Comparison
Data Warehouse vs Data Lakehouse
How DataTalks.Club podcast guests compare warehouse-centered analytics with lakehouse architectures built from object storage, table formats, catalogs, compute engines, and governance.
Related Wiki Pages
A data warehouse is modeled analytical storage for governed SQL work. It supports ingestion and transformations. It also supports business-facing tables, BI metrics, and operational syncs.
In DataTalks.Club podcast discussions, the warehouse usually appears inside the modern data stack. It sits close to ELT and dbt-style modeling. Orchestration and activation sit nearby too.
Natalie Kwong places warehouses and marts in that same map. She also connects them to transformations and reverse data flows (ETL vs ELT and the Modern Data Stack, 10:00-18:47 and 30:59-35:42).
A data lakehouse keeps a data lake storage boundary while adding warehouse-like table behavior. Metadata, access, and governance are part of that design. Lars Albertsson describes lakehouse architecture as warehouse-style use layered onto a lake-style platform. He gets there through raw object storage and self-service SQL. Ingress and egress are part of the same platform discussion (DataOps 101 for Scaling Data Platforms, 21:29-30:34 and 1:07:52).
Adrian Brudaru updates that vocabulary through Apache Iceberg, Parquet-backed table formats, and catalogs. Metadata and lineage sit there too (Modern Data Engineering Trends, 18:17-23:41 and 49:42).
The useful comparison isn’t “old warehouse versus new lakehouse” because the real decision is where analytical trust lives. A warehouse concentrates storage and compute in a managed analytical system. Permissions, SQL modeling, and BI integration sit close to that system.
A lakehouse preserves open storage and multiple compute paths, so the team must still operate table formats and catalogs. Lineage, quality checks, and access controls are part of the same data engineering platform and DataOps responsibility.
Common Definition
The strongest warehouse-first episodes define the warehouse around trusted analytics. Kwong’s modern-stack episode connects warehouse-side transformations with data marts and dbt. Orchestration and reverse ETL are part of the same operating flow (ETL vs ELT and the Modern Data Stack, 15:30-18:47 and 30:59-35:42).
Arpit Choudhury uses the same structure for growth data. His flow starts with event collection and warehouse storage before moving into dbt transformations, BI, and data activation through reverse ETL (Data-Led Growth Stack, 22:50-41:30).
That definition favors a warehouse when the team needs consistent modeled tables and SQL access more than direct control over open files. Dashboards, metrics, and customer or product activation belong on that side too. It also fits analytics engineering work, where tests and documentation sit close to BI-facing warehouse tables.
The strongest lakehouse episodes define the lakehouse around open storage and shared platform layers. Albertsson starts from object storage and raw data. He then adds compute, workflow engines, and governance before naming self-service SQL and lakehouse architecture (DataOps 101 for Scaling Data Platforms, 23:29-35:57 and 1:07:52).
Brudaru’s Iceberg discussion adds the modern table format version. Files and tables are separate from compute engines, while catalogs and metadata become explicit platform layers. Access and lineage do too (Modern Data Engineering Trends, 18:17-30:31).
That definition favors a lakehouse when raw and modeled data need to live on open storage. It also fits when more than one engine must read the same tables. Spark-style processing and vendor lock-in concerns make the case stronger. ML workloads can fit here when they need shared open tables rather than only BI-facing models.
Kwong explicitly leaves room for teams to keep both systems. The split depends on users and data shapes (ETL vs ELT and the Modern Data Stack, 24:24-27:39).
Guest Differences
Natalie Kwong frames the choice from the analytics stack. Her warehouse argument is strongest when ELT lets teams load first and transform later in the warehouse. Analysts can then use SQL and dbt-style workflows without rebuilding extraction code (ETL vs ELT and the Modern Data Stack, 7:57-18:47). She’s not anti-lake because the same episode defines lakes for files, logs, and media. It also warns that unmanaged lakes become data swamps without governance and ownership (19:50-24:24).
Lars Albertsson starts from platform operations rather than BI consumption. His warehouse/lake distinction is tied to raw data, aggregates, and use cases. Object storage, ingress, and egress are part of the same platform view. Self-service SQL belongs there too (DataOps 101 for Scaling Data Platforms, 21:29-30:34). He then brings in immutable pipeline design, reproducibility, lineage, and versioning as the operating disciplines that keep lakehouse-style platforms usable (16:42-21:29 and 1:04:18-1:07:52).
