-founder and CEO Justin Borgman will share his perspective on why companies have continued this pursuit, why now is the time to change, and how to move forward to get different (and better) results.

Starburst product vision

Our vision is to empower businesses with the tools and technologies they need to thrive in a growing and changing data-driven economy. We believe in challenging the status quo for data management and analytics, so businesses can unlock the full potential of their data, wherever it resides. In this session, Starburst’s SVP of Product, Alison Huselid, gives an overview of our 2023 product roadmap, and how it can help advance your data journey. Join Alison, and our mission to support the next generation of analytics anywhere, and empower our customers the freedom to be curious.

Data Mesh 2033: What happened?

The year is 2033. Companies who pursued a data mesh strategy starting in 2022 pontificate about what the next 10 years of their journey looks like: Expected wins, expected hurdles, and new dynamics they expect to enter their data world. In this panel discussion, hear from Sachin Menon from Priceline, Bryan Aller from Comcast, and Dileep Pournami from NatWest – who have already begun their data mesh journey. You’ll hear quick lightning talks from each company on their “Why” for data mesh, and “Where” they are today, and then we’ll have a discussion about what the next 10 years might look like.

The future of Financial Services with distributed data

Calculated speed that reduces risk while maximizing competitive edge and return on assets is key to the long-term success of financial firms. This panel features Bank of America, Wells Fargo, and Accenture leaders. As regulatory scrutiny and data sovereignty requirements increase, financial firms are evolving the role data plays in risk mitigation and growth. In this session, we will explore how these institutions are evolving their data and analytics strategies to adapt to changing customer expectations, combat the increasingly complex web of financial crimes, and increase productivity while reducing costs.

The future of data: A BCG study

In a landmark research study by Boston Consulting Group, the macro trends shaping the data & analytics imperative are explored. As analytics use cases proliferate, and more data is created, how will companies illuminate dark data and make it easy to consume? Given today’s economic uncertainty, how can companies solve for faster data consumption while finding a path to analytics cost control?

Join Pranay Ahlawat, Partner and Associate Director, Enterprise Software & Cloud at BCG as he presents their latest findings on how companies can derive the most value from an organization’s single most important asset: Data. Pranay will be joined by Steven Huels, Senior Director, AI Product Management and Strategy at Red Hat, and Adrian Estala, Field Chief Data Officer at Starburst.

Teaching data engineers to be curious

Organizations of all sizes strive to proclaim the badge of honor that their company is “data-driven.” No decision should be made without the corroborative analysis to support the answered question causing the decision, but what if your business is asking the wrong questions? It is common for data consumers to toss idealistic requirements over the fence without understanding the data sources at large, as this responsibility falls to the data engineer who works with the data daily. To transition from data to information, data engineers must cultivate a habit of acting as curious challengers on behalf of the data they are working with and be given the power of a stakeholder within the data manipulation process.

Building data products requires a rethink (And a holistic approach)

The way we have been building analytics takes too long, incurs high costs and often only partially meets business requirements. Data products provide a way out of this mess. But to build data products efficiently, a cohesive mindset is needed. One where the key elements comprising data quality, data governance, DataOps, observability, and data security become equal first citizens. This session delves into how data and analytics teams are helping successful organizations get the most out of their data assets.

Green Big Data: How can your data strategy protect the environment (And help you hit ESG goals)?

Environment, Social, and Corporate Governance (ESG) goals have grown from aspirational ideas to business imperatives. Not only is it the right thing to do to help protect our planet, but ESG initiatives are also increasingly regulated. On the Datanova stage, Dan O’Riordan, VP – Global Head of Platform & AI Engineering at Capgemini, will explore: How can your data strategy reduce your carbon footprint? Might there be a path to identifying opportunities to hit your ESG targets by building data products that help reduce waste and carbon emissions, while improving supply chains? The concept of data products means local responsibility on quality. This will include relevant data elements such as green energy use and waste. Further, future data ecosystems will need to be collaboratively built on a foundation of sustainable, conscious data products.

