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Warehouse vs Lake vs Lakehouse (with Iceberg & BigLake)

Dean Jain

Dean Jain

Senior Staff Software Engineer · Enterprise AI, Data & Cloud Architect

· 5 min read

LakehouseApache IcebergData EngineeringBigLake
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    WH["🏛️ Warehouse<br/>structured · governed · costly &amp; rigid"]:::obs --> LH["🏠 Lakehouse<br/>best of both"]:::good
    LK["🌊 Lake<br/>cheap · flexible · ungoverned"]:::warn --> LH
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Figure 1: The lakehouse exists to resolve a real trade-off, warehouse governance against lake economics, by taking the best of each.

For years, data teams ran two systems and copied data between them. A warehouse for clean, governed, BI-ready structured data. A lake for cheap storage of everything else. It worked, but it was expensive, stale and duplicative.

The lakehouse collapses the two. Warehouse-grade features, meaning transactions, schema and governance, implemented directly on cheap open lake storage. And the thing that finally made it possible isn’t a product. It’s open table formats like Apache Iceberg.

TL;DR

  • Warehouse: great for structured data and BI but not suited to unstructured/semi-structured data, and not cost-efficient at high variety/velocity/volume.
  • Lake: cheap storage for raw data in any format but lacks transactions, data-quality enforcement, and the consistency to safely mix reads/writes or batch/streaming.
  • Lakehouse: warehouse features, meaning ACID, schema enforcement, governance and BI, on top of low-cost open storage. One copy of data, decoupled storage and compute, diverse data and workloads.
  • Open table formats make it real. Apache Iceberg (also Delta Lake, Hudi) add ACID, schema evolution, hidden partitioning, and time travel to files in object storage.
  • On GCP: BigQuery + BigLake + Iceberg unify warehouse and lake with fine-grained access control over a single copy of data.

1. Two systems, two compromises

Data warehouses matured through massively-parallel (MPP) architectures. They handle large structured datasets beautifully, and remain the gold standard for BI on clean, modelled data. But modern enterprises drown in unstructured and semi-structured data: images, logs, JSON, video.

High variety, velocity and volume. That is exactly what warehouses handle poorly and expensively. Forcing everything into a warehouse is neither feasible nor cost-efficient.

So teams added data lakes: repositories for raw data in any format on cheap object storage. Lakes solved the cost and flexibility problem but introduced a governance hole. Critically, a bare lake:

  • doesn’t support transactions,
  • doesn’t enforce data quality, and
  • lacks the consistency and isolation to safely mix appends and reads, or batch and streaming jobs.

That’s the bind. The warehouse is governed but rigid and pricey. The lake is cheap and flexible but a free-for-all.

For years the “solution” was to run both and ETL between them. You paid to store and reconcile two copies of your data, with the warehouse always a little stale. The lakehouse exists to end that compromise.

2. The lakehouse: warehouse features on lake storage

A lakehouse is an open architecture that implements data-warehouse-style structures and management features directly on top of low-cost cloud storage in open formats.

Instead of two systems, you have one. Cheap storage, governed like a warehouse. The defining capabilities:

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    LH["🏠 Lakehouse"]:::gov
    LH --> T["⚛️ ACID transactions<br/>concurrent reads/writes"]:::good
    LH --> S["📐 Schema enforcement<br/>+ governance"]:::server
    LH --> B["📊 BI on source data<br/>(no second copy)"]:::obs
    LH --> D["🔗 Storage decoupled<br/>from compute"]:::gate
    LH --> O["📂 Open formats<br/>(Parquet) + APIs"]:::good
    LH --> W["🧪 Diverse data &amp; workloads<br/>+ streaming"]:::warn
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Figure 2: The lakehouse feature set. Exactly the governance a bare lake lacks, added on top of cheap open storage.

  • Transaction (ACID) support multiple pipelines can read and write concurrently with consistency, typically via SQL. This is the single biggest thing a bare lake was missing.
  • Schema enforcement and governance enforce and evolve schemas (including star/snowflake models), reason about data integrity, and audit access.
  • BI support run BI tools directly on source data, cutting staleness and latency, and removing the cost of operationalizing two copies.
  • Storage decoupled from compute separate clusters scale independently, supporting far more concurrent users and larger data.
  • Openness. Standardized storage formats like Parquet, with APIs so any engine reads the data directly: Spark, Trino, Flink, plus ML and Python or R libraries.
  • Diverse data and workloads. Structured through to unstructured, serving SQL, BI, data science and ML from one repository. Streaming is end to end, so real-time and batch share the same store.

Notice each feature maps to a lake’s weakness from §1. The lakehouse isn’t a new idea bolted on it’s the lake plus exactly the governance it lacked.

3. What makes it work: open table formats

A lakehouse’s magic isn’t the storage. That’s just Parquet files in object storage. It’s the open table format layered on top that adds the database-like guarantees. Apache Iceberg (along with Delta Lake and Hudi) is the key enabler:

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    ICE["🧊 Apache Iceberg<br/>(table format over object storage)"]:::gov
    ICE --> F1["⚛️ ACID transactions"]:::good
    ICE --> F2["🔄 Full schema evolution"]:::server
    ICE --> F3["🙈 Hidden partitioning"]:::obs
    ICE --> F4["⏪ Time travel &amp; rollback"]:::gate
    ICE --> F5["🗜️ Data compaction"]:::good
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    classDef gate fill:#D7C3F2,stroke:#8E5BD0,stroke-width:2px,color:#0F172A

Figure 3: Iceberg brings SQL-table reliability to big data. ACID, schema evolution, hidden partitioning and time travel, with many engines working the same tables safely.

Iceberg brings the reliability and simplicity of SQL tables to big data, while letting engines like Spark, Trino, Flink, Presto and Hive safely operate on the same tables at the same time.

Its standout features are what turn a pile of Parquet files into a trustworthy table. Full schema evolution, so you rename, add and drop columns without rewriting data. Hidden partitioning, so the engine manages partitions and queries don’t hardcode them. Time travel and rollback, to query or restore a past snapshot. Plus data compaction and ACID transactions.

On Google Cloud, the lakehouse shows up as BigLake. It unifies warehouse and lake, letting BigQuery and open-source engines like Spark query a single copy of data. You get fine-grained access control and native Iceberg support, though BigQuery’s managed Iceberg tables keep their data in Cloud Storage. Querying other clouds is a separate BigLake and Omni capability. You store one copy, get warehouse-grade access control and lake-grade openness, and skip the duplicate-and-reconcile tax.

When does each still fit? A pure warehouse is still simplest for classic, all-structured BI at moderate scale. A bare lake is fine as a cheap landing zone for raw data you’ll process later. But once you need both governed analytics and cheap flexible storage over diverse data, which is most modern shops, the lakehouse is the architecture that stops you running two systems.

The warehouse-versus-lake debate was always a false choice forced by old technology. You paid for governance or economics, and bridged the gap with brittle ETL. Open table formats dissolved that trade-off. You can now have ACID transactions, schema governance and BI directly on cheap open storage, with one copy of your data and any engine you like.

If you’re still copying data between a lake and a warehouse, the lakehouse is how you stop.

Further reading