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Our stretch of conferences for 2025 continues next week with the highly anticipated Databricks Data + AI Summit, held in San Francisco (and online!) from June 9-12. Databricks has been making some serious waves the past few years. Their Data Intelligence Platform, built on an open lakehouse architecture, is loved by InterWorks’ data engineers and clients alike. If you’re totally new to Databricks, we recommend you give their website a visit!

For those of you who are familiar with Databricks or are simply curious what the Data + AI Summit is all about, we thought we’d put together a conference primer sourced directly from the InterWorks team members who will be attending.

This is Databricks’ biggest event of the year, so you can expect keynote sessions from the Databricks top brass. There’s also plenty of opportunity to hear directly from the Databricks development team about new features and functionality, along with hands-on demos for you to test drive them yourself. Beyond that, you can choose from over 700 sessions hosted by talent from the world’s leading organizations. JPMorgan Chase, Microsoft, Anthropic, Meta, DoorDash, PepsiCo Inc., Rivian, Coinbase, etc… These are just a few of the names on the presenter list!

Now, let’s get into the weeds. Here’s what our team is excited about for the Data + AI Summit:

Ryan Callihan, Analytics Lead
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Recently, with Databricks’ Data + AI Summit coming right on the heels of the Snowflake Summit, it’s fascinating to see what these two behemoths announce in such a short time frame. Their announcements are often made in direct competition with one another. More importantly, they incrementally shift the data landscape, as both Databricks and Snowflake heavily influence the modern data stack.

At this year’s conference, my focus is on understanding the Databricks roadmap, especially around AI/BI tooling such as Dashboards, Genie and any new agent-driven analytics features. Beyond gawking at features released in the keynote, I’m particularly interested in talking to customers to understand their enthusiasm towards and possible adoption of these tools.

Are enterprise customers willing to move critical parts of their analytics into the their data platform? If so, what does that transition look like? And what other legacy tooling are they replacing by using Databricks AI/BI instead?

Justin Lemmon, Data Lead
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There is a lot to cover here, but here’s what I’m looking at broken down by different focus areas. Some of these things are known to some degree, and we’re just excited to learn more about them. Other areas have open questions, some about specific functionality within Databricks’ and others at a higher level:

GenAI and AI Agents

• How are enterprises operationalizing GenAI/LLMs and agents for real business value?
• How do businesses assess their readiness for AI?

Data Intelligence and Governance

• What’s new in data sharing, privacy and governance (Unity Catalog, Delta Sharing, Data Clean Rooms)? What about automated policy management, data contracts and secure data sharing?
• Ethics and security around responsible AI implementation.
• Data mesh and domain-focused governance in practice.

Lakehouse and Analytics Modernization

• How are organizations modernizing analytics and BI with the lakehouse paradigm?
• How are companies optimizing cost with ever-increasing data and analytics needs?

Industry Use Cases

• What are the most impactful case studies in our clients’ industries?
• Which industry-specific compliance measures and concerns should we know about?

Data Quality and Observability

• Integration with Databricks and/or building within Databricks.
• Automated monitoring and alerting.

Other

• ELT, additional surprises.

Additional Questions

• What are the biggest blockers to AI adoption and how are leaders overcoming them?
• How are companies measuring and proving ROI from data and AI investments?
• What are the best practices for secure, governed and scalable AI solutions?
• What are recommended architectures and proven use cases?
• What skills/certifications are most in demand for data/AI teams in 2025?
• How can InterWorks support gap analyses for clients? Aaaaaand don’t forget change management!
• Which new Databricks features/tools can give our clients a competitive edge?

As you can see, there is a lot I’m hoping to learn about at this year’s summit, but I think spurring this much curiosity is a good thing. These are questions very big players are trying to answer.

Karl Riddett, Strategy Lead
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AI/BI

Is this the new “self-service” approach? What Databricks is building through LLMs and their AI models should allow easy access for ad hoc queries/one-off questions and be able to do this in a ChatGPT-like interface. Initial demos I’ve seen are looking great. Could this be the answer for non-technical personnel to get easy access to their analytical questions without having to go to a dashboard or using a tool like ThoughtSpot? We have a client that is asking exactly those questions as a dbx customer. I am going to be working with them on that strategy this upcoming week.

Metadata

The biggest opportunity for AI to help make data engineering easier is for metadata automation. Snowflake has already made major announcements this week. What will be Databricks’ answer to that? Unity Catalogue was a game-changer when they released that. What AI-driven enhancements will come to make life easier for data teams?

Cost Management

The same client in my first section wants to manage current BI tool costs. As an existing Databricks customer, they also want to keep spend at a manageable level. I plan on coming back with some best practices after the show on cost management, which I may write about in additional detail.

Matt Woods, Services Lead
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Here are a few things I’m looking forward to next week:

New Feature and Product Announcements

The tight competition between Databricks and Snowflake has led to a massive amount of development on both platforms. Combined with the two company’s summits being back-to-back (in the same convention center no less), this is a wild time of year for the data, analytics and AI spaces.

This is probably a me thing, but every year I’m fascinated to see what features and capabilities are integrated into the Databricks platform itself as opposed to relying on third-party tools. This shows what areas Databricks is prioritizing, and it has a huge impact on platform adoption, data architecture and tooling.

Customer Stories

Getting to hear real world use cases and their impact is almost always interesting and informative. It better grounds me in understanding what’s useful and what’s not from a practical standpoint.

Technical Sessions

These are a great opportunity to focus on improving knowledge and skills, and it’s hard not to love that! We also like to work on our certifications at the summit as it’s a good time to focus on a subject and use the in-person proctoring services. That’s a testing environment that many of us are more used to which can be beneficial. Plus, the swag is great!

Jackson Reber, Data Engineer
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Honestly, I’m really looking forward to just learning more about Databricks best practices in regard to pretty practical data engineering tasks and concepts. I’ve been working with dbx for close to a year now, and while I’ve interacted with many aspects of Databricks, there is still so much I haven’t explored.

There are a ton of ways to do the same thing in Databricks, so I really want to narrow in on “best practices” and recommendations from Databricks themselves on the best way to approach some of the more common data engineering tasks/problems I’ve encountered in my experience.

Most of the sessions I’ve signed up for are very much “common sense” type sessions. These aren’t talking about any flashy new tool or AI integration, but rather sessions about common data engineering tasks or problems and the best ways to approach them. Since Databricks is a newer tool in the InterWorks ecosystem, I want to use this conference as a way to really build up our internal data engineering documentation for Databricks and find the best resources for newer data engineers getting familiar with Databricks

Of course, at these conferences, it is nearly impossible to avoid sessions about flashy new tools or AI integrations, so I’m sure I’ll get some of that as well. I’m also hoping to take and pass the Databricks Certified Data Engineer Professional certification.

Chris Chang, Data Engineer
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I have four general areas I’m looking forward to at this year’s Data + AI Summit:

• General data pipeline optimization techniques
• Interoperability and data governance best practices with our current partners
• Understanding more about Apache Spark
• Meeting up with everyone else on the InterWorks team!

Explore Databricks with Us!

A lot of ground is going to be covered at the Data + AI Summit, and we expect there will be plenty of Databricks chatter to parse through. Even so, this might be the best time to dive into Databricks if you’re ready to explore lakehouse architecture. We’d love to help you on that journey! If you’re ready to learn how Databricks can impact your data stack directly, reach out to us today!