Data as a Product — Part 1: Foundations
2026-03-1719 min read
Foundations & concepts: DaaP, product thinking, self-service, quality, anatomy.
13 posts

2026-03-17·19 min read
Foundations & concepts: DaaP, product thinking, self-service, quality, anatomy.

2026-03-17·7 min read
Git workflow strategy for Lakehouse: 5 work streams, main/staging/develop, feature branches, monthly review, CI/CD, hotfix, best practices.

2026-03-17·1 min read
Seven-layer Lakehouse: Ingestion (Airbyte, Kafka), Processing (Spark, Flink, dbt), Storage Format (Iceberg, Delta), MinIO, Metadata, Trino, Consumption.

2026-03-17·2 min read
Strategic goals, project rationale, data ecosystem, Lakehouse scope, and success criteria for a unified data platform.

2026-03-17·2 min read
Seven-layer Lakehouse architecture: Ingestion, Processing, Storage Format, Storage, Metadata, Query, Consumption. Batch and streaming.

2026-03-17·2 min read
End-to-end data flow: sources (Core, CRM, Payment, Risk, API), Airbyte/Kafka/Flink, Spark/dbt, Iceberg/MinIO, DataHub, Trino, Superset/ML/API.

2026-03-17·2 min read
Ingestion Layer: collect from CRM, Core, Risk, Payment, API; batch (Airbyte) and streaming (Kafka); CDC, retry, schema registry, quality control.

2026-03-17·1 min read
Security & Privacy: protect all layers, RBAC, PII masking, audit, encryption, compliance with personal data protection regulations.

2026-03-17·2 min read
BRD appendix: 6 layers (Raw, Staging, Curated, Analytics, Lineage & Metadata, Audit); 4-step lineage raw→stg→cur→ana.

2026-03-17·1 min read
Intro to a 7-post series specifying each Lakehouse layer: Raw, Staging, Curated, Analytics, Metadata & Governance, Query & BI, Consumption.

2026-03-17·20 min read
MinIO trong Lakehouse: S3 API, buckets, security.

2026-03-17·16 min read
PostgreSQL trong Lakehouse: schema, labs, integration.

2026-03-17·5 min read
Development guidelines for Lakehouse team: Git workflow, coding standards, testing, security, deployment, code review, communication.