One table.

Every workload

Collapse the warehouse and the lake into a single plane of truth. One query. One copy. One governance layer. Zero vendor lock-in.

4.2×

FASTER THAN WAREHOUSE-ONLY

TPC-DS BENCHMARK · 10TB SCALE FACTOR

MEASURED VS SNOWFLAKE, BIGQUERY, REDSHIFT

SCROLL

Twelve dimensions.
One clear winner.

We benchmarked Lakehouse against traditional warehouses, raw data lakes, and leading SaaS platforms across the dimensions that actually matter at 3 AM when a pipeline is down.

Dimension⬡ LakehouseWarehouseRaw LakeSnowflakeBigQuery
Cost per TB / month$18$230+$8$280+$200+
Query latency (p99)< 2s< 5s45–120s< 4s< 6s
ACID transactionsyesyesnoyes~ partial
Open file formatyesnoyesnono
Unified governanceyes~ partialno~ partial~ partial
ML / AI integrationnativeexternalmanualexternalexternal
Streaming ingestionyeslimitedyeslimitedyes
Time-travel (days)907–30none14–907
Data duplicationnonerequirednonerequiredrequired
Vendor lock-innonehighlowhighhigh
Self-hosted optionyesnoyesnono
Concurrent workloadsunlimited~ credit-gated~ cluster-limited~ credit-gated~ slot-limited

Numbers that don't
need a gradient.

All benchmarks run on identical hardware (96-core, 384GB RAM, NVMe) against each platform's recommended production configuration. Reproducible setup available on GitHub.

TPC-DS Query Throughput

QUERIES / HOUR AT 10TB

Higher is better. Standard decision-support benchmark across 99 queries.

Lakehouse4,200 q/hr
Snowflake (XL)1,980 q/hr
BigQuery1,540 q/hr
Redshift RA31,720 q/hr
Self-managed Spark980 q/hr

Cost per TB Processed

USD / TB

Lower is better. Includes compute + storage at steady-state production load.

▼ LOWER IS BETTER
Lakehouse$18
Snowflake (XL)$280
BigQuery$200
Redshift RA3$230
Databricks$195

P99 Query Latency

SECONDS (INTERACTIVE QUERIES)

Lower is better. Measured at 500 concurrent users, mixed read/write workload.

▼ LOWER IS BETTER
Lakehouse1.8s
Snowflake (XL)4.2s
BigQuery5.9s
Redshift RA37.1s
Raw S3 + Athena42s

* All benchmarks conducted Feb 2026 · TPC-DS is a trademark of the Transaction Processing Performance Council · Full methodology in downloadable report · Results may vary based on configuration

One copy.
Infinite perspectives.

Traditional architectures duplicate data between lake and warehouse, creating governance nightmares and runaway costs. Lakehouse stores data once in open Delta/Iceberg format, serving every workload from a single source of truth.

BEFORE: THE DUCT-TAPE PIPELINE
Kafka
PostgreSQL
S3 Events
ETL Pipeline (Spark / Glue) — 4 copies of data
Data Lake (raw S3)
ML Training Store
Data Warehouse (Redshift)
BI Cache (Tableau)
4× DATA COPIES · 6+ TEAMS · $240+/TB/MO
AFTER: THE LAKEHOUSE MODEL
Kafka
PostgreSQL
S3 Events
⬡ LAKEHOUSE STORAGE LAYER
Delta Lake / Apache Iceberg · One copy · ACID transactions
SQL Queries
ML / Python
BI Tools
Streaming
1× DATA COPY · UNIFIED GOVERNANCE · $18/TB/MO
DATA COPY
No duplication overhead
90d
TIME TRAVEL
Full version history
ACID
TRANSACTIONS
Serializable isolation
CONCURRENCY
No slot limits

The benchmark report
engineers forward.

42 pages of reproducible benchmarks, cost modeling spreadsheets, and migration runbooks. The data your CTO needs to sign off on the Snowflake migration. The proof your team needs to stop arguing about the architecture.

Full TPC-DS results at 1TB, 10TB, 100TB scale
Cost modeling spreadsheet (editable, your numbers)
Migration runbook: Redshift → Lakehouse in 4 weeks
Security & compliance checklist (SOC2, GDPR, HIPAA)
Architecture decision record (ADR) templates
STEP 1 OF 2 — WORK EMAIL

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