Adrian Brudaru is more skeptical of buying a packaged stack without decomposing the layers. His 2025 discussion separates storage and compute. It also separates access, metadata, and lineage. Later it compares Delta Lake, Hudi, and Iceberg (Modern Data Engineering Trends, 21:27-23:41 and 49:42).
His requirements-led tool guidance makes the lakehouse decision a catalog and cost question. Lock-in and operating model matter too, not only the table-format choice (44:42).
Santona Tuli treats the choice as pipeline- and persona-specific. She compares Snowflake, Databricks, and Upsolver before discussing build-vs-buy choices. Staging and lakehouse patterns come next (Modern Data Pipeline Architecture, 29:16-37:10). Her ingestion sections name the early platform work. Deduplication, ordering guarantees, and PII masking may need to happen before data becomes useful for analytics or ML (37:10).
Eddy Zulkifly adds the cost dimension. His FinOps discussion places BigQuery and dbt inside a digital warehouse. Orchestration, monitoring, and tests belong there too. He then moves into reservation planning and storage tiers. Tagging, cost reporting, and accountability come next (FinOps for Data Engineers, 21:57-24:34 and 31:40-46:17).
That makes cost management a design requirement for both warehouse and lakehouse paths.
Consumer Workflow
Warehouses are strongest when the consumer workflow is SQL analytics and BI. They also fit product analytics and activation. Kwong ties warehouse-side transformation to analyst autonomy and data marts. dbt is part of that same flow (ETL vs ELT and the Modern Data Stack, 10:00-18:47).
Choudhury ties warehouse-first growth analytics to Snowflake and BigQuery. The same discussion includes Redshift, BI, and reverse ETL (Data-Led Growth Stack, 28:52-41:30).
Lakehouses are strongest when the consumer workflow spans raw files and large events. Open table storage and ML pipelines fit here too. Spark-style processing and multiple SQL or compute engines also belong on this side.
Albertsson names storage, compute, and workflow engines as core platform components. He then discusses Spark, Flink, and containers. The same comparison includes managed services too (DataOps 101 for Scaling Data Platforms, 30:34-35:57). Brudaru’s catalog discussion adds the table, metadata, and lineage layer that makes open-storage tables discoverable and governed (Modern Data Engineering Trends, 18:17-23:41).
The same organization can need both workflows. Kwong discusses cases where a team maintains a lake, a warehouse, or both. The split depends on whether the data is structured analytical data or less structured files. Logs and media belong on the lake side of that distinction (ETL vs ELT and the Modern Data Stack, 19:50-27:39).
The episodes support a split where teams keep raw events and long-lived history in lake-style storage while Finance, growth, BI, and activation consume warehouse-modeled tables.
Storage and Table Layer
A warehouse hides most storage details behind the analytical database. That’s useful when the main interface is modeled tables, permissions, BI, and SQL transformations. Kwong’s warehouse discussion keeps marts, transformations, and orchestration close to the same analytical destination (ETL vs ELT and the Modern Data Stack, 15:30-18:47 and 30:59-35:42). Choudhury’s growth stack uses warehouse storage as the place where event data becomes BI analysis and activation-ready customer data (Data-Led Growth Stack, 28:52-37:25).
A lakehouse exposes the storage and table layer as part of the architecture. Brudaru explains Iceberg as a table format over Parquet-style storage, then separates storage and compute. Access, metadata, and lineage are separate layers too (Modern Data Engineering Trends, 18:17-23:41).
That separation is powerful only if the team also operates a catalog and permissions. Quality checks, ownership paths, and lineage are part of the same work. Albertsson and Brudaru draw the same boundary. Without catalog, lineage, and operating ownership, the team has storage plus a table format. It doesn’t have a usable lakehouse.
Tuli’s pipeline discussion shows how the boundary affects design. Her staging and lakehouse section places Snowflake, Databricks, and Upsolver in a build-vs-buy conversation. It then connects staging to transformations, entities, and foreign keys. Data marts that feed dashboards sit downstream (Modern Data Pipeline Architecture, 29:16-44:57). The storage choice is therefore connected to ingestion and modeling design, not isolated from it.
Governance and Trust
Warehouse trust usually comes from a smaller number of managed surfaces. The warehouse keeps modeled schemas and permissions close together. Tests, BI semantics, and dbt-style documentation sit in the same operating layer. Kwong connects warehouse-side transformation to dbt and analytics engineering (ETL vs ELT and the Modern Data Stack, 12:39-18:47). Choudhury later adds documentation and self-service analytics as adoption requirements for growth data teams (Data-Led Growth Stack, 51:40-53:48).
