Description

A focused course, tailored for you

Building a Modern Data Stack with Lakehouse Architecture for Regulated Industries

Build a production lakehouse stack from scratch in 12 weeks. Iceberg + Delta + Unity Catalog patterns. Plus GDPR + HIPAA + SOC 2 overlay.

Lakehouse architecture went from research pattern to production-default in 2 years. Regulated-industry clients now expect lakehouse-pattern data platforms with full governance, lineage, and compliance overlays from day one. Here's the 12-week build playbook your team can ship.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Lakehouse architecture (Iceberg, Delta Lake, Hudi on top of object-store with metadata catalogue) is now production-default for regulated-industry data platforms. Healthcare, financial services, and federal customers expect: open table format (Iceberg or Delta), unified catalogue (Unity, Polaris, Open Catalog), lineage and observability, RBAC/ABAC enforcement at the catalogue layer, audit trail for every read and write, and seamless interoperability with Spark, Trino, Snowflake, Databricks, BigQuery, Redshift, and emerging engines.

This course teaches the lakehouse-build playbook your team can ship in client engagements: architecture decisions (Iceberg vs Delta vs Hudi), catalogue selection (Unity vs Polaris vs Open Catalog vs Tabular vs custom), storage strategy (S3 vs Azure Blob vs GCS, plus tiering), compute strategy (multi-engine, autoscaling), and the regulated-industry overlays (GDPR data-subject rights, HIPAA PHI handling, SOC 2 audit-trail). Twelve modules, each ending with a deliverable artefact. Plus a hand-built implementation playbook for your specific client engagement profile.

What you walk away with

A documented lakehouse-architecture decision framework.
An Iceberg or Delta production-pattern reference architecture.
A catalogue-selection methodology for Unity, Polaris, Open Catalog.
An RBAC/ABAC enforcement pattern at the catalogue layer.
A lineage and observability framework.
A regulated-industry overlay (GDPR + HIPAA + SOC 2).
A 12-week client engagement implementation plan.

The 12 modules

Module 1. Lakehouse vs warehouse vs lake: 2026 decision framework

Detailed walkthrough of the 2026 lakehouse landscape: open table formats (Iceberg, Delta Lake, Hudi, Paimon), catalogues (Unity, Polaris, Open Catalog, Tabular, Glue, Hive Metastore), compute engines (Spark, Trino, Snowflake, Databricks, BigQuery, Redshift, Flink, Snowflake Iceberg integration). When lakehouse vs cloud-warehouse vs hybrid. Decision framework for client engagement. Deliverable: architecture decision document.

Module 2. Iceberg deep dive and production patterns

Iceberg architecture and metadata model: snapshots, manifests, partition specs, schema evolution, time travel, compaction strategy. Production patterns: write-amplification mitigation, partition design, table-maintenance strategy, multi-engine coexistence (Spark write + Snowflake read), incremental processing patterns. Deliverable: Iceberg production-pattern reference architecture. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting that lands the artefact for review.

Module 3. Delta Lake deep dive and production patterns

Delta architecture and transaction log: Delta protocol versions, change data feed, deletion vectors, liquid clustering, UniForm for Iceberg interop. Production patterns: optimisation strategy, vacuum cadence, time-travel retention, Unity Catalog integration. When Delta vs Iceberg for client engagements. Deliverable: Delta production-pattern reference architecture.

Module 4. Catalogue selection and the open catalog wars

2026 catalogue landscape: Unity Catalog (Databricks), Polaris (Snowflake-led OSS), Apache Polaris (open governance), Open Catalog, Tabular (acquired into Databricks), Apache Gravitino, Lakekeeper. Federation patterns (multi-catalog), credentials-vending services, RBAC/ABAC enforcement at catalog layer. Decision framework for client engagement. Deliverable: catalogue selection document.

Module 5. Storage strategy and tiered architecture

Object-store selection (S3, Azure Blob, GCS, MinIO for on-prem), tiering strategy (Standard, IA, Glacier; Hot, Cool, Archive), lifecycle policies, replication for DR, encryption (KMS-managed vs customer-managed keys), and the cost-model (egress, request, storage). The storage-cost optimisation that protects the business case. Deliverable: storage strategy document.

Module 6. Compute strategy and multi-engine pattern

Compute strategy for the lakehouse: Spark (Databricks, EMR, Dataproc, OSS), Trino (Starburst, OSS), Snowflake, BigQuery external tables, Redshift Spectrum, DuckDB for edge, Flink for streaming. Multi-engine compatibility patterns. Autoscaling and cost controls. When dedicated cluster vs serverless vs autoscaling. Deliverable: compute strategy document.

Module 7. Ingestion patterns: batch, streaming, CDC

Ingestion patterns: batch via Spark/Trino, streaming via Kafka + Flink + Spark Structured Streaming, change-data-capture via Debezium + Kafka or managed CDC (Fivetran, Airbyte, Estuary), Iceberg + Flink streaming inserts. Pattern selection by data-source characteristic. Deliverable: ingestion pattern matrix. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting that lands the artefact for review.

