Lakehouse Iceberg vs Delta Lake : 2026

Lakehouse = data lake + warehouse capabilities. 2026 : Apache Iceberg gagne traction massive (Snowflake, BigQuery support). Delta Lake leader Databricks.

TL;DR

- Lakehouse : best of lake + warehouse.

- Iceberg : open standard, multi-engine.

- Delta Lake : Databricks-native.

- 2026 : Iceberg = standard de facto.

Comparaison formats

Apache Iceberg :

• Open source Apache
• Multi-engine (Spark, Trino, Snowflake, BQ)
• ACID transactions
• Time travel
• Schema evolution
• Hidden partitioning
• Adoption massive 2024-26

Delta Lake :

• Databricks-led (Linux Foundation)
• ACID, time travel
• Forte intégration Databricks
• Spark-native
• Limites multi-engine

Apache Hudi :

• LinkedIn / Uber origins
• Streaming-first
• Incremental processing
• Best for : CDC use cases

Iceberg adoption 2026

• Snowflake : Iceberg tables natifs (2024+)
• BigQuery : BigLake Iceberg support
• AWS : Athena, Glue, EMR Iceberg
• Databricks : Iceberg interop
• Trino : Iceberg natif
• Spark : Iceberg via library
• Flink : Iceberg connector

Catalogs :

• AWS Glue
• Snowflake Catalog
• Polaris (open source)
• Nessie (Project Nessie)
• Apache Iceberg REST catalog

Architecture lakehouse

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Layers :

• Storage : S3, GCS, Azure Blob
• Format : Parquet (data) + Iceberg (metadata)
• Catalog : Glue, Polaris, REST
• Compute : Spark, Trino, Snowflake, etc.

Avantages vs warehouse classique :

• Storage 10-100x cheaper
• Format ouvert (no lock-in)
• Multi-engine flexibility
• ML workloads native (Parquet)

Avantages vs data lake classique :

• ACID transactions
• Schema evolution
• Time travel
• Performance via stats

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Use cases Africa

• Banques :
• 100 TB+ historique transactions
• Iceberg sur S3 + Snowflake compute
• Save 50-70 % vs Snowflake-managed

• Telcos :
• CDR (Call Detail Records) volume massif
• Iceberg + Trino self-hosted
• Économies massives

• E-commerce :
• Customer 360 + transactions
• Lakehouse Databricks ou Iceberg

• ML training :
• Datasets large (vidéo, image)
• Lakehouse format Parquet

FAQ

Q : Iceberg vs Hudi ?

R : Iceberg leader 2026 adoption. Hudi forte CDC use case. Iceberg défaut sauf raison spécifique.

Q : Migrer Hive → Iceberg ?

R : Migration tools matures. Spark migration scripts. Effort 1-3 mois selon volume.

Conclusion

Lakehouse Iceberg vs Delta Lake 2026 : Iceberg open standard gagne traction massive. Multi-engine + cost savings + flexibility = avantages décisifs. Adoption Snowflake, BigQuery, Databricks. Africa : alternative économique data warehouse classique.