Retail Analytics Lakehouse Logbook

A beginner‑friendly guide to mastering data engineering and analytics using Databricks, Power BI, and Tableau.

Introduction

This guide provides comprehensive, beginner-friendly explanations and detailed, step-by-step instructions for building a scalable analytics platform using the lakehouse approach with Databricks. The context is a typical multinational retail company generating millions of transactional and customer data points daily.

Your goal is to:

Ingest large volumes of daily data.
Clean, transform, and aggregate this data.
Serve it efficiently to visualization tools like Power BI or Tableau.

Typical Retail Data & Scale

Large multinational retail companies commonly handle data such as:

Domain	Example Tables / Files	Daily Volume (Typical Range)
Point‑of‑Sale (POS)	`transactions`, `items_sold`	1M – 10M rows
E‑commerce	`orders`, `clickstream`	10M – 100M rows
Inventory & Products	`sku_details`, `pricing`, `inventory`	10K – 100K rows
Customer Data (CRM)	`customer_profiles`, `loyalty_activities`	100K – 10M rows
Logistics	`shipments`, `warehousing`	10K – 1M rows

Data Ingestion & Storage in Azure

Step-by-Step Process:

Identify Source Data: Source systems such as ERP, CRM, and POS generate CSV, JSON, or Parquet files daily.
Set Up Azure Data Lake Storage (ADLS):
- Sign in to Azure Portal.
- Create an ADLS Gen2 storage account (use default settings initially).
- Create a container named raw-data.
Automated Data Ingestion:
- Use Azure Data Factory (ADF) or Event Hubs for automation.
- In ADF, create pipelines to automatically transfer data files daily from the source systems to ADLS container paths like /raw-data/pos/ or /raw-data/crm/.

Medallion Architecture (Bronze → Silver → Gold)

Layer	Description	Use Case
Bronze	Raw data in original format, converted to Delta format without transformation.	Auditing and data replay.
Silver	Cleansed, validated, and standardized data (e.g., remove nulls, standardize dates, mask personal data).	Reliable and ready for analysis and ML tasks.
Gold	Aggregated and ready-for-business tables (fact and dimension tables).	Directly serve visualization tools and reports.

Delta Lake & Lakehouse Explained Clearly

Delta Lake:

Open-source storage layer.
Provides ACID (Atomic, Consistent, Isolated, Durable) transactions.
Maintains a transaction log (metadata record) that allows tracking of changes, versioning (time travel), and efficient queries.

Lakehouse:

Combines the flexibility and scalability of data lakes with structured management (like data warehouses).
Utilizes Delta Lake + Apache Spark + Databricks infrastructure.

Databricks Detailed Setup & Workflow

Initial Setup:

Sign in to Azure Databricks:
- Access Databricks workspace via Azure Portal.
Creating Databricks Cluster:
- Choose “Clusters” tab, click “Create Cluster”.
- Select “Runtime for Machine Learning”.
- Choose autoscaling (e.g., 2-8 nodes).
Set Up Service Principal (Secure Access):
- Go to Azure Portal → Azure Active Directory → App registrations.
- Click “New Registration”, give it a meaningful name, and register.
- After registration, create a new secret under “Certificates & secrets”.
- Record the Application (client) ID, Tenant ID, and secret.
Add Service Principal to Databricks:
- In Databricks, go to “Settings → Admin → Service principals”, add new, paste Application ID.

Reading & Writing Data with Notebooks:

Create Notebooks in Databricks (use Python/PySpark).

Example to load data:

df = spark.read.format("csv").option("header", "true").load("abfss://raw-data@your_storage_account.dfs.core.windows.net/pos/*.csv")
df.write.format("delta").saveAsTable("bronze.pos_transactions")

Data Transformation (Silver & Gold tables):

Perform cleaning, validation, and aggregation steps using PySpark.
Save results to Silver and Gold Delta tables.

Example aggregation:

df_gold = df.groupBy("date", "store_id").agg({"sales_amount": "sum"})
df_gold.write.format("delta").saveAsTable("gold.daily_sales")

Databricks SQL Warehouse

SQL endpoint enabling BI tools (Power BI, Tableau) to query Delta tables.
Easy to configure via Databricks UI (SQL section).
Optimized for interactive queries and dashboards.

Visualization Layer (Power BI/Tableau)

Connect BI tools directly to Databricks SQL Warehouse.
Create dashboards for:
- Sales trends
- Inventory levels
- Customer segments
- Conversion funnels

Database Types Cheat-Sheet

Relational (OLTP): SQL Server, PostgreSQL
Cloud Warehouse (OLAP): Snowflake, Databricks SQL Warehouse
Vector DB: Pinecone (for AI embedding similarity)
Graph DB: Neo4j (for complex relationship queries)
Key-Value/Document DB: MongoDB, Redis (fast lookups)

Glossary Expanded

Clearly defined technical terms with easy explanations (ACID, ADLS, clickstream, dimension tables, etc.).

Comprehensive Learning Roadmap

Detailed step-by-step learning and practical recommendations:
1. Spark & PySpark basics
2. Delta Lake (MERGE, OPTIMIZE, Time Travel)
3. Automation with Databricks workflows
4. Power BI dashboard basics and advanced visualization

Resources & Next Steps

Databricks official courses and tutorials.
Microsoft Learn for Power BI and Azure integration.