How data leaders are closing the gap between data management & analytics

In this fireside chat, Michelle Boston, Managing Director of Data Management Technology, and Amy Avery, Senior Vice President and Head of Analytics, Research & Insights, will discuss the collaboration between their teams at Bank of America to drive business forward through the analysis of critical data while maintaining data security and governance. The conversation will be moderated by Cindi Howson, host of The Data Chief Podcast and CDSO at ThoughtSpot. This keynote will provide insights on how to achieve faster analytics and broader data usage while prioritizing data security.

Accelerate your time to value in a multicloud world

With its hybrid data architecture, Starburst provides the fastest, most efficient analytics engine for data warehouses, data lakes, or data mesh. How can you take that innovation and accelerate time to value further? Partner with industry leader, Dell Technologies to help customers overcome the challenge of data silos across their Multicloud environment spanning public cloud, private cloud and on-premises. Join to learn how Dell Technologies and Starburst are helping IT and data leaders address the data challenges of multicloud era to accelerate time to value.

Data disrupted: A fireside chat with Justin Borgman, Martin Casado, Zhamak Dehghani, and Teresa Tung

As data architectures evolve there is a big question to answer: How do we best evolve people, too? Is it more pragmatic to adopt a “Modern Data Stack” which is iterative and requires less people and process change? Is it better to take a more holistic approach, which Data Mesh proposes, pushing people & technology forward at once? What is the best next step toward companies becoming data-driven?

This fireside chat, moderated by Justin Borgman, Starburst Co-Founder & CEO, will bring the stars of the data world to the table to discuss the hard and soft side of data: People and technology.

Data Rebels and Partner of the Year 🏆 Awards Ceremony

Join us for Starburst’s Inaugural Data Rebel Awards ceremony! Celebrating the best customers, partners, and leaders that embody the spirit of a true “Data Rebel” – those that dare to push the boundaries of what’s possible to shape the future of data. During this session, we’ll announce the winners of Starburst’s customer awards, role-level awards, and partner awards, sponsored by AWS.

Fun with founders: Expectation versus reality with Matt, Drew, Max and Michel

Being a startup founder requires a special blend of ambition, drive, and resilience. Not only does the role demand grit, it also calls for the ability to dream. Hear the unique perspective from founders in the data space, Matt Fuller from Starburst, Drew Banin from dbt, Max Beauchemin from Preset, and Michel Tricot from Airbyte. Listen as our star studded founder panel takes part in an open discussion about the future of data platforms, the importance of open source, the ecosystem, and paying attention to user experience.

Is data mesh the end of data engineering?

Best-selling author Joe Reis debates the benefits and pitfalls of the Data Mesh for Data Engineers.

Is data mesh the pinnacle of data engineering innovation, or will it challenge data engineering as we know it? Maybe both? Join Joe Reis, best-selling author of Fundamentals of Data Engineering, along with Starburst technical experts, Colleen Tartow and Andy Mott, for a fun and lively Oxford style debate on whether the adoption of Data Mesh creates opportunity or spells doom for the field of Data Engineering.

Data Products for everyone featuring Glovo

Let’s put theory into practice. Listen to Simone Grandi, Glovo’s Data Platform Engineering Manager, discuss their experience implementing data mesh firsthand. Hear about their organizational challenges, future strategies, and interest in data as a product. We’ll then watch Vishal Singh, Head of Data Products at Starburst, use Starburst Data Products to demonstrate a quick and easy way to get started with setting up and running a data mesh in your organization.

Building a data analytics platform with a lakehouse at 7bridges

7bridges is an AI-powered logistical supply chains platform and was named one of the top 15 hottest AI startups in Europe in 2020. Hear from Simon Thelin, lead data engineer, about how this fast growing company is using Starburst Galaxy to power reporting and AI.

The architecture behind fault-tolerant execution

While Trino is mostly known for ad-hoc analytics, companies like Salesforce and Lyft were using Trino to run long-running queries at a petabyte scale. With the implementation of fault-tolerant execution, running those queries becomes even easier. Learn about the architecture behind the feature which makes this possible and the potential capabilities it can bring to your own ecosystem.

