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Kato TechStack Standards — Data Architecture, Engineering & AI

Kato (Quan Ngo)

August 2, 2025

5 min read

“In the era of AI, the new architecture isn’t just about pipelines — it’s about creating systems that learn.”

As a Data Architect and Senior Data Engineer, I focus on designing scalable data ecosystems where AI and analytics can thrive together. This document defines my Tech Stack Standards — a blueprint for building data-driven platforms that support both analytical workloads and machine learning systems from day one.

🧱 Core Architecture Philosophy

Data-Centric Foundation Build once, use everywhere — data should be usable across analytics, ML, and real-time decisioning.
Schema as Contract Every dataset has a defined lifecycle — from ERD to metadata catalog to production schema registry.
Observability + Explainability Design for traceable data flow and model lineage — critical for AI governance and debugging.
Composable Systems Choose modular tools across ingestion, processing, storage, and inference. Interoperability > monolith.

☁️ Cloud & Infrastructure

Core Stack

Compute & Storage: AWS (S3, Redshift, EMR), GCP (BigQuery, Vertex AI), Azure (Data Factory, Synapse)
Containerization: Kubernetes, Docker
IaC: Terraform, Jenkins, CI/CD automation
Observability: Grafana, Prometheus, ELK stack
Security: IAM, Apache Ranger, Cloudflare edge protection

🧮 Data Warehouse & Lakehouse Standards

Warehousing Layers

Staging → Core → Mart modeled using dbt
Batch & Realtime pipelines unified via event streaming (Kafka, Flink)
Query Engines: Trino, StarRocks, ClickHouse

Lakehouse Architecture

Object Storage + Open Table Formats (Iceberg, Delta, Hudi)
Unified access with Trino & Hive Metastore
Data quality layers with Great Expectations and ydata_profiling

"All houses" architecture "All houses" architecture

🤖 ML & AI Artifacts

Modern data architecture must natively support AI workflows. These components define the AI-ready foundation of my stack:

Feature Engineering Layer

Feature Stores: Feast, Vertex AI Feature Store, or custom Delta-based feature hubs
Reusable, version-controlled features across ML models

Model Management

Model Registry: MLflow, SageMaker Model Registry, Weights & Biases
Track model metrics, artifacts, and lineage
Automated deployment triggers to inference endpoints

MLOps

CI/CD for models with Jenkins or GitHub Actions
Model training pipelines on Databricks, Vertex AI, or custom Airflow DAGs
Batch and real-time inference APIs via FastAPI or gRPC services

AI Observability

Drift detection and retraining triggers (EvidentlyAI, Arize, or custom)
Bias testing and interpretability via SHAP, LIME, or integrated dashboards

ML lifecycle ML lifecycle

🧠 AI in SDLC (Software Development Lifecycle)

AI now acts as a co-pilot across the engineering lifecycle:

SDLC Phase	AI Augmentation	Tooling / Example
Planning & Design	AI-assisted system design, architecture generation	ChatGPT, Claude, Copilot Labs
Development	Auto code generation, test synthesis	GitHub Copilot, Tabnine
Data & Model Lifecycle	Data validation, model versioning, retraining	Great Expectations, MLflow, Kubeflow
Testing & QA	Synthetic data, anomaly detection	Deepchecks, Faker
Monitoring & Maintenance	Log summarization, anomaly detection, root cause AI	Elastic AI, Datadog ML
Governance	Explainability, lineage, policy enforcement	OpenMetadata, AI Governance Toolkit

AI Overflowing Stack AI Overflowing Stack

🔒 Data Governance, Quality & Compliance

Governance: Apache Ranger, OpenMetadata, IAM Roles
Lineage: dbt docs, OpenLineage
Testing & Profiling: Great Expectations, Soda Core
Compliance: GDPR / HIPAA / SOC2 readiness

📊 Visualization & Consumption Layer

Enterprise BI: Power BI, Looker
Embedded analytics: Superset, Metabase
Realtime dashboards for operational insights
Generative AI-enhanced insights: LLM-based BI Q&A or AI co-pilot on data

🧩 Engineering Standards

CI/CD Pipelines: Code + Data + Model in unified workflows
Coding Practices: Python (FastAPI, PySpark), SQL, and Java Spring Boot
Version Control: Semantic commits + dbt model versioning
Documentation: Auto-generated lineage and doc sync with metadata store

DataOps DataOps

🏗️ Architecture Example — Healthcare AI Data Platform

Enterprise-grade healthcare lakehouse data platform architecture: Lakehouse platform Lakehouse platform

Design Principles:

Vision-Aligned Architecture: Lakehouse architecture supports KPJ’s goal of a unified, scalable, and secure healthcare data platform.
Hybrid Deployment: Built on Cloudera Data Platform (CDP) with on-prem for critical ops and cloud-ready for scale.
Medallion Lakehouse Model: Implements Bronze, Silver, and Gold zoning for raw, refined, and curated data.
Open Standards Compliance: Aligns with FHIR, HL7, OMOP, ICD for healthcare interoperability.
Unified Metadata Governance: Uses Apache Atlas & Apache Ranger for lineage and metadata across all zones.

💡 Closing Thoughts

“Data Architecture is the skeleton. AI is the nervous system. Together, they form the living organism of modern software.”

The future of data platforms is AI-native — where data, models, and applications continuously learn and improve. These are my evolving standards for designing architectures that aren’t just scalable, but self-improving.

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