How to Build a Data Platform Without a Warehouse (Lakehouse + Query Layer)

July 15, 2026

Not every team needs a traditional data warehouse to get reliable analytics. If you're trying to move fast, keep costs predictable, or avoid locking into one vendor, you can build a "warehouse-less" data platform by combining object storage, open table formats, and a query layer.

Below is a practical blueprint that works for startups and modern data teams: you'll still get governed tables, SQL access, and BI dashboards-just without a monolithic warehouse as the center of gravity.

1) Use object storage + an open table format as your "source of truth"

Your first move is to put durable, cheap storage at the center (e.g., S3, GCS, ADLS). Store curated datasets as columnar files (Parquet) managed by an open table format like Apache Iceberg or Delta Lake (Iceberg is a common pick for multi-engine support).

Why this matters: file folders alone turn into chaos. A table format adds transactions, schema evolution, partitioning, and time travel-basically the "table guarantees" people expect from warehouses.

Practical example:

Raw zone: append-only events and CDC dumps (JSON/Avro/Parquet)
Curated zone: Iceberg tables like analytics.orders, analytics.customers
Metrics-ready zone: wide tables or aggregates (daily_revenue, funnel_steps)

Minimum setup checklist:

Choose a catalog (Hive Metastore, AWS Glue, or a managed catalog)
Standardize file layout and naming (partition by event_date, org_id, etc.)
Define retention rules (e.g., raw retained 30 days, curated retained forever)

2) Ingest data with CDC + streaming, then transform with SQL-first pipelines

Without a warehouse, you'll typically load data into the lake first and transform there.

Ingestion patterns that work well:

Database CDC: Debezium, Airbyte, Fivetran â†’ land changes into raw tables (or files)
Event streaming: Kafka/PubSub/Kinesis â†’ land event streams into append-only tables
SaaS sources: periodic syncs â†’ land snapshots and dedupe into curated tables

Then transform using a SQL-first engine and an orchestrator:

Engines: Spark, Flink, or DuckDB (small) for heavy lifting
Orchestration: Dagster, Airflow, or Prefect to schedule and retry jobs
Modeling: dbt can work against many lakehouse engines (e.g., Trino, Spark)

Concrete pipeline example (orders analytics):
1) CDC brings postgres.public.orders into raw.orders_cdc
2) A daily job builds curated orders (dedupe by primary key, keep latest record)
3) A second job builds fact_orders joined with customers and products
4) A final job creates daily_revenue for dashboards

Key tip: treat curated tables as contracts. Add tests (unique keys, non-null constraints, freshness checks) so your lake doesn't become a swamp.

3) Add a query layer + governance so BI feels "warehouse-like"

To analysts, "no warehouse" should still feel like: open the BI tool, run SQL, get answers fast.

Use a query layer that can read your table format directly:

Trino/Presto for interactive SQL across Iceberg + other sources
Dremio for a user-friendly semantic layer and acceleration
ClickHouse or Pinot as optional serving layers for ultra-fast analytics on specific datasets

Governance essentials:

One catalog, clear schemas (raw, curated, marts)
Row/column-level security via the query engine or a governance tool
A data dictionary (even a simple docs site generated from dbt helps)

When performance becomes a concern, you have options without "adding a warehouse":

Optimize table layouts (partitioning, clustering/sorting)
Materialize aggregates into separate tables
Add caching/acceleration (Dremio reflections, Trino caching, or an OLAP store for hot metrics)

If you build around open storage + open tables + a flexible query layer, you get most warehouse benefits-while keeping your platform modular, portable, and easier to evolve.

Search This Blog

tylers-blogger-blog