Unlocking the power of analytics and AI to create data products and apps at Deloitte

With the advent of data mesh and data products, data has become more important—and more accessible—to the enterprise than ever before. With Deloitte’s CFG Workshop —a self-service analytics toolkit powered by SEMOSS — users can leverage an integrated AI and BI platform to access raw data, transform and visualize it, and expose it through API as a data product. In this demo, we will use a mock diabetes registry data sourced from starburst, federate it with personal data to build a data product from scratch and expose that product back to Starburst as a database.

For more information on Deloitte CFG and SEMOSS, go to:

The best of both worlds: Achieving query latency and flexibility with Apache Pinot and Trino

Apache Pinot is an open-source, distributed OLAP datastore built to provide real-time analytics at ultra low-latency and extremely high throughput, typically needed for user facing workloads. Trino is a distributed, ANSI compliant SQL query engine that can process complex interactive queries over various data sources. By combining the power of Pinot and Trino, we unlock the ability to do complex ad-hoc analysis on real-time data as well as federate queries across Pinot and other data sources. Furthermore, by intelligently pushing down predicates and aggregation functions to Pinot, we can accelerate overall query performance. Listen to Chinmay Soman, founding engineer at Startree, discuss Apache Pinot and the architectural contexts in which combining both technologies together provides speed and scalability without sacrificing flexibility.

dbt and Starburst: better together

The mission of dbt Labs is to “empower data practitioners to create and disseminate organizational knowledge”. They believe this is best accomplished using dbt, which enables SWE best practices on top of a data platform that provides a central location for data professionals to get their work done. Starburst Galaxy accomplishes this regardless of where the data “lives”.
In this off-the-cuff session, Anders Swanson, Partner Engineer at dbt Labs will:
– quickly share dbt fundamentals,
– make the case that data work is software engineering work
– showcase the power couple of dbt and Starburst

In data & people we trust: Building a reliable data platform with Trino and data observability

It’s one thing to have data, but how do you know your company can rely on it? Assurance is an online distribution platform for insurance and financial products that operates between consumers who are looking for coverage, the agents that have the expertise to educate on the available products, and the actual interface into the many products available. To ensure that data is reliable and delivers quick value to customers and internal stakeholders alike, Assurance needed a way to monitor, alert to, and resolve data issues in a scalable and automated way across their Trino-powered data platform. In this tech talk, Mitchell Posluns and Zhi Zhang will share how they rolled out a comprehensive data observability strategy to improve visibility, data quality, and collaboration between data producers and consumers. They’ll also demonstrate how their data observability platform helped them identify and resolve compute-intensive and deteriorating queries run by their Trino-powered query engine, and share what’s next on their data platform roadmap.

An introduction to data contracts

As organizations grow, Data Producers and Data Consumers lose touch and critical disconnections within the organization start to arise. Data Producers should not be held responsible for support they never agreed to, yet Data Consumers cannot be expected to own data from source systems they didn’t build. Consumers should have the power to define the schema they need instead of being forced to adapt to low-quality data. The answer? Data Contracts. Learn from Chad Sanderson, Chief Operator of Data Quality Camp, how data contracts can drive a cultural change toward data-centric collaboration resulting in well-modeled, high-quality, and trusted data.

Get in the driver’s seat of your AI industrialization using Dataiku

AI is maturing at a rapid pace. And it won’t be long before business leaders knock on your door to get their AI models up and ready. According to the latest McKinsey Survey, 50% of respondents reported implementing AI in at least one business sector, a figure that has doubled in 2 years. Now it’s time for you to get in the driver’s seat. In this session, join Jed Dougherty for an exciting session to learn about the key ingredients to win the AI race without losing control. In this session, you will learn:
– What capabilities do you need in your scale strategy to orchestrate enterprise-grade AI pipelines
– Why Dataiku has become the AI orchestration platform for many enterprise customers.
– How Dataiku and Starburst can help you go from reactive to proactive in managing your Data, Analytics and AI projects

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