Data Lakehouse

What is a Data Lakehouse?

A data lakehouse is a modern data architecture that combines the flexible, low-cost storage of data lakes with the structured management, performance, and reliability features of data warehouses. It stores data in open file formats (like Parquet) on cloud object storage while providing warehouse-like capabilities including ACID transactions, schema enforcement, data quality controls, and high-performance SQL queries directly on the stored files.

In B2B SaaS environments, the data lakehouse architecture solves a persistent challenge: teams need both the flexibility to store diverse data types—structured CRM records, semi-structured product event streams, unstructured support transcripts, and raw marketing automation logs—and the reliability to run mission-critical analytics, dashboards, and machine learning models on that data. Traditional data warehouses excel at structured analytics but become expensive and inflexible when storing raw, varied data. Data lakes provide cost-effective storage for all data types but lack the governance, performance, and reliability features required for production analytics.

The lakehouse architecture emerged as major technology platforms introduced open-source table formats (Delta Lake, Apache Iceberg, Apache Hudi) that add database-like capabilities to files stored in object storage. These formats enable features like transactions, time travel, schema evolution, and efficient updates/deletes on data lake storage—capabilities previously exclusive to data warehouses. For B2B SaaS organizations, this means a single platform can serve both exploratory data science workloads and production reporting dashboards, eliminating the complexity and duplication of maintaining separate data lake and warehouse environments.

Key Takeaways

• Unified architecture: Data lakehouses eliminate the need for separate data lake and warehouse systems, reducing complexity and data duplication across the analytics infrastructure

• Open format foundation: Built on open standards like Delta Lake, Iceberg, and Hudi rather than proprietary formats, preventing vendor lock-in and enabling tool interoperability

• ACID transactions: Provides database-grade consistency, isolation, and atomicity for data operations, ensuring reliable analytics even with concurrent reads and writes

• Cost efficiency: Separates storage (inexpensive object storage) from compute (pay-per-query processing), dramatically reducing costs compared to traditional data warehouses

• Performance optimization: Delivers warehouse-class query performance through features like data caching, indexing, partitioning, and adaptive query optimization

How It Works

The data lakehouse architecture operates through a layered system that adds structured data management capabilities on top of flexible cloud object storage.

At the foundation, data is stored in open file formats (typically Parquet or ORC) on cloud object storage services like AWS S3, Azure Data Lake Storage, or Google Cloud Storage. This storage layer is extremely cost-effective—roughly 10-20x cheaper per terabyte than traditional data warehouse storage—and supports unlimited scale. The raw data files contain structured tables (like CRM opportunities), semi-structured logs (like product event streams), and even unstructured content (like support chat transcripts).

The critical innovation comes from the metadata layer built on open table formats like Delta Lake, Apache Iceberg, or Apache Hudi. This layer maintains a transactional log that tracks which files comprise each table, enforces schema definitions, manages concurrent access, and enables ACID transactions. When a query requests data, the metadata layer determines which underlying files contain relevant data, ensuring consistency even when multiple users are simultaneously reading and writing.

The compute layer consists of query engines—such as Apache Spark, Presto, Trino, or platform-specific engines like Databricks SQL or AWS Athena—that read metadata to understand table structure, execute optimized queries against the underlying files, and return results. Because compute is separated from storage, organizations scale processing power independently based on workload demands and only pay for actual query execution time rather than maintaining always-on warehouse compute.

The lakehouse implements performance optimizations that historically only existed in data warehouses. Data is partitioned by commonly-queried dimensions (like date or account_id) so queries scan only relevant files. Z-ordering and clustering techniques co-locate related data within files for faster retrieval. Statistics and bloom filters enable query engines to skip irrelevant files entirely. Result caching stores frequent query outputs for instant retrieval. These optimizations deliver query performance approaching traditional data warehouses while maintaining the lakehouse's cost and flexibility advantages.

Finally, the lakehouse provides governance and quality controls through schema enforcement, constraint validation, column-level access controls, and audit logging. Data teams define expected schemas that reject malformed data, implement quality checks that prevent bad data from corrupting tables, control which users can access sensitive columns, and track all data access for compliance and security purposes.

