Description

A focused course, tailored for you

Apache Iceberg for Multi-Engine Federated Queries: Production Patterns and Migration

Build the Iceberg multi-engine federation pattern from scratch in 10 weeks. Catalog selection + Spark write + Snowflake/Trino/DuckDB read + governance.

Apache Iceberg went from incubation to default open table format in 24 months. Multi-engine federation (Spark write, Snowflake/Trino/DuckDB read against same tables) is now the production pattern enterprise data teams expect. Here's the 10-week build playbook.

$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

Apache Iceberg is now the default open table format for enterprise data platforms. The multi-engine federation pattern (Spark for transformations, Snowflake for BI, Trino for ad-hoc, DuckDB for edge, all reading the same Iceberg tables) is the production architecture enterprise clients now expect. The pattern works only when catalog selection, RBAC enforcement, schema evolution, partitioning strategy, and concurrency control are designed deliberately.

This course teaches the 10-week build of an Iceberg multi-engine federation: catalog selection (Polaris, Unity, Open Catalog, Tabular, Glue, Hive), Spark + Iceberg write pattern, Snowflake + Iceberg external integration, Trino + Iceberg federation, DuckDB + Iceberg edge query, governance and RBAC at catalog layer, schema-evolution strategy, partitioning and compaction strategy, observability, and migration from Parquet-on-S3 or Hive Metastore. Twelve modules, each ending with a deliverable artefact. Plus a hand-built implementation playbook for your specific multi-engine stack.

What you walk away with

A catalog selection methodology (Polaris vs Unity vs Open Catalog vs Tabular vs Glue).
A Spark + Iceberg production write pattern.
A Snowflake + Iceberg external integration pattern.
A Trino + Iceberg federation pattern.
A DuckDB + Iceberg edge query pattern.
A governance and RBAC framework at the catalog layer.
A schema-evolution + partitioning + compaction strategy.
A migration plan from Parquet-on-S3 or Hive Metastore.
A 10-week build plan.

The 12 modules

Module 1. Iceberg landscape and 2026 multi-engine ecosystem

Detailed walkthrough of Iceberg specification (v1, v2, v3-draft), Iceberg metadata model (snapshots, manifests, partition specs), the 2026 multi-engine ecosystem (Spark, Snowflake, Trino, Databricks, BigQuery, Redshift, DuckDB, Flink, ClickHouse), and the engine-by-engine maturity matrix for read and write. When to use Iceberg vs Delta vs Hudi for multi-engine federation. Decision framework. Deliverable: architecture decision document.

Module 2. Catalog selection deep dive

2026 catalog landscape: Apache Polaris (open governance, Snowflake-aligned), Unity Catalog (Databricks-led, OSS in 2024), Open Catalog (federated), Tabular (Databricks acquisition), Apache Gravitino, Lakekeeper, AWS Glue, Hive Metastore. Federation patterns (multi-catalog), credentials-vending services, RBAC/ABAC enforcement at catalog layer, REST catalog spec compatibility. Decision framework for client engagement. Deliverable: catalog selection document with three options.

Module 3. Spark + Iceberg production write pattern

Build the Spark write pattern: Iceberg connector configuration, write-path optimisation (write-amplification mitigation, compaction strategy, distribution mode), partition design (hash, range, bucket, hidden partitioning), schema-evolution support, branching and tagging usage, and snapshot-management strategy. Multi-version Spark coexistence. Deliverable: Spark + Iceberg production pattern.

Module 4. Snowflake + Iceberg external integration

Build the Snowflake integration: Iceberg external tables (catalog-integration with Polaris and Unity), automatic-refresh strategy, query-acceleration (Search Optimization, Snowpark, materialised views over Iceberg), and the credentials-vending model. Snowflake-managed Iceberg vs externally-managed tables. Performance optimisation for cross-engine queries. Deliverable: Snowflake + Iceberg pattern document.

Module 5. Trino + Iceberg federation

Build the Trino federation: Iceberg connector configuration, multi-catalog federation pattern, predicate pushdown and statistics integration, dynamic filtering, vectorised reads, materialised-view alignment, and the autoscaling pattern. Trino as the federation engine across multiple Iceberg catalogs. Deliverable: Trino + Iceberg federation pattern.

Module 6. DuckDB + Iceberg edge query

Build the DuckDB edge pattern: DuckDB Iceberg extension, local file caching, partitioned-read optimisation, secret-management for cloud catalogs, and the embedded-query architecture (Streamlit, Jupyter, embedded apps, Notebook-as-a-Service). DuckDB extends Iceberg federation to lightweight edge analytics. Deliverable: DuckDB + Iceberg edge pattern.

