Transforming Retail Intelligence with Data Engineering

Data engineering helped a retailer unify data, improve analytics, and optimize decisions.

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Client Overview

A leading retailer used data engineering to unify customer, sales, inventory, and supplier data for smarter operations.

Industry

Retail Sector

Duration:

[9 Months]

Services Provided:

Data engineering, analytics, and cloud data services

The Challenge

Customer, sales, inventory, and supplier data were scattered across POS, ERP, CRM, and eCommerce systems. Delayed reporting, inconsistent data quality, manual dashboards, and weak forecasting limited decision-making across stores and digital channels.

The Solution

  • Alchemy built a modern data engineering layer to unify retail data from POS, ERP, CRM, eCommerce, loyalty, and supplier systems.
  • Automated ETL pipelines cleaned, validated, and transformed data for analytics.
  • Cloud data warehouses enabled faster reporting, while dashboards improved visibility into sales, inventory, customer behavior, and demand trends.
  • Data governance, access controls, and quality checks ensured reliable insights across business teams.
Key Performance Growth
Key performance growth
Reporting
0%
Reduction in reporting delays through unified data pipelines and automation
📦
Inventory visibility — real-time stock visibility across stores and warehouses eliminating blind spots
📈
Demand forecasting — strengthened accuracy through integrated historical and real-time data signals
⚡
Faster decisions — business teams empowered with timely, reliable data for strategic action
🎯
Customer segmentation — improved targeting precision enabling personalized offers and campaigns
🗂️ Efficiency
Manual Effort Reduced
Automated reporting pipelines eliminating repetitive manual data preparation work
🏛️ Foundation
Trusted Data Layer
Single source of truth enabling analytics, personalization and AI use cases
🤖 AI Ready
Future AI Use Cases
Clean, governed data foundation ready for advanced AI and ML model deployment
🛍️ Personalization
Customer Intelligence
Enriched customer profiles powering hyper-personalized retail experiences at scale