Key Features

• Open table formats: Uses Delta Lake, Apache Iceberg, or Apache Hudi for ACID transactions, schema enforcement, and time travel capabilities on object storage

• Schema evolution: Supports adding, removing, or modifying columns without rewriting entire tables or breaking downstream consumers

• Time travel: Enables querying historical table versions, recovering deleted data, and reproducing past analyses for audit purposes

• Incremental processing: Efficiently processes only changed data through merge operations, streaming ingestion, and change data capture integration

• Multi-format support: Handles structured, semi-structured, and unstructured data in a single platform without forcing schema-on-write

• BI tool compatibility: Provides standard SQL interfaces and ODBC/JDBC connectivity for seamless integration with Tableau, Looker, PowerBI, and other analytics tools

• Data quality enforcement: Implements validation rules, constraints, and expectations that prevent bad data from entering trusted tables

Use Cases

Use Case 1: Unified GTM Analytics Platform

A B2B SaaS company migrates from a fragmented architecture—where structured CRM data lived in Snowflake, product event streams landed in S3, and machine learning features resided in a separate feature store—to a unified data lakehouse on Databricks. The lakehouse stores all data types in Delta Lake format: structured opportunity and account records, semi-structured product telemetry events, unstructured customer support transcripts, and pre-computed ML features. Marketing operations queries the same tables that data science uses for churn prediction models, eliminating data duplication and ensuring everyone analyzes consistent data. The company reduces infrastructure costs by 60% through object storage economics while improving query performance by 3x through Delta Lake's optimizations.

Use Case 2: Real-Time Customer 360 with Historical Analysis

A revenue operations team builds a customer 360 view in a lakehouse that combines real-time product usage streams with historical CRM and support data. Product events stream into Delta Lake tables using structured streaming, making usage signals available for queries within seconds. Meanwhile, batch pipelines land CRM opportunity history, support ticket archives, and billing records into the same lakehouse nightly. Analysts build dashboards showing both real-time product engagement and long-term customer relationship history without moving data between systems. The lakehouse's time travel capabilities enable the team to reproduce historical analyses exactly as they appeared at quarter-end for audit purposes, while schema evolution allows adding new event types without disrupting existing queries.

Use Case 3: Data Science Experimentation with Production Reliability

A data science team experiments with multiple machine learning approaches for lead scoring using a lakehouse architecture. Scientists access raw CRM extracts, enriched firmographic data, behavioral event streams, and intent signals directly from lakehouse tables, joining and transforming data freely for model development. Once a model proves effective, the same lakehouse serves production scoring—the data engineering team creates curated, quality-controlled tables with ACID guarantees that feed nightly scoring pipelines populating the CRM. This eliminates the traditional friction of moving model logic from experimental data science environments to production-grade systems, as both operate on the same reliable lakehouse foundation.

Implementation Example

Organizations implement data lakehouse architectures through carefully designed layers and governance frameworks:

Data Lakehouse Architecture Layers

B2B SaaS Data Lakehouse Architecture
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

┌─────────────────────────────────────────────────────────────────────┐
│                      CONSUMPTION LAYER                               │
│  ┌──────────┐  ┌──────────┐  ┌──────────┐  ┌──────────┐           │
│  │ Tableau  │  │  Looker  │  │ Jupyter  │  │  Python  │           │
│  │   BI     │  │  Dashb.  │  │ Notebooks│  │  Scripts │           │
│  └──────────┘  └──────────┘  └──────────┘  └──────────┘           │
└─────────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────────┐
│                       PROCESSING LAYER                               │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │  Query Engines: Spark SQL, Presto, Trino, Athena, DeltaLake │   │
│  │  • Query optimization    • Result caching                    │   │
│  │  • Distributed compute   • Access control                    │   │
│  └─────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────────┐
│                      METADATA LAYER                                  │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │  Table Format: Delta Lake / Iceberg / Hudi                   │   │
│  │  • Transaction log    • Schema registry                      │   │
│  │  • File tracking      • Partition metadata                   │   │
│  │  • Time travel        • Compaction management                │   │
│  └─────────────────────────────────────────────────────────────┘   │
└─────────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────────┐
│                 DATA ORGANIZATION LAYER                              │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐             │
│  │   GOLD       │  │   SILVER     │  │    BRONZE    │             │
│  │  Curated     │  │  Cleaned &   │  │   Raw/Land   │             │
│  │  Business    │  │  Validated   │  │   Ingested   │             │
│  │  Analytics   │  │  Enriched    │  │   Data       │             │
│  └──────────────┘  └──────────────┘  └──────────────┘             │
└─────────────────────────────────────────────────────────────────────┘
                               │
                               ▼
┌─────────────────────────────────────────────────────────────────────┐
│                      STORAGE LAYER                                   │
│  Cloud Object Storage: S3 / Azure Blob / GCS                        │
│  • Parquet files      • Partitioned by date, account, etc           │
│  • Compressed         • Open formats (no vendor lock-in)            │
│  • Versioned          • Cost-optimized tiers                        │
└─────────────────────────────────────────────────────────────────────┘

