Blog Index / Technical Blog

Platform Engineering, Data Engineering, and Generative AI

Technical examples for data platforms, data pipelines, and large language model applications.

2025

Building a Retail Analytics Conversational UX with Databricks Genie

Databricks Genie transforms the Lakehouse into a conversational analytics layer—allowing retail leaders to ask natural-language questions about key KPIs like OTIF or stockout exposure and get consistent, real-time answers grounded in governed data from Unity Catalog.

Next-Generation Retail Logistics Analytics with Databricks AI / BI Dashboards

By surfacing key performance indicators like On-Time In-Full (OTIF) and Stockout Exposure through dynamic visualizations and natural-language insights, the dashboard transforms complex warehouse, carrier, and inventory data into actionable intelligence for the COO, logistics leads, and merchandising teams alike.

Building a Retail Semantic Layer with Unity Catalog Metric Views

This article explains how Databricks Unity Catalog Metric Views create a governed semantic layer that transforms complex retail data into consistent, business-ready insights.

Driving Retail Information Architecture with the Databricks Medallion Architecture

Information architecture within the Information Layer is what transforms a company’s raw data into a single, trusted system of record that fuels knowledge work across every business function. By organizing and governing how data is modeled, related, and exposed through APIs, the Information Layer ensures that every team—from operations to finance to marketing—works from the same version of the truth.

Bordereaux Data Integration into Databricks Delta Tables with Tabsdata

In this article we create enriched property insurance Databricks Delta Tables with Tabsdata.

Building a Retail Product Delta Table with Databricks Unity Catalog

In this article we get you going quickly building Delta Tables with CTAS statements and Unity Catalog on Databricks.

Understanding the Difference Between Unity Catalog and the Hive Metastore

In this article we give a breakdown of the key differences between the legacy hive metastore and the modern Databricks Unity Catalog.

Analyzing Claims Data With the Cube Semantic Layer, Databricks, and Google GSheets

Connecting your Google GSheet to a Cube.dev claims data model backed by Databricks Delta Tables for faster and more consistent information analysis in property insurance.

Building a Standard Claims Data Model With the Cube Semantic Layer and Databricks

Creating standard claims data models for claims knowledge work in property insurance companies.

Building a Property Insurance Claims Data Lakehouse with Airflow and Databricks

In this article we build a data lakehouse for property insurance claims management. This allows us to build out a complete claims analysis platform complete with standardized data modeling.

Capturing Email Attachments with Apache Airflow

Automate claims TPA bordereaux email attachment capture with Apache Airflow.

Selecting a Data Storage Strategy for Your Data Platform

The choice of data storage architecture directly impacts your organization's ability to efficiently query, manage, and scale large datasets on your data platform. This article takes you through how to make an informed decision about selecting a data storage strategy for your data platform.