Autonomous Agentic Event-Driven Systems Architecture

Autonomous / agentic event-driven systems are a class of AI-native architectures where software agents continuously sense events, reason over shared state, take actions, and learn from outcomes—all in real time and without human-in-the-loop orchestration. At an architectural level, these systems combine event streaming, stateful processing, and agentic decision layers to form closed-loop AI systems capable of operating independently at scale.

Enterprise Knowledge Management with RAG for Digital-Native Companies

Enterprise knowledge management RAG (Retrieval-Augmented Generation) is a production-grade AI architecture designed to connect Large Language Models (LLMs) securely to a continuous, real-time flow of proprietary corporate data. Unlike basic RAG implementations that rely on static document uploads and batch-processed vector databases, an enterprise RAG architecture utilizes event streaming to ingest document updates, regenerate embeddings, and synchronize context in real time.

RAG and GenAI for Regulated and Public Sector Architectures

As a cloud engineer, I’ve seen organizations rush to implement Generative AI, only to hit a brick wall when the Chief Information Security Officer (CISO) asks about data residency or PII leakage. In the public sector and regulated industries like healthcare or finance, moving fast and breaking things isn't an option.

How to Scale Paid Media Across 5 Channels Without Losing Visibility (Google, Meta, LinkedIn, TikTok)

Agencies hit the same wall every time they try to grow: who is going to actually run the campaigns, and how do you keep visibility across every client and every channel when you do? Ashish Chaturvedi, data analyst of Atidiv, walks through how Atidiv and Databox solve both sides of the problem. Atidiv handles campaign execution across Google Ads, Meta, LinkedIn, TikTok, and email. Databox gives you the visibility layer: one interactive view where you can see spend, revenue, and return across every channel without chasing updates in Slack, email, or spreadsheets.

AlloyDB Lakehouse Federation: Unified access to BigQuery and Google Cloud Lakehouse

Join Paul Ramsey, Product Manager at Google, for a demonstration of AlloyDB’s new Lakehouse Federation capability. Using a fictional financial services firm, Cymbal Investments, we show how analysts can research S&P 500 trends by combining real-time vector search with data in BigQuery and Google Cloud Lakehouse. In this video, you will see: Learn how AlloyDB enables cloud and AI transformation for your data platform.

Zero-Copy Mirroring in Qlik Open Lakehouse

There’s a moment in every data team’s journey when a bill arrives with unexpected costs. It might be an egress charge you didn’t see coming, a query cost that spiralled when usage spiked, or the quiet realization that migrating your data to a different platform would take months of work and a significant budget. If you’ve been there, you know what vendor lock-in really costs, and it’s not just dollars.

Think Big: Inside the Hakkoda/IBM Snowflake Partnership

Ryan Tucker, CRO & Co-Founder of Hakodaa (now an IBM company), shares how their True Blue Snowflake partnership since 2021 drives data transformation and AI value with vertical expertise. He highlights customer wins including Cortex AI-powered sentiment analysis for a UK wealth manager and Snowflake Intelligence for retail executive reporting, and discusses how the IBM acquisition amplifies their Snowflake-specialized DNA with global reach.