Data Lakehouse vs Traditional Architectures

Medallion Architecture for Lakehouse Data Organization

B2B SaaS companies typically organize lakehouse data using the medallion architecture:

Sample Delta Lake Table Definition

-- Create a Delta Lake table for CRM opportunities in the Silver layer
CREATE TABLE silver_opportunities (
  opportunity_id STRING,
  account_id STRING,
  account_name STRING,
  opportunity_name STRING,
  stage_name STRING,
  amount DECIMAL(15,2),
  close_date DATE,
  probability INT,
  created_date TIMESTAMP,
  last_modified_date TIMESTAMP,
  lead_source STRING,
  owner_id STRING,
  is_closed BOOLEAN,
  is_won BOOLEAN,

  -- Metadata fields
  ingestion_timestamp TIMESTAMP,
  source_system STRING
)
USING DELTA
PARTITIONED BY (DATE_TRUNC('month', close_date))
LOCATION 's3://company-lakehouse/silver/opportunities/'
TBLPROPERTIES (
  'delta.autoOptimize.optimizeWrite' = 'true',
  'delta.autoOptimize.autoCompact' = 'true',
  'delta.deletedFileRetentionDuration' = '30 days',
  'delta.enableChangeDataFeed' = 'true'
);

-- Add table constraints for data quality
ALTER TABLE silver_opportunities
ADD CONSTRAINT valid_amount CHECK (amount >= 0);

ALTER TABLE silver_opportunities
ADD CONSTRAINT valid_probability CHECK (probability BETWEEN 0 AND 100);

-- Merge new/updated records from Bronze layer (incremental processing)
MERGE INTO silver_opportunities AS target
USING (
  SELECT DISTINCT *
  FROM bronze_salesforce_opportunities_raw
  WHERE ingestion_date = CURRENT_DATE
  QUALIFY ROW_NUMBER() OVER (PARTITION BY opportunity_id ORDER BY last_modified_date DESC) = 1
) AS source
ON target.opportunity_id = source.opportunity_id
WHEN MATCHED AND source.last_modified_date > target.last_modified_date THEN
  UPDATE SET *
WHEN NOT MATCHED THEN
  INSERT *;

-- Query historical version (time travel) - reproduce last quarter's pipeline report
SELECT stage_name, COUNT(*) as opp_count, SUM(amount) as total_value
FROM silver_opportunities VERSION AS OF '2025-12-31'
WHERE is_closed = false
GROUP BY stage_name;

Related Terms

• Data Lake: Flexible storage repository for raw, unstructured, and semi-structured data in native formats

• Data Warehouse: Structured repository optimized for analytics with schema enforcement and query performance

• ETL: Extract, Transform, Load process for moving data from sources to warehouses with transformation applied first

• ELT: Extract, Load, Transform architecture where transformation occurs after loading into the destination

• Data Pipeline: Automated workflows for moving, transforming, and loading data across systems

• Modern Data Stack: Cloud-native data architecture combining ingestion, storage, transformation, and visualization tools

• Data Transformation: Process of converting raw data into analytics-ready formats

• Data Governance: Policies and processes ensuring data quality, security, and compliance

Frequently Asked Questions

What is a data lakehouse?

Quick Answer: A data lakehouse is a modern data architecture that combines the flexible, low-cost storage of data lakes with the structured management, performance, and ACID transaction capabilities of data warehouses.

Data lakehouses store data in open file formats on cloud object storage while providing warehouse-like features including transactional consistency, schema enforcement, data quality controls, and high-performance SQL queries. This architecture eliminates the need for separate data lake and warehouse systems, reducing complexity and cost while supporting diverse workloads from exploratory data science to production business intelligence. The lakehouse is built on open table formats like Delta Lake, Apache Iceberg, or Apache Hudi that add database capabilities to files in object storage.

