Two Architectures for Analytical Data

The data warehouse and the data lakehouse are both architectures for enabling analytical queries on large volumes of business data. Both provide SQL query interfaces, data governance, and support for BI tools and dashboards. But their underlying designs reflect fundamentally different philosophies about how analytical data infrastructure should be built.

Understanding the genuine trade-offs between these architectures is essential for making informed infrastructure decisions, rather than following industry trends without understanding their applicability to your specific context.

The Data Warehouse Model

A traditional data warehouse (Snowflake, Redshift, BigQuery) is a fully integrated, proprietary analytical database. Data is stored in the warehouse’s proprietary format (Snowflake’s Hybrid Columnar Storage, Redshift’s columnar format) in the warehouse’s managed infrastructure. The warehouse controls storage, compute, query optimization, metadata management, and access control as a unified, managed system.

The data warehouse model’s strengths: exceptional query performance (decades of optimization for SQL analytics), minimal operational overhead (fully managed, no infrastructure to operate), strong governance tooling (access control, auditing, data sharing), and a simple pricing model (pay for compute and storage, no infrastructure management).

The data warehouse model’s limitations: data is locked in the vendor’s proprietary format (migrating to a different warehouse requires re-ingesting all data), high costs at scale (proprietary storage pricing is significantly higher than object storage), limited interoperability with ML tools and Python analytics (most warehouses require exporting data for ML workloads), and vendor dependency for all platform capabilities.

The Data Lakehouse Model

A data lakehouse stores data in open formats (Apache Parquet via Iceberg) in commodity object storage (S3, ADLS, GCS) that the organization controls. Query engines (Dremio, Spark, Trino, Flink) are separate from storage and connect to the open data through standard protocols (Iceberg REST Catalog, Arrow Flight). Different engines can be used for different workload types: Flink for streaming ingestion, Spark for heavy batch transformation, Dremio for governed BI analytics.

The lakehouse model’s strengths: storage cost at object storage pricing (typically 5-10x cheaper than warehouse storage), complete data format openness (Parquet files can be read by any Iceberg-compatible engine, with no vendor lock-in), unified storage for all workload types (BI, ML, streaming analytics from the same data), and modular architecture (best-of-breed engines for each workload type).

The lakehouse model’s limitations: higher operational complexity (more components to manage and integrate), requires careful architecture to achieve warehouse-like governance and query performance, and requires investment in the technical expertise to operate the ecosystem.

When to Choose Each

Choose a data warehouse when: the team has limited data engineering expertise, operational simplicity is the top priority, data volumes are moderate (under ~10TB), all workloads are SQL analytics (no significant ML or streaming requirements), and cost predictability matters more than cost optimization.

Choose a data lakehouse when: data volumes are large and growing (object storage cost advantage compounds significantly at scale), ML and Python analytics workloads require direct data access without export, multi-engine flexibility is a strategic requirement, data format openness and vendor independence are organizational priorities, or existing data already resides in S3/ADLS that makes warehouse ingestion costs prohibitive.

Many mature organizations operate a hybrid model: a data lakehouse (Iceberg on S3, Dremio for query) as the primary analytical platform for large-scale and ML workloads, with a specialized warehouse (Snowflake or BigQuery) for specific high-performance BI use cases or legacy reporting that is too costly to migrate.

Learn More

To dive deeper into these architectures and master the modern data ecosystem, check out the comprehensive books by Alex Merced available in our Books section.