Lakehouse for Retail: Why Data Lakehouse Is the Future of Data Management

If you run data, digital, or IT in a retail organization today, you are likely dealing with the same issues: 

• Dozens of systems: POS, e-commerce, mobile app, loyalty, ERP, WMS, TMS, marketing platforms, and IoT sensors. 

• Multiple data stores: legacy data warehouses, cloud data lakes, departmental marts, and BI tools. 

• Competing truths: the same metric shows different values in different reports. 

• AI use cases that stall because data is fragmented and not available in real time. 

Traditional data warehouses and data lakes were never designed for omnichannel retail, real-time inventory expectations, and AI-driven experiences. That is exactly why the data lakehouse is becoming the default architecture for modern retail data management. 

At the same time, ACI Infotech brings exclusive and strategic partnerships across Salesforce (Agentforce), Databricks, Snowflake, and leading clouds like AWS, Azure, and Google Cloud giving retailers direct access to modern lakehouse and AI-native capabilities that generic SIs simply don’t have.  

What Is a Data Lakehouse? (In Retail Terms) 

A data lakehouse is a modern data architecture that combines: 

• The low-cost, flexible storage and support for all data types from a data lake. 

• The schema, governance, transactional guarantees, and performance of a data warehouse. 

Technically, a lakehouse: 

• Stores all data (structured, semi-structured, and unstructured) in cloud object storage. 

• Adds a metadata and governance layer with ACID transactions, indexing, caching, and fine-grained access controls. 

• Supports both batch and streaming workloads, plus ML/AI on the same platform. 

In business terms: it is one unified, governed platform for retail data management, real-time analytics, and AI instead of separate lakes, warehouses, and point solutions. 

Why Legacy Retail Data Architectures Are Failing 

Data Warehouses: Governed, But Too Rigid 

Classic data warehouses excel at structured reporting but struggle when: 

• You need to ingest clickstream, IoT, or event data at scale. 

• You want to run data science and ML next to BI on the same datasets. 

• You must adapt quickly to new data sources, schemas, and KPIs. 

Result: more ETL, more data copies, more marts, and slower time to insight. 

Data Lakes: Flexible, But Often “Data Swamps” 

Cloud data lakes solved the storage problem, but introduced new ones: 

• It is easy to land data, hard to enforce quality, lineage, and access control. 

• BI users often find lakes too slow or unreliable for day-to-day reporting. 

• Without a strong metadata layer, lakes degrade into “data swamps” where trust erodes. 

Split Architectures: A “Two-Speed” Retail Business 

Most retailers ended up with: 

• A warehouse for financial and operational reporting. 

• A data lake (and sometimes separate platforms) for data science and AI. 

• Additional streaming systems and specialized marketing, personalization, or fraud tools. 

Every hand-off between these platforms introduces latency, cost, and inconsistency, making it harder to deliver real-time experiences and reliable AI. 

The data lakehouse is designed to eliminate this “split brain”. 

Retail Data Lakehouse Architecture (Practical View) 

Bringing together best practices from Databricks, Oracle, Microsoft, and independent architecture guides, a practical retail data lakehouse can be pictured in layers. 

Ingestion Layer 

• Batch ingestion: POS, ERP, CRM, HR, WMS/TMS, vendor and marketplace feeds. 

• Streaming ingestion: clickstream, app events, RFID and IoT, payment events, supply chain status updates. 

This layer standardizes how data lands regardless of source or cadence. 

Storage & Modeling (Bronze / Silver / Gold) 

• Bronze (Raw)
  As-is ingestion from all systems, with minimal transformation. 
• Silver (Cleansed & Conformed) 
  
  • Standardized keys (product, store, region, customer, order). 
  • Business logic applied for cancellations, returns, promotions, and tax. 

• Gold (Curated Data Products) 
  Tables like Store_SKU_Daily_Inventory, Customer_360, Promotion_Performance, 
  Omnichannel_Margin, SupplyChain_Fulfillment_View. 

Governance & Observability 

• A unified catalog with schemas, tags, and ownership. 

• Row/column-level security, masking, and role-based access control. 