Lakehouse trust has more moving parts because governance spans object storage and catalogs. Compute engines and downstream consumers add more surfaces. Albertsson’s DataOps framing keeps governance close to object storage and raw dumps. It also ties trust to ingress, egress, and versioning. Lineage matters too (DataOps 101 for Scaling Data Platforms, 23:29-28:22 and 1:04:18-1:07:52).
Brudaru’s catalog sections make metadata and lineage explicit parts of the platform rather than background details (Modern Data Engineering Trends, 21:27-23:41).
Both paths need data quality and observability, but the trust work concentrates in different places. In a warehouse-centered stack, tests and documentation often live around SQL models and BI-facing tables. In a lakehouse, the team also has to govern the table format and catalog. Raw storage and multiple access paths need control too.
Cost and Lock-In
Warehouse convenience can hide cost as query volume and dashboard use grow. FinOps for Data Engineers is the canonical page for the cost-visibility and accountability side of that decision.
Reverse ETL or storage growth can add more spend. Zulkifly treats cloud cost optimization as part of data engineering work. His examples include reservations and storage tiers. Forecasting, tagging, and accountable cost reporting are part of the same practice (FinOps for Data Engineers, 31:40-46:17).
That applies directly to warehouse-first stacks built on BigQuery or Snowflake. It also applies to Redshift-style stacks where BI and transformations drive recurring spend.
Lakehouses can reduce some lock-in by keeping data in open storage, but they move more responsibility into the platform. Brudaru says Iceberg can reduce vendor lock-in by separating the table format from a single engine. He then discusses headless table formats and portable compute options such as DuckDB (Modern Data Engineering Trends, 18:17 and 25:58-30:31). The tradeoff is that catalogs and orchestration still cost engineering time. Access, lineage, and quality controls need operational attention too.
Cost can therefore go either way, though the podcast archive doesn’t give a single warehouse-versus-lakehouse cost formula. A warehouse can be the cheaper choice when one managed SQL system serves the consumers and FinOps practices control usage. A lakehouse can be the better choice when open storage, multiple engines, or long-lived raw history avoids expensive copying or product lock-in. The archive supports making that call from requirements rather than from architecture labels (Modern Data Engineering Trends, 44:42 and FinOps for Data Engineers, 40:18-46:17).
Migration Triggers
Don’t migrate from a warehouse to a lakehouse just because the vocabulary is new. Brudaru’s critique of packaged stacks argues against architecture by trend. His requirements-led tool selection does the same (Modern Data Engineering Trends, 14:32 and 44:42).
If BI and dbt models already serve the business, the better move may be improving the warehouse path. Warehouse permissions and reverse ETL belong in that improvement path. Cost controls and documentation belong there too. Orchestration and FinOps do as well (ETL vs ELT and the Modern Data Stack, 30:59-43:02 and FinOps for Data Engineers, 40:18-46:17).
A stronger lakehouse trigger is a concrete need for open storage and multiple compute engines. Raw-file retention and Spark-style processing make the case stronger. Lower vendor lock-in can do the same. ML workloads strengthen the case when they need shared open tables or Spark-style processing.
Albertsson’s platform discussion places storage, compute, and workflow engines in the same architecture decision. Spark, Flink, and managed services belong there too (DataOps 101 for Scaling Data Platforms, 30:34-39:57). Brudaru’s Iceberg and headless-table-format sections explain how open table formats and catalogs support that split (Modern Data Engineering Trends, 18:17-30:31).
Before choosing, map the workload to the actual consumer. Analysts and BI users usually point toward warehouse-modeled tables, while ML engineers and platform engineers may push the architecture toward shared open storage. Product teams and operational tools often strengthen the warehouse or activation path when they need modeled customer and product data.
Also classify the source data and operating ownership. A warehouse path fits modeled business tables, while raw files and logs strengthen the lakehouse case. Media, event streams, and long-lived history also strengthen it.
The team should decide whether one SQL engine is enough, because several engines may need shared tables without copying data. The team should also assign metadata and lineage ownership. Permissions, freshness, and cost reporting need owners too.
Kwong grounds the consumer and source-data checks (ETL vs ELT and the Modern Data Stack, 19:50-27:39). Albertsson grounds the compute and workflow checks (DataOps 101 for Scaling Data Platforms, 30:34-35:57).
Brudaru’s catalog split grounds the metadata and lineage checks. Zulkifly’s FinOps framing adds the cost side (Modern Data Engineering Trends, 21:27-23:41 and FinOps for Data Engineers, 34:15-44:41).
Related Pages
Use these adjacent pages for the storage, platform, and analytics vocabulary around the comparison.