System
Architecture

An enterprise-grade Data Platform designed for scalability, reliability, and low-latency insights. Integrating Batch and Streaming paradigms into a unified Lakehouse.

VERSIONv3.0.1

STACKAZURE / DATABRICKS

STATUS● ONLINE

01 — Batch Pipeline

Medallion Architecture

I implement a strict Multi-hop architecture where data quality improves as it flows through the system. This isn't just folder organization; it's a rigorous Quality Guarantee contract.

Bronze (Raw)

As-is ingestion (JSON/Parquet)
Append-only history
Replayability source of truth
Zero validation (capture everything)

Silver (Refined)

Deduplication & Merge logic
Strong Schema Enforcement included
Null checks & Type casting
3rd Normal Form (3NF) modeling

Gold (Business)

Star Schema / Dimensional Models
Pre-computed Aggregates
Row-Level Security (RLS) applied
Ready for PowerBI / Tableau

System Architecture

BATCH PROCESSING • MEDALLION ARCHITECTURE

02 — Real-Time

Streaming Ingestion

For latency-sensitive workloads, I utilize Spark Structured Streaming and Delta Live Tables (DLT) to process events from Kafka/Event Hubs in near real-time.

The Consistency Trade-off

Micro-Batch (Streaming)

Prioritizes freshness (<1s latency). Ideal for fraud detection and operational monitoring. Accepts eventual consistency in complex joins in exchange for speed.

Batch Processing

Prioritizes completeness & accuracy. Re-processes entire partitions to ensure perfect join consistency for financial reporting and regulatory compliance.

Streaming Pipeline

REAL-TIME • LOW LATENCY

03 — System Specifications

Infrastructure

IaCTerraform / Bicep
ComputeAKS / Databricks
RegistryAzure SCR
NetworkPrivate VNET Injection

CI / CD

PipelineGitHub Actions
Unit TestsPyTest / Nutter
QualitySonarQube
DeployBlue/Green

Observability

LogsLog Analytics
MetricsPrometheus / Grafana
TracingOpenTelemetry
SLA99.99% Uptime

Throughput

10 TB/DAY

Latency

<500MS

Sources

45+

Users

2.5KDAU

04 — Methodology

I don't build pipelines.
I build engines that generate them.

Traditional data engineering relies on brittle, hand-coded pipelines for every table. My approach handles the entire Data Lifecycle programmatically. By abstracting logic into a Metadata-Driven Framework, I ingest, clean, and model thousands of tables with a single, unified codebase.

01. Schema Evolution Detection
02. Dynamic Quality Expectations
03. Auto-Healing Ingestion

● RUNTIME AGNOSTIC

Azure Databricks

The Standard

The reference implementation running on massive Spark clusters. Optimized for heavy batch processing and ML workloads via Unity Catalog.

Microsoft Fabric

The SaaS Evolution

Zero-copy interaction with OneLake. The exact same logical architecture deployed as Fabric Items (Notebooks/Pipelines) without infrastructure management.

Apache Airflow

The Orchestrator

Can run completely on Open Source. I decouple the Control Plane (Airflow) from the Compute Plane (Spark/Snowflake/DuckDB) for total portability.

05 — Governance

Unified Data Estate

Governance is not an afterthought; it is the foundation. I utilize Unity Catalog to implement a centralized permission model across all workspaces. This enables Attribute-Based Access Control (ABAC) and provides granular lineage visualisation from source ingestion down to the specific BI dashboard widget.

Access Control Lists (ACLs)
Dynamic Masking
Audit Logging
Discovery / Search

CATALOG_EXPLORER

GRANTED

READ_VOLUME

DENIED

06 — Performance Internals

Photon Engine

Native vectorized execution engine written in C++. It bypasses the JVM for critical operations, delivering up to 12x performance on large aggregations and joins compared to standard Spark.

Z-Ordering

Colocating related information in the same set of files. I enforce Z-Order indexing on high-cardinality join keys (e.g. CustomerID) to enable massive Data Skipping during read queries.

The 1GB Checkpoint

Tuning maxBytesPerTrigger to ensure structured streaming micro-batches don't exceed driver memory. Optimized checkpointing strategies to prevent metadata bloat on S3/ADLS.

dataset: "orders_global"
owner: "checkout_team@company.com"
sla: "4 hours"
quality:
  - check: "row_count > 0"
  - check: "null_percentage(user_id) < 0.01"
schema:
  - name: "order_id"
    type: "string"
    primary_key: true
  - name: "amount"
    type: "decimal(10,2)"
    pii: false
    constraints: ["min_value(0)"]

07 — Data Contracts

API-First Data Engineering

I treat data producers and consumers as microservices. Before a pipeline is deployed, a Data Contract (YAML) must be defined and versioned in Git.

This contract is enforced in the CI/CD pipeline. If a schema change violates the contract (e.g., changing a column type without versioning), the PR is automatically blocked. This prevents "silent failures" in downstream dashboards.

08 — FinOps & Cost Strategy

Performance at the Right Price

70%

Spot Instance Usage

Auto

Scaling Clusters

TTL

Lifecycle Policy