DATABRICKS SERVICES · [SERVICE CATEGORY]
A governance foundation for e-commerce
Create a governed data and metrics layer where business teams, analysts and AI work from the same trusted definitions.
Dateonic implements Unity Catalog, data governance and reusable business metrics on Databricks — so your teams can move faster without losing control.
A governance foundation for modern e-commerce
Unity Catalog expertise
Design access control, ownership, discovery, lineage and governance around your data and AI assets.
Metrics as code
Move business definitions out of individual dashboards and into a shared, governed metrics layer.
Governance without bottlenecks
Give domain teams autonomy while maintaining consistent enterprise policies and controls.
E-commerce Data Governance
One platform for every signal behind your commerce business
Modern e-commerce generates data across storefronts, mobile applications, transactions, marketing, inventory and other operational systems. The challenge is not collecting more data — it is making that data available, consistent and usable across the business.
We bring these sources together in a governed Databricks Lakehouse so data engineers, analysts, data scientists and business applications can work from the same foundation.
What we deliver
What We Implement
GOVERN
Centralize access control, discovery, lineage and governance for your data and AI assets. Unity Catalog provides these capabilities across tables, models and other governed assets.
DEFINE
Define revenue, orders, customers, conversion and other critical metrics once — rather than recreating business logic in individual BI tools.
CONTROL
Implement the right access model for domains, teams and sensitive data while keeping governance manageable at scale.
TRUST
Make data dependencies visible and monitor the quality of the data behind critical decisions.
How we work
A controlled path from fragmented sources to one platform
01 · DISCOVER
Map your e-commerce data
Identify source systems, domains, data owners, dependencies, latency requirements and critical business workloads
02 · DESIGN
Define the target architecture
Design the Databricks architecture, data domains, ingestion patterns, storage layers and operating model.
03 · BUILD
Connect and model the data
Build pipelines and reusable data products for customer, product, order, inventory and behavioral data.
04 · ENABLE
Make the data usable
Connect analytics, BI, ML and AI workloads and enable your teams to build on the new foundation.
Selected Work · Optional
Real-Time E-commerce Data
CASE STUDY
Near real-time inventory visibility and a unified data foundation for analytics and AI.
Dateonic unified POS, e-commerce, ERP and warehouse data on Databricks, enabling real-time inventory insights, consistent reporting and a scalable foundation for forecasting and AI.
CASE STUDY
Customer Intelligence for Premium Retail
A unified customer view with actionable insights for more personalized engagement.
Technical Leadership
Every signal behind your e-commerce business, gathered in one place.
E-commerce businesses generate enormous amounts of data across customers, products, orders, clickstream and inventory. The challenge isn’t collecting more data — it’s connecting it.
At Dateonic, we help e-commerce companies bring these signals together on Databricks and turn them into a trusted foundation for analytics, AI and better decisions.
FAQ
E-commerce data foundation questions, answered
QUESTION 01
Do we need to replace our existing e-commerce systems?
No. The goal is not to replace Shopify, Magento, Salesforce, ERP, PIM or other operational systems.
We connect the data they generate into a shared Databricks foundation, so analytics, AI and downstream applications can work across the full business without replacing the systems that run it.
QUESTION 02
Which data should we bring into the platform first?
Start with the data required for the highest-value decisions. For most e-commerce companies, that means some combination of customer, product, orders, clickstream and inventory data.
We then expand the foundation in waves rather than attempting to centralize every source before delivering any value.
QUESTION 03
Do we need real-time data everywhere?
No. Real-time architecture should follow the decision, not the technology.
Clickstream personalization or inventory availability may require low latency, while financial reporting or historical analysis may be perfectly suited to scheduled processing.
QUESTION 04
What is the difference between this and a traditional data warehouse?
The goal is broader than central reporting. The platform needs to support batch and streaming data, structured and semi-structured sources, analytics, machine learning and AI from the same foundation.
That means you are not building one platform for BI and then separate systems for real-time data and AI.