Apache Kafka explained: how it works, the hard parts, and why teams pick it over Azure, AWS and Google

Demo 01

dbt + Medallion Architecture

Watch a messy orders.csv get progressively refined by dbt models — with lineage, tests, macros and incremental builds.

Demo 02

SQLMesh vs dbt

A standalone look at where SQLMesh diverges from dbt — stateful incrementals, built-in audits, column-level lineage and virtual environments — and the situations where it's the better choice.

Demo 03

Master Data Management

Watch the same customer — scattered across CRM, ERP, e-commerce and support with conflicting data — get standardized, matched, merged into a Golden Record, and synced back to every system.

Demo 04

Change Data Capture (CDC)

Watch inserts, updates and deletes from an operational orders table get captured and streamed into Bronze → Silver → Gold — with a side-by-side of log-based, query-based and trigger-based capture.

Demo 05

Banking

Submit a sample loan form and watch a workflow fire node by node — webhook, credit check, risk scoring, decision branch, core-banking update and notifications — replacing days of manual hand-offs.

Demo 06

AI Support

Submit a real support message and watch a live workflow use AI to classify it, set priority, route it to the right team and draft a reply — actually running on the backend, not a simulation.

Demo 07

Finance / AP

Step through a workflow that runs a 3-way match of a vendor invoice against the purchase order and goods receipt — pausing for a receiving check and approval before posting to the ERP.

Demo 09

Azure AI Agent Blueprint

An interactive, animated reference architecture: watch a user request flow through the Application, Context and Orchestration layers — gateway, Container Apps, RAG, memory, Azure AI Foundry and model fine-tuning.

Demo 10

AWS AI Agent Stack

An interactive, animated AWS reference architecture: watch a request flow through ALB and API Gateway into ECS Fargate agents, out to Bedrock Knowledge Bases and Kendra for RAG, DynamoDB/ElastiCache memory, and SageMaker + Bedrock for reasoning and fine-tuning.

Demo 11

Google Cloud AI Agent Playbook

An interactive, animated Google Cloud reference architecture: watch a request flow through Cloud Load Balancing and Apigee into Cloud Run / GKE agents, out to Vertex Vector Search and the RAG Engine, Firestore/Memorystore memory, and Vertex AI + Gemini for reasoning and tuning.

Demo 12

Data Platform Architecture

An interactive, animated data-platform architecture: watch data flow from operational sources through CDC, streaming and batch ingestion into a Bronze → Silver → Gold lakehouse on an open table format, then out to BI, SQL, ML and real-time serving.

Demo 13

Enterprise RAG · Security & Governance

Take RAG inside the company: sync SharePoint, Confluence and shared drives, filter retrieval by each user's permissions, redact sensitive data, and measure answer quality before rollout.

Demo 14

Data Quality & Observability

Catch bad data before it reaches your dashboards — automated quality checks, anomaly detection, lineage and alerting across the pipeline.

Demo 15

RAG · Retrieval-Augmented Generation

Watch a question flow through a Retrieval-Augmented Generation pipeline — documents chunked and embedded into a vector database offline, then a query embedded, top-k chunks retrieved, and the LLM answering grounded in real, cited text.

Demo 16

3NF vs. Data Vault

The old Inmon-vs-Kimball argument is back, and this time it's about the silver layer of the medallion architecture. Should silver be modeled in Inmon-style 3NF, or as a Data Vault 2.0 raw vault?