Key Features

P1 — Unified Data Ingestion
Unified Data Ingestion
POS · ERP · CRM · eCommerce · Loyalty
Unified Data Ingestion
Retail Data Engineering
Unified Data
Ingestion Layer
POS · ERP · CRM · eCommerce · Loyalty · Supplier
Kafka · Airflow · dbt · Snowflake · Synapse
The Challenge: Fragmented Data Silos
6 disconnected sources · No single truth · Reporting delays · Data corruption
⚠ NO LINK
POS Systems ⚠
cust_id: NULL
date: 20241-??
3 errors · unresolved
ERP System ⚠
12,450 duplicates
Encoding: mismatch
Unfit for joins
CRM ⚠
email: 34% missing
seg: NULL, "??"
34% incomplete
eCommerce ⚠
stock: -1, null
sku: format clash
Cannot join reliably
Loyalty ⚠
Weekly batch
No real-time
Always stale data
Supplier ⚠
loc_code: deprecated
Manual CSV upload
No API, no trust
✕ ✕ ✕ ✕ ✕ ✕
60%
Reporting Delays
12K
Duplicate Records
6
Siloed Sources
0
Unified View
POS SystemsPoint of Sale
ERP SystemEnterprise Resource
CRM PlatformCustomer Relations
eCommerceOnline Channels
Loyalty ProgramCustomer Rewards
Supplier DataVendor Systems
Unified
Data
Lake
ETL Pipeline
Airflow · dbt · Kafka
WAREHOUSE
Snowflake / Redshift
Cloud data warehouse
PROCESS
Databricks / Spark
Distributed processing
GOVERN
Data Governance
Quality & access controls
INSIGHT
Power BI / Tableau
Unified analytics
Source Systems
POS, ERP, CRM, eCommerce, Loyalty & Supplier feeds unified
Integration Layer
Airflow, dbt, Kafka orchestrate automated ETL workflows
Cloud Warehouses
Snowflake, Azure Synapse, Redshift power analytics at scale
Source Channels
Ingest Flow
Unified Hub
Output Flow
Warehouses
Cloud Warehouse Layer
LIVE
48K
SKUs Tracked
2.4M
Profiles
320
Stores
640
Suppliers
Source ingestion coverage
updating
POS
96%
ERP
88%
CRM
82%
eCom
95%
Data throughput (last 6h)
Warehouse Stack
Snowflake
Petabyte scale · Auto-cluster
Azure Synapse
Analytics + lakehouse
AWS Redshift
Real-time ingestion
PostgreSQL · dbt · Databricks
Impact Delivered
One Source of Truth.
Zero Silos.
60%
Faster Reporting
6
Sources Unified
Real-time
Data Sync
Kafka-powered ingestion layer connects all retail data sources into a single Snowflake warehouse — eliminating silos, enabling real-time trusted analytics.
P2 — Automated ETL & Data Quality
ETL & Data Quality
Extract · Transform · Validate
ETL & Data Quality
Retail Data Engineering
Automated ETL &
Data Quality Engine
Extract · Transform · Validate · Load
dbt · Apache Spark · Airflow · Talend · Informatica
SALES DATA
RAW
INVENTORY
RAW
CUSTOMER
RAW
ORDERS
RAW
SUPPLIER
RAW
LOYALTY
RAW
ETL Engine
dbt · Airflow · Talend
Type
Field
Status
Sales
Revenue_Q4
VALID
Inventory
StockLevel
VALID
Customer
Cust_ID
CLEAN
Orders
Order_Date
VALID
Supplier
SKU_Code
VALID
Loyalty
Points_Bal
VALID
Sales
Discount_%
CLEAN
78% records validated · 3.2M rows processed
Raw Data Sources
Sales, Inventory, Customer, Orders, Supplier & Loyalty raw feeds
ETL Pipeline
dbt, Airflow, Talend, Informatica automate transformation workflows
Data Quality
Validation rules, cleansing, deduplication & governance checks
Raw Documents
Ingest Flow
ETL Engine
Output Flow
Validated
Raw Data Reality: Messy, Incomplete, Untrusted
This is what your data looks like BEFORE the ETL pipeline processes it
POS EXPORT.csv
cust_id: NULL ⚠
date: "20241-03-?" ⚠
amt: -999.00 ⚠
store: "branch???"
3 CRITICAL ERRORS 12K rows
ERP DUMP.xml
sku: "N/A", "" ⚠
12,450 duplicates
encoding: ISO-8859 ⚠
missing: 22% fields
5 ISSUES FOUND 48K rows
CRM RECORDS.json
email: 34% missing ⚠
phone: +44 / 0044 mixed
seg: NULL, "??" ⚠
No schema standards
4 CRITICAL ERRORS 2.4M rows
42%
Quality Score
12K
Duplicates
34%
Missing Fields
0
Governance Rules
ETL Pipeline — How Data Flows
Follow the data: Raw sources enter left → processed through 4 stages → clean output delivered right
SOURCES
① EXTRACT
② TRANSFORM
③ VALIDATE
④ LOAD
OUTPUT
