Managing flows of security related events requires working with a lot of data. Data collection, enrichment, detection of potentially dangerous or malicious activity requires orchestrating many data workflows. During this talk we will see how the Dataiku product can be used for security purposes.

Before launching an attack, cybercriminals exploit OSINT to map your digital and physical world. From crafting convincing spear phishing emails to enabling real-world intrusion, they turn public data into powerful weapons.

To fully unlock the potential of AI within KPN, scaling is key. Therefore KPN focuses on 4 pillars: AI Literacy, Governance, end-to-end implementation with business, IT, data and AI, and the expansion of our technical infrastructure. Together, these elements support the democratization of AI capabilities across the organization. With the emergence of Generative AI—especially Agentic AI—broad enablement has become even more critical. In this session, KPN will share organizational opportunities and challenges related to AI adoption at scale, and how it utilizes Dataiku as the central Data Science platform to drive this transformation.

In the GenAI era, enabling everyone to build with AI is critical. Learn how Dataiku, The Universal AI Platform™, empowers teams of all skill levels to build and deliver GenAI and agentic AI products with speed, control, and confidence. Uncover approaches for integrating GenAI and orchestrating AI agents in production across functions with the right controls, access, and oversight.

As insurers increasingly leverage IoT data to personalize policy pricing, reconciling disparate datasets across devices, policies, and insurers becomes mission-critical. In this session, learn how Nationwide transitioned from prototype workflows in Dataiku to a hardened data stack on Databricks, enabling scalable data governance and high-impact analytics. Discover how the team orchestrates data reconciliation across Postgres, Oracle, and Databricks to align customer driving behavior with insurer and policy data—ensuring more accurate, fair discounts for policyholders. With Anomalo’s automated monitoring layered on top, Nationwide ensures data quality at scale while empowering business units to define custom logic for proactive stewardship. We’ll also look ahead to how these foundations are preparing the enterprise for unstructured data and GenAI initiatives.

In regulated industries like finance, agility can't come at the cost of compliance. Morgan Stanley found the answer in combining Dataiku and Databricks to create a governed, collaborative ecosystem for machine learning and predictive analytics. This session explores how the firm accelerated model development and decision-making, reducing time-to-insight by 50% while maintaining full audit readiness. Learn how no-code workflows empowered business users, while scalable infrastructure powered Terabyte-scale ML. Discover best practices for unified data governance, risk automation, and cross-functional collaboration that unlock innovation without compromising security. Ideal for data leaders and ML practitioners in regulated industries looking to harmonize speed, control, and value.

AI agent systems hold immense promise for automating complex tasks and driving intelligent decision‑making, but only when they are engineered to be both resilient and transparent. In this session we will explore how Dataiku’s LLM Mesh pairs with Databricks Mosaic AI to streamline the entire lifecycle: ingesting and preparing data in the Lakehouse, prompt engineering LLMs hosted on Mosaic AI Model Serving Endpoints, visually orchestrating multi‑step chains, and monitoring them in real time. We’ll walk through a live demo of a Dataiku flow that connects to a Databricks hosted model, adds automated validation, lineage, and human‑in‑the‑loop review, then exposes the agent via Dataiku's Agent Connect interface. You’ll leave with actionable patterns for setting guardrails, logging decisions, and surfacing explanations—so your organization can deploy trustworthy domain‑specific agents faster & safer.

Five years ago, Rolls-Royce had no dedicated data science capabilities. Today, over 7,000 users—from coders to citizen data scientists—actively leverage AI. This transformation extends beyond technology, emphasizing AI democratization, a data-driven culture, and responsible scaling. This session explores key strategies for enterprise-wide adoption, from use case ideation to realisation of significant AI value.

Discover key insights into Aviva's ongoing journey of integrating data and AI more deeply into their business operations. This session will highlight the value created by their technical teams, address current challenges and business priorities, and outline the roadmap for enabling the democratization of data and AI within the organization. Attendees will gain practical knowledge and actionable strategies to apply in their own data and AI initiatives.

Explore Aviva's journey in integrating data and AI, the value created, challenges faced, and strategies for democratizing AI across the org.

Discover how Dataiku's LLM Mesh empowers IT teams to create secure, scalable GenAI apps that align with operations and governance.

Looking to deliver safe, scalable, cost-effective, and future-proof LLM applications aligned with your operations and governance principles? Enter: The LLM Mesh. In this session, we’ll explore how Dataiku equips IT and Data teams to build secure, enterprise-ready GenAI applications, ensuring maximum control while delivering the high performance your business demands.

