Building a Modern Data Analytics Platform: A Developer's Journey

The Data Scaling Crisis: A Modern E-commerce Story

The Wake-up Call

It's Monday morning, and your phone won't stop buzzing. Sarah, the Head of Analytics at GlobalMart, has been trying to reach you since 6 AM. The company's Black Friday sale was a massive success – too successful, perhaps. The analytics dashboard is down, data scientists can't access their notebooks, and the CEO needs immediate insights into the weekend's performance.

Learning Through a Demo First

While GlobalMart deals with terabytes of data daily, we'll build a proof-of-concept (POC) with a representative sample dataset. This allows us to focus on architecture and functionality before scaling to production volumes.

Demo Dataset Overview

For our demonstration, we'll use a manageable subset of data that mirrors the production patterns:

Demo Data Volumes

Data Type	Production Volume	Demo Volume	Sample Contents
Clickstream	2TB/day	100MB	1 week of user sessions, page views
Mobile Events	500GB/day	50MB	3 days of app interactions
Transactions	500GB/day	20MB	1000 orders with line items
Inventory	100GB/day	10MB	Stock levels for 100 products

Architecture Diagram

Implementation Phases

1️⃣ Demo Data Sources

• Sample Data Generation: Scripts to create realistic test data
• Data Structure: Matching production schemas with reduced volume
• Simulation: Mock real-time data streams at a smaller scale

2️⃣ Processing Layer

Development environment specifications:

• Local Spark Cluster: 2-3 worker nodes for demonstration
• Stream Processing: 1-minute intervals (instead of real-time)
• Batch Processing: 15-minute jobs (instead of hourly)

3️⃣ Storage Layer

Demo environment setup:

• MinIO: Local S3-compatible storage
• Iceberg Tables: Same schema as production, smaller scale
• Data Retention: 1 week of history (vs months in production)

4️⃣ Analytics Layer

Simplified analytics environment:

• JupyterHub: Single-instance setup
• Superset: Core dashboards with essential KPIs

5️⃣ Demo Users

Test environment roles:

• You: Data scientist/developer role
• Colleague: Business analyst perspective
• Stakeholder: Executive dashboard user

Scaling to Production

Production Migration Path

This demo environment is designed to scale. Here's how components would grow:

• Data Volume: From MB to TB by adjusting ingestion
• Processing: From local to cloud-based Spark cluster
• Storage: From MinIO to AWS S3/Azure Blob/GCS
• Analytics: From single instance to distributed deployment

Implementation Journey

Now let's build each component of our demo environment...

[Implementation details follow...]

Chapter 1: Setting Up the Development Environment

Setup Commands

# Install kubectl and helm
helm repo add jupyterhub https://jupyterhub.github.io/helm-chart/
helm install jhub jupyterhub/jupyterhub --version=3.0.0

Configuration

# config.yaml
jupyterhub:
  hub:
    config:
      Authenticator:
        admin_users:
          - admin
      GitHubOAuthenticator:
        client_id: your-client-id
        client_secret: your-client-secret

Important

Make sure to replace your-client-id and your-client-secret with your actual GitHub OAuth credentials.

Chapter 2: Data Lake Foundation

Iceberg Setup

-- Create Iceberg catalog
CREATE CATALOG sales_catalog
WITH (
  'type'='iceberg',
  'warehouse'='s3://globalmart-datalake/'
);

Schema Definition

CREATE TABLE sales_catalog.raw.clickstream (
  user_id STRING,
  timestamp TIMESTAMP,
  page_id STRING,
  action STRING
) PARTITIONED BY (days(timestamp));

Production Readiness Checklist

• High Availability Configuration
• Backup and Recovery Procedures
• Monitoring and Alerting
• Security Controls
• Performance Optimization
• Documentation

Resources and References

• JupyterHub Documentation
• Apache Iceberg Documentation
• Apache Spark Documentation
• Apache Superset Documentation