Sales CSV ERP XML CRM JSON Inventory Supplier Loyalty EXTRACT Apache Spark Parse · Decode TRANSFORM dbt Models Clean · Enrich VALIDATE Schema Rules Check · Dedup LOAD Airflow + Talend Write · Index Snowflake ✓ Synapse ✓ Redshift ✓ ORCHESTRATED BY AIRFLOW · MONITORED BY dbt · GOVERNED BY INFORMATICA ↑ Every stage is automated, logged, and retried on failure
Extract
Spark · Kafka
Transform
dbt · Databricks
Validate
Great Expectations
Load
Talend · Informatica
Orchestrate
Airflow · ADF
Data Quality Monitor
LIVE
97%
Quality Score
was 42% →
12K
Dupes Removed
auto-deduplicated
100%
Automated
zero manual work
6 TB
Daily Processed
all 6 sources
Source quality (post-ETL)
updating
POS
98%
ERP
96%
CRM
99%
Invnt
94%
Validation log
✓ Sales.Revenue_Q4 → VALID [98.4%]
✓ Inventory.StockLevel → VALID [96.1%]
~ Customer.Cust_ID → CLEANED [12K fixed]
✓ Orders.Order_Date → VALID [99.2%]
✓ Supplier.SKU_Code → VALID [94.8%]
ETL Stack
Apache Spark
dbt Core
Airflow
Talend · Informatica
Quality Delivered
42% → 97%
Data Quality.
12K
Dupes Removed
100%
Automated
6 TB
Daily Processed
Spark + dbt pipelines run daily on Airflow — transforming raw retail data into 97% quality-validated datasets with full lineage tracking and zero manual effort.
P3 — Analytics & Dashboard Visibility
Analytics & Dashboards
Sales · Inventory · Segments
Analytics & Dashboard Visibility
Retail Intelligence
Analytics &
Dashboard Visibility
Power BI · Tableau · Looker — Real-time Insight
Sales · Inventory · Customer · Demand · Digital · Loyalty
The Problem: Flying Blind
No unified view · Scattered data · Decisions made on stale, incomplete information
📊 SALES ⚠
???
NO SIGNAL
Last report: 3 days ago
Manual Excel · Weekly batch
📦 INVENTORY ⚠
???
NO DATA
320 stores — no real-time
Blind to stockouts
👥 CUSTOMER ⚠
???
NOT FOUND
2.4M profiles — disconnected
No single customer view
📈 DEMAND ⚠
???
UNAVAILABLE
Forecast: spreadsheet guess
Reactive, never predictive
💻 DIGITAL ⚠
???
DISCONNECTED
eCommerce silos — no link
Sessions invisible to stores
⭐ LOYALTY ⚠
???
BATCH ONLY
Segments updated weekly
Always out of date
60%
Reporting Delays
12
Excel Sheets
0
Real-time View
$2.1M
Overstock / Year
Power BI Tableau Looker Reports Alerts Insights
SalesPOS · eCom
InventoryWarehouse
CustomerBehavior
DemandForecasts
LoyaltySegments
DigitalSessions
DATA PROCESSOR
Profile Aggregation
Sales
92%
Invnt
85%
CRM
78%
Dmnd
70%
Live Activity
Insight Scoring
Accuracy96.2
Coverage88.5
Latency4.2s
Uptime99.9
Sales
↑ 18%
vs last quarter
Inventory
320
stores in view
Customer
2.4M
profiles active
Demand
↑ 92%
forecast acc.
Loyalty
6 Segs
auto-segmented
Digital
60%
faster reports
Visualization
Power BI · Tableau · Looker
Processing
Databricks · Spark · Azure Synapse
Outcome
Real-time visibility · Better decisions
Sales & Inventory
Customer & Demand
Loyalty & Digital
Data Pipeline
Real-time Analytics Dashboard
LIVE
↑18%
Sales Growth
98.4%
Inv. Accuracy
2.4M
Active Profiles
92%
Forecast Acc.
Monthly sales trend
live
Jan Mar May Jul +8% +12% +15% +16% ↑18% YTD ↑18%
Channel mix
Store
74%
Online
88%
App
62%
BI Stack
Power BI
Tableau
Looker
Databricks · Synapse
Analytics Data Pipeline — How Data Flows
Warehouse → Processing → Visualization → Business Decisions — follow the data left to right
WAREHOUSE
① INGEST
② PROCESS
③ VISUALISE
DECISIONS
Snowflake Azure Synapse AWS Redshift PostgreSQL INGEST Databricks Stream · Batch PROCESS Spark · dbt Transform · Model VISUALISE Power BI · Tableau Looker · Reports Sales KPIs ✓ Inventory View ✓ Customer 360 ✓ Demand Signals ✓ ORCHESTRATED BY AIRFLOW · POWERED BY DATABRICKS ↑ Every stage is automated, governed, and monitored in real time
Warehouse
Snowflake · Synapse
Ingest
Databricks · Kafka
Process
Spark · dbt · Azure ML
Visualise
Power BI · Tableau · Looker
Govern
Airflow · Unity Catalog
Visibility Delivered
From Blind Spots
to Real-time Insight.
60%
Faster Reports
320
Stores Visible
3 BI
Tools Unified
Power BI, Tableau and Looker now draw from a single Databricks layer — enabling every team to answer their own questions in real time, across all 320 stores and 2.4M customer profiles.
P4 — Demand Forecasting & AI Foundation