The data landscape is evolving rapidly, with generative AI poised to revolutionize insight generation and data culture. Join experts from Databricks, MongoDB, Confluent, and Dataiku for an exclusive executive discussion on harnessing gen AI's transformative potential. We'll explore how to break down multicloud data silos, empowering informed decision-making and unlocking your data's full value with gen AI. Discover strategies for integrating gen AI, addressing challenges, and building a future-proof, innovation-driven data culture.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.

Aptiv, Baidu, Cerebras, Dataiku… we could keep going… and going… and going. If you know what this list is composed of (nerd), then you probably have some appreciation for how complex and fast moving the AI landscape is today. It would be impossible for a mere human to stay on top of it all, right? Wrong! Our guest on this episode, Matthew Lynley, does exactly that! In his Substack newsletter, Supervised, he covers all of the breaking news in a way that's accessible even if you aren't an MLE (that's a "machine learning engineer," but you knew that already, right?). We were thrilled he stopped by to chat with Julie, Tim and Val about some of his recent observations and discuss what the implications are for analysts and organizations trying to make sense of it all. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

In this session, we will highlight all parts of the analytics lifecycle using Dataiku + Databricks. Explore, blend, and prepare source data, train a machine learning model and score new data, and visualize and publish results — all using only Dataiku's visual interface. Plus, we will use LLMs for everything from simple data prep to sophisticated development pipelines. Attend and learn how you can truly have it all with Dataiku + Databricks.

Talk by: Amanda Milberg

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Hear from two guests. First, Ben Lorica (Principal, Gradient Flow) on AI and LLMs. Second guest, Conor Jensen (Field CDO, Dataiku), discusses democratizing AI through AutoML, LLMs, and the role of Field CDOs. Third guest, Franco Patano (Lead Product Specialist, Databricks), on modern data stacks and technology community. Hosted by Holly Smith (Sr Resident Solutions Architect, Databricks) and Jimmy Obeyeni (Strategic Account Executive, Databricks)

Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc

Summary

The data ecosystem has been building momentum for several years now. As a venture capital investor Matt Turck has been trying to keep track of the main trends and has compiled his findings into the MAD (ML, AI, and Data) landscape reports each year. In this episode he shares his experiences building those reports and the perspective he has gained from the exercise.

Announcements

Hello and welcome to the Data Engineering Podcast, the show about modern data management Businesses that adapt well to change grow 3 times faster than the industry average. As your business adapts, so should your data. RudderStack Transformations lets you customize your event data in real-time with your own JavaScript or Python code. Join The RudderStack Transformation Challenge today for a chance to win a $1,000 cash prize just by submitting a Transformation to the open-source RudderStack Transformation library. Visit dataengineeringpodcast.com/rudderstack today to learn more Your host is Tobias Macey and today I'm interviewing Matt Turck about his annual report on the Machine Learning, AI, & Data landscape and the insights around data infrastructure that he has gained in the process

Interview

Introduction How did you get involved in the area of data management? Can you describe what the MAD landscape report is and the story behind it?

At a high level, what is your goal in the compilation and maintenance of your landscape document? What are your guidelines for what to include in the landscape?

As the data landscape matures, how have you seen that influence the types of projects/companies that are founded?

What are the product categories that were only viable when capital was plentiful and easy to obtain? What are the product categories that you think will be swallowed by adjacent concerns, and which are likely to consolidate to remain competitive?

The rapid growth and proliferation of data tools helped establish the "Modern Data Stack" as a de-facto architectural paradigm. As we move into this phase of contraction, what are your predictions for how the "Modern Data Stack" will evolve?

Is there a different architectural paradigm that you see as growing to take its place?

How has your presentation and the types of information that you collate in the MAD landscape evolved since you first started it?~~ What are the most interesting, innovative, or unexpected product and positioning approaches that you have seen while tracking data infrastructure as a VC and maintainer of the MAD landscape? What are the most interesting, unexpected, or challenging lessons that you have learned while working on the MAD landscape over the years? What do you have planned for future iterations of the MAD landscape?

Contact Info

Website @mattturck on Twitter MAD Landscape Comments Email

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don't forget to check out our other shows. Podcast.init covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story. To help other people find the show please leave a review on Apple Podcasts and tell your friends and co-workers

Links

MAD Landscape First Mark Capital Bayesian Learning AI Winter Databricks Cloud Native Landscape LUMA Scape Hadoop Ecosystem Modern Data Stack Reverse ETL Generative AI dbt Transform

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