Module 8. Governance, lineage, and observability

Governance framework: catalog-level RBAC, column-level masking, row-level filtering, lineage capture (OpenLineage, Marquez, Atlan, Datahub, Alation), observability (Monte Carlo, Bigeye, Soda, OSS like Great Expectations). Lineage as the foundation of regulator engagement. Deliverable: governance and observability framework. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting that lands the artefact for review.

Module 9. Regulated-industry overlay: GDPR

GDPR overlay for the lakehouse: data-subject-rights workflow (access, rectification, erasure), right-to-be-forgotten implementation in Iceberg/Delta (deletion vectors, copy-on-write), lawful-basis tracking, processor-vs-controller distinction, cross-border-transfer model (SCCs, adequacy, DPF). Iceberg + Delta erasure patterns. Deliverable: GDPR overlay document. Three worked examples drawn from real implementation packages plus the conversation-script for the next sponsor meeting that lands the artefact for review.

Module 10. Regulated-industry overlay: HIPAA and SOC 2

HIPAA PHI handling in the lakehouse: PHI-tagging convention, encryption at rest and in transit, audit-trail requirement, business-associate-agreement model, breach-notification protocol. SOC 2 overlay: control mapping (CC series), audit-evidence capture, attestation-readiness model. The control-coverage matrix that satisfies both. Deliverable: HIPAA + SOC 2 overlay document.

Module 11. Performance optimisation and cost

Performance patterns: partition pruning, predicate pushdown, file-format selection (Parquet, ORC), file-size targeting, Z-ordering, compaction strategy, materialised views. Cost patterns: tiering, query-aware compute selection, autoscaling rules, idle-shutdown, multi-cluster routing. The cost-and-performance dashboard that the CFO reads. Deliverable: performance and cost framework.

Module 12. Your 12-week client engagement plan

Week-by-week plan with weekly deliverables for client engagement. Weeks 1-2: client architecture decision + Iceberg or Delta selection. Weeks 3-4: catalogue selection + storage strategy + RBAC design. Weeks 5-6: ingestion patterns + first pipeline. Weeks 7-8: governance and observability framework. Weeks 9-10: regulated-industry overlay. Weeks 11-12: performance optimisation + handover documentation. Deliverable: full client lakehouse package.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Modules 1 to 6 cover lakehouse architecture, format selection, catalogue, storage, and compute decisions.

Modules 7 to 11 cover ingestion, governance, regulated-industry overlays, and performance.

Module 12 covers the 12-week client engagement plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates for architecture decision document, Iceberg/Delta production patterns, catalogue selection, storage strategy, RBAC framework, governance and observability, GDPR overlay, HIPAA + SOC 2 overlay, performance and cost framework.
A hand-built implementation playbook generated for your specific client engagement profile.
Three worked examples of lakehouse builds at regulated-industry clients (financial services, healthcare, federal).
Scripted talking points for the client architecture-review board.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: Architecture decision framework adopted.

Week 2: First client architecture decision shipped.

Week 4: Catalogue + storage + RBAC design delivered.

Week 8: Governance and observability framework delivered.

Week 12: Full client lakehouse package delivered.

Before and after

Before

Your firm ships data engagements but lakehouse patterns are ad-hoc. Clients ask for Iceberg vs Delta guidance. Regulator overlays are afterthoughts. Practice principal wants a productised lakehouse offering.

After

A documented lakehouse-build playbook is shippable to client engagements. Iceberg or Delta production patterns are in place. Catalogue, RBAC, governance, observability, and regulated-industry overlays are tailored. The practice has a productised lakehouse offering.

What happens if you do not address this

Lakehouse architecture is now the production-default for regulated-industry data platforms. Consulting firms without a shippable lakehouse offering lose engagements to firms that do.

Who it is for

For data engineers, data architects, data-platform engineers, and consulting practice leaders shipping lakehouse engagements to regulated-industry clients.

Who this is NOT for. Pure research roles. Engineers with no client-engagement scope. Firms not building data engagements.

How it arrives

Text-based course via LMS, plus downloadable templates and the hand-built implementation playbook.

Time investment. Roughly 18 hours of reading and 40 to 60 hours building the first client engagement deliverable.

Why $199 is the right number

External lakehouse consultants charge $200K-$1M for production-pattern engagements. Specialist data-engineering firms (dbt Labs, Streamkap, Onehouse) charge $500K-$1M. $199 buys the focused playbook plus the implementation document for your client engagement profile.

FAQ

Will this replace hiring a lakehouse specialist?

Partially. It teaches you the production patterns your team ships. You may still want specialist input for complex multi-engine performance tuning.

What if my client wants Delta only (Databricks-anchored)?

Module 3 covers Delta-anchored patterns in detail.

Does this cover legacy migration (Hive to Iceberg, Glue to Polaris)?

Module 4 covers Hive Metastore migration patterns.

What about streaming-only architectures (Flink + Iceberg)?

Module 7 covers streaming patterns including Flink + Iceberg streaming inserts.

What is in the implementation playbook for me specifically?

An architecture decision template tailored to your typical client engagement; a 12-week build plan with milestones.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.