Demand Forecasting
Predict · Plan · Automate
Demand Forecasting & AI Foundation
Retail AI Intelligence
Demand Forecasting
& AI Foundation
Historical · Seasonal · Predictive · Automated
Databricks · Spark MLlib · Time-Series Models
Without AI: Forecasting Means Guessing, And Guessing Costs Money
① You Predict
Based on last year's data
+ gut feel + spreadsheet
10,000
units ordered
Excel model, weekly update
Forecast accuracy: 62%
→
Reality
arrives
② Reality Hits
Actual customer demand
was completely different
13,800
units demanded
38% ERROR GAP — Every. Single. Time.
→
Costs
pile up
③ You Pay The Price
Two ways to lose money:
$2.1M
Overstock
per year
84
Stockouts
per month
Unsold stock + lost sales
= reactive, never predictive
IMPACT METER — Current state without AI 62% accuracy is below industry threshold
Forecast Accuracy62%
Wasted Budget$2.1M / yr
Stockout Rate84 / month
Historical Sales24-month rolling
Inventory LevelsSKU · Location
Customer PatternsBehavior signals
Seasonal IndexHoliday · Promo
Supplier FeedsLead times · Cost
Market TrendsExternal signals
High +25% +12% Base Mid Low AI Demand Volume Index — 12-Month View NOW +8% +14% +20% +28% +35% ← HISTORICAL DATA (Jan–Jul) AI FORECAST (Aug–Dec) → Jan Feb Mar May Jul Sep Oct Nov Historical Demand AI Forecast Confidence Band
URGENT
Restock Alert
18 SKUs below threshold
DEMAND
Peak Forecast
+22% surge in Q4
ACTIVE
Segment Model
6 clusters auto-updated
NEW
AI Foundation
Ready for personalization
AUTO
Price Optimize
Dynamic rules deployed
ML Stack
Databricks · Spark MLlib · PostgreSQL
Forecasting
Time-series models · Seasonal tuning
Outcome
Stronger demand accuracy · Reduced overstock
Historical Data
AI Forecast Line
Confidence Band
Input Signals
AI Prediction — Live Command Center
Demand signals in → AI model → Automated actions out · Running 24/7
92% accuracy
SIGNALS INCOMING
48,000 SKUs · 320 stores
Apparel / SKU-482+18%
Seasonal / SKU-231+45%
Electr. / SKU-118URGENT
Grocery / SKU-774+8%
Sports / SKU-390stable
Apparel / SKU-503+12%
Seasonal / SKU-678+38%
Beauty / SKU-092+6%
Electr. / SKU-205+22%
Grocery / SKU-841stable
Apparel / SKU-482+18%
Seasonal / SKU-231+45%
Electr. / SKU-118URGENT
Grocery / SKU-774+8%
Sports / SKU-390stable
Apparel / SKU-503+12%
Seasonal / SKU-678+38%
Beauty / SKU-092+6%
Electr. / SKU-205+22%
Grocery / SKU-841stable
0 SKUs processed
AI MODEL PROCESSING
INPUT PROCESS OUTPUT S I M AI ↑ ✓ Sales Invnt Mkt Auto-PO Alert → Analyse → Predict ⚡ Processing live signals... Spark MLlib · Databricks · 92% confidence
0
Predictions/day
0
Auto POs sent
0
Urgent alerts
ACTIONS OUT
Auto-triggered · zero manual
Apparel
↑ +18% · Auto-order
94% confidence
Seasonal
↑ +45% · Peak prep 🔥
96% confidence
Electronics
↑ +22% · URGENT ⚠
88% confidence
Grocery
↑ +8% · Monitor
91% confidence
Loyalty
→ Stable · OK ✓
87% confidence
0 actions today
48,000
SKUs Analysed
340
Auto POs Sent
Daily
Model Refresh
18
Urgent Alerts
AI Pipeline — How Predictions Become Decisions
Raw signals enter left → AI processes through 4 stages → automated actions exit right
SIGNALS
① COLLECT
② MODEL
③ PREDICT
④ ACT
OUTPUTS
Sales History Inventory Data Seasonal Index Supplier Feeds Market Trends COLLECTDatabricksFeature Store MODELSpark MLlibTime-Series PREDICTForecast Engine+Confidence ACTAirflowAuto-trigger Auto-reorder ✓ Stock buffer ✓ Pricing rules ✓ Restock alert ✓ ORCHESTRATED BY AIRFLOW · POWERED BY DATABRICKS · 92% ACCURACY ↑ Every prediction triggers automatic business actions — zero manual intervention required
Collect
Databricks · Kafka
Model
Spark MLlib · dbt
Predict
Time-Series · Prophet
Act
Airflow · REST APIs
Govern
MLflow · Unity Catalog
AI Impact Delivered
62% → 92%
Forecast Accuracy.
↓68%
Stockouts
$1.4M
Saved / Year
Daily
AI Reorders
Spark MLlib models run daily on Databricks — predicting SKU-level demand, automatically triggering reorders, pricing updates and stock buffers across all 320 locations.
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