• Lineage, data quality checks, and SLAs to support regulatory compliance and audit-readiness for GDPR, CCPA, PCI, etc. 

Consumption & Activation 

• BI & Reporting: Finance, merchandising, supply chain, HR, marketing, store operations. 

• Advanced Analytics & AI: Forecasting, optimization, personalization, fraud, workforce planning. 

• Operational Systems: APIs that feed e-commerce, mobile apps, CRM and CDP, store tools, contact center, and partner portals. 

Everyone works off the same governed platform, rather than off competing extracts. 

Enterprise Challenges 

When “In Stock” Is a Guess, Not a Promise 
Fragmented POS, OMS, WMS, and e-commerce systems mean inventory is always a step behind reality driving stockouts, cancellations, and broken customer trust. ACI Infotech designs retail data lakehouse blueprints that stream POS, RFID/IoT, and fulfillment data into a single, governed inventory truth that feeds BOPIS, ship-from-store, and replenishment in near real time. 

Customer 360° in Theory, 180° in Practice 
Loyalty, CRM, web analytics, and marketing clouds all hold pieces of the customer, but no one owns the full journey, so personalization and churn strategies underperform. ACI Infotech unifies these sources in a lakehouse-based Customer 360, with consistent IDs, consent flags, and behavioral signals that marketing, digital, and CRM teams can actually activate. 

AI Roadmaps Built on Spreadsheets and Silos 
Data science teams spend more time hunting for clean data than training models, and production AI breaks because training and serving data don’t match. ACI Infotech implements a retail-ready medallion model and feature-store patterns on the lakehouse, so BI and ML share the same curated data products making demand forecasting, price optimization, and recommendations both repeatable and reliable. 

Compliance Drag That Slows Every New Idea 
GDPR, CCPA, and PCI-DSS turn every new data initiative into a governance fire drill when policies are scattered across tools and teams. ACI Infotech centralizes catalog, access control, masking, and lineage on the lakehouse, mapping regulations to concrete data controls so innovation can move faster without putting regulators or your brand on edge. 

ACI Infotech Solutions and Successes 

How ACI Infotech Turns Retail Data Chaos into a Lakehouse Growth Engine 

Instead of pushing a single vendor stack, ACI Infotech acts as a neutral orchestrator for your data lakehouse journey, using the best combination of: 

• Cloud-native storage (AWS, Azure, GCP, OCI) 

• Open table formats (Delta Lake, Apache Iceberg, Apache Hudi) 

• Lakehouse platforms (Databricks, Fabric, Snowflake+Iceberg, others) 

Signature ACI Lakehouse Moves for Retailers 

• Collaborative workshops with business, data, and IT teams to define what “truth” means for sales, margin, inventory, and customer metrics. 

• Output: a logical retail lakehouse blueprint and prioritized data products. 

Industry-Patterned Medallion Models

• Predefined patterns for Product, Location, Inventory, Orders, Customer, Promotions, and Supply Chain domains. 
• Designed to align with the Bronze/Silver/Gold approach recommended in modern lakehouse architectures. 

Real-Time First, Not Real-Time Later 

• Streaming pipelines for POS, e-commerce events, and logistics set up from the start. 

• Avoids “batch-only” lakehouse designs that have to be reworked when real-time use cases arrive. 

BI + AI on Day One 

• Curated Gold tables serve both traditional BI tools and ML workflows. 

• ACI designs semantic models and feature stores around the same lakehouse entities so that analytics and AI evolve together. 

Governance That Doesn’t Kill Speed 

• Catalog, classification, and access policies are built into the design not stapled on after the first use case. 
• ACI uses patterns influenced by the latest lakehouse governance guidance to keep both auditors and analysts happy. 

Turn Your Retail Data into a Lakehouse Advantage with ACI Infotech 

If your teams are still wrestling with conflicting reports, delayed insights, and AI projects stuck in “pilot purgatory,” your data architecture is holding you back. 

ACI’s certified, best-in-class delivery teams turn those platforms into outcomes, combining deep retail data engineering, governance, and analytics expertise to move you from scattered systems to a governed, high-performance lakehouse stack.