Module 7. Governance and RBAC at catalog layer

Build the governance framework: catalog-level RBAC, column-level masking, row-level filtering (where supported), credential-vending model (per-principal scoped credentials), audit logging, and the cross-engine consistency strategy. The governance model that survives multi-engine reads. Three governance patterns from peer engagements. Deliverable: governance and RBAC framework.

Module 8. Schema-evolution and partitioning strategy

Iceberg supports schema evolution and hidden partitioning. Build the schema-evolution strategy: column addition, type promotion, partition-spec evolution (a unique Iceberg capability), and the backward-compatibility model for multi-engine reads. Partitioning best practices for typical workload patterns. Deliverable: schema-evolution and partitioning strategy.

Module 9. Compaction, maintenance, and table optimisation

Build the compaction-and-maintenance strategy: file-compaction (bin-pack, sort), expire-snapshots cadence, remove-orphan-files, rewrite-data-files, rewrite-manifests, table-statistics maintenance, and the maintenance-job orchestration. The maintenance schedule that keeps multi-engine performance predictable. Deliverable: compaction and maintenance runbook. 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. Observability and cost analysis

Build the observability: per-engine query telemetry, table-level access pattern, snapshot churn rate, file-size distribution, query-cost-per-engine, and the cross-engine cost dashboard. Cost analysis at table-and-engine level. Deliverable: 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 11. Migration: Parquet-on-S3 or Hive Metastore to Iceberg

Build the migration plan: legacy assessment (Parquet-on-S3, Hive Metastore, Athena, Glue), Iceberg table creation (add_files for in-place, migrate procedure for snapshot), validation strategy, multi-engine validation, cutover plan, and the rollback model. Migration is where Iceberg adoption succeeds or fails. Three worked migrations. Deliverable: migration plan template.

Module 12. Your 10-week build plan

Week-by-week plan with weekly deliverables. Weeks 1-2: architecture decision + catalog selection. Weeks 3-4: Spark + Iceberg production pattern. Weeks 5-6: Snowflake + Iceberg integration + Trino federation pattern. Week 7: DuckDB edge pattern + governance framework. Weeks 8-9: schema-evolution + compaction-and-maintenance + observability. Week 10: migration plan + first table migration. Deliverable: full production-ready Iceberg multi-engine federation.

How this addresses your situation

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

Modules 1 to 2 cover the Iceberg landscape and catalog selection.

Modules 3 to 6 produce engine-by-engine production patterns (Spark write, Snowflake/Trino/DuckDB read).

Modules 7 to 10 cover governance, schema evolution, compaction, and observability.

Modules 11 to 12 cover migration and the 10-week build plan.

What you get with this course

The 12-module course delivered as text plus downloadable templates.
Templates for catalog selection, Spark/Snowflake/Trino/DuckDB Iceberg patterns, governance and RBAC, schema-evolution, compaction, observability, migration plan.
A hand-built implementation playbook generated for your specific multi-engine stack.
Three worked examples of Iceberg federations at peer firms.
Scripted talking points for client architecture-review board.

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

Day 1: Architecture decision drafted.

Week 4: Spark + Iceberg production pattern shipped.

Week 6: Snowflake + Trino federation pattern shipped.

Week 8: Governance + maintenance operational.

Week 10: Full Iceberg federation running with first migration completed.

Before and after

Before

Your firm or client uses Parquet-on-S3 or Hive Metastore. Single-engine lock-in is constraining usage. Multi-engine federation is a stated goal but the production pattern is not in place.

After

An Iceberg multi-engine federation is running. Spark writes. Snowflake, Trino, and DuckDB read. Catalog enforces RBAC. Schema evolution works across engines. Compaction maintains performance. The pattern is shippable to next engagement.

What happens if you do not address this

Iceberg + multi-engine federation is the production-default for enterprise data platforms. Firms without the pattern are stuck in single-engine lock-in and lose engagements where federation is required.

Who it is for

For data engineers, data architects, and platform engineers building Iceberg-based data platforms at IT services firms and end-customer enterprises.

Who this is NOT for. Pure research roles. Firms not building data platforms.

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 60 to 120 hours building the first production federation.

Why $199 is the right number

External Iceberg consultants charge $200K-$1M for production patterns. Specialist data-engineering firms (Tabular, Onehouse, Starburst, Dremio) charge $300K-$1.5M. $199 buys the focused playbook plus the implementation document for your multi-engine stack.

FAQ

Will this replace hiring an Iceberg consultant?

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

What if my organisation is Snowflake-anchored?

Module 4 covers Snowflake-anchored Iceberg patterns.

Does this cover Iceberg V3 (deletion vectors, position deletes)?

Module 1 covers spec evolution; Module 8 covers V3 features as they land.

What about REST catalog specification compatibility?

Module 2 covers REST catalog and cross-catalog interop.

What is in the implementation playbook for me specifically?

A catalog selection tailored to your stack; engine-by-engine production patterns; a 10-week build plan.

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.