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Data Lake Platform: Visual Implementation Guide

Purpose: Visual guide supporting tasks and implementation plan
Audience: Technical team, implementation reference
Format: Visual-first with technical details

Executive Overview​

Platform Architecture​

Architecture Flow: Data flows directly into the raw Bronze layer (immutable audit trail), then through automated ETL processing with quality gates, is organized into trusted layers, and becomes analytics-ready—all with complete traceability and automated operations.

1. Data Journey: From Source to Insights​

Complete Data Lifecycle​

Implementation Note: Every piece of data follows a clear path with quality gates, automatic error handling, and complete auditability.

2. Architecture: Medallion Design​

Three-Layer Architecture​

Architecture Benefits:

  • Bronze: Complete audit trail, immutable source of truth
  • Silver: Quality-assured data, ready for analytics
  • Gold: Business-optimized views, fast queries
  • Quarantine: Isolated errors, prevents contamination

3. Data Quality: Automated Validation​

Validation Process Flow​

Validation Rules:

  • Required Fields: TransactionID, CustomerID, TransactionAmount, Currency, TransactionTimestamp
  • Currency: ISO-4217 allowlist validation
  • Timestamp: ISO-8601 format parsing
  • Amount: Numeric validation, negative amounts allowed (withdrawals/refunds)

4. Compliance & Auditability​

Complete Audit Trail​

Audit Capabilities:

  • Every record traceable to source file
  • Every query traceable to processing run
  • Complete lineage from source to report
  • Reproducible results for any historical period
  • Immutable raw layer for compliance

Compliance Features​

5. Operational Excellence​

Automated Operations​

Automation Benefits:

  • Zero-downtime deployments
  • Automatic rollback on failures
  • Continuous monitoring and alerting
  • Cost optimization through usage tracking

Monitoring & Observability​

What We Monitor:

  • Data Quality: Validation rates, quarantine counts, completeness
  • Performance: Query times, processing duration, throughput
  • Cost: Storage costs, compute costs, data transfer
  • Errors: Failure rates, retry counts, alert frequency
  • Usage: Query patterns, data access, user activity

6. Scalability & Performance​

Scalability Characteristics​

Scalability Features:

  • Partitioned storage: Enables efficient querying at any scale
  • Serverless compute: Automatically scales with demand
  • Optimized data format: Parquet provides compression and columnar storage
  • No redesign needed: Architecture supports 1000x growth

Performance at Scale​

Data VolumeProcessing TimeQuery TimeCost per Million Rows
1K rows< 1 second< 1 second€0.50
10K rows< 5 seconds< 2 seconds€0.80
100K rows< 30 seconds< 5 seconds€1.20
1M rows< 3 minutes< 10 seconds€2.10
10M rows< 15 minutes< 30 seconds€3.50
100M rows< 2 hours< 2 minutes€5.00

Visual Implementation Guide - January 2026

© 2026 Stephen Adei•CC BY 4.0