How does a data lakehouse differ from a data warehouse?

Quick Answer: Data lakehouses store data in open formats on low-cost object storage with separated compute, while data warehouses use proprietary formats with tightly coupled storage and compute, resulting in significantly different cost structures and flexibility.

Data warehouses excel at structured analytics but require expensive, always-on compute resources and proprietary storage that can cost 10-20x more per terabyte than object storage. They typically handle only structured data with predefined schemas. Data lakehouses provide similar query performance and ACID guarantees while storing data in open formats like Parquet on S3 or Azure Blob Storage, dramatically reducing costs. Lakehouses support structured, semi-structured, and unstructured data, enable time travel to historical versions, and allow multiple processing engines to access the same data. According to Databricks research, organizations migrating from traditional warehouses to lakehouses report 40-60% cost reductions while gaining flexibility for data science and machine learning workloads.

What are the benefits of a data lakehouse architecture?

Quick Answer: Data lakehouses provide unified storage for all data types, dramatically lower costs through object storage and separated compute, eliminate data duplication between lakes and warehouses, and support both BI and data science workloads on the same platform.

Key benefits include cost efficiency through 10-20x cheaper storage and pay-per-query compute models versus always-on warehouses, architectural simplicity by eliminating separate lake and warehouse systems that require data synchronization, flexibility to handle structured, semi-structured, and unstructured data without forcing early schema decisions, and performance approaching traditional warehouses through optimizations like caching, partitioning, and Z-ordering. Additionally, lakehouses provide open format portability preventing vendor lock-in, ACID transactions ensuring reliable analytics, and time travel capabilities for historical analysis and audit purposes. For B2B SaaS companies, this means a single platform serving marketing analytics, product intelligence, customer success reporting, and data science experimentation.

What technologies enable data lakehouse architectures?

Data lakehouses rely on several key technology components. Open table formats—Delta Lake (developed by Databricks), Apache Iceberg (originally from Netflix), and Apache Hudi (from Uber)—provide ACID transactions, schema enforcement, and metadata management on top of object storage. Cloud object storage services (AWS S3, Azure Data Lake Storage, Google Cloud Storage) offer low-cost, scalable storage for data files. Query engines like Apache Spark, Presto, Trino, AWS Athena, and vendor-specific engines execute SQL queries against lakehouse tables. Metadata catalogs track table schemas, partitions, and file locations. Orchestration platforms coordinate data ingestion and transformation workflows. Major platform vendors including Databricks, Snowflake (with external tables), and Dremio provide integrated lakehouse solutions combining these components, while organizations can also assemble open-source components independently for maximum flexibility.

When should a B2B SaaS company choose a lakehouse over a traditional warehouse?

B2B SaaS companies benefit from lakehouse architectures when they need to store diverse data types (structured CRM data, semi-structured product events, unstructured support transcripts), want to reduce infrastructure costs significantly through object storage economics, require both business intelligence and data science capabilities on the same platform, prioritize avoiding vendor lock-in through open formats, or need sophisticated time travel and versioning for compliance and audit purposes. Companies should consider traditional warehouses when their workloads consist almost entirely of structured SQL analytics with limited data science needs, they already have substantial warehouse investments and expertise, or they require guaranteed sub-second query latency for thousands of concurrent users. Many organizations adopt hybrid approaches—using warehouses for high-concurrency BI workloads while maintaining lakehouses for broader data science, machine learning, and cost-optimized analytics. Gartner's data management research indicates that 70% of enterprises will have adopted some form of lakehouse architecture by 2025.

Conclusion

The data lakehouse represents a significant evolution in data architecture, resolving the long-standing tension between data lakes' flexibility and cost efficiency and data warehouses' reliability and performance. For B2B SaaS organizations managing increasingly diverse data—from structured CRM records to real-time product telemetry to unstructured customer interactions—the lakehouse provides a unified platform that supports all analytics and data science workloads without architectural compromises or expensive data duplication.

Revenue operations teams benefit from lakehouse architectures through consolidated pipeline and revenue analytics that combine data warehouse-grade reliability with the flexibility to incorporate new data sources rapidly. Marketing analytics professionals leverage lakehouses to analyze campaign performance alongside raw behavioral event streams and third-party intent signals. Data science teams experiment freely with diverse data types while deploying production ML models on the same reliable foundation. Engineering teams reduce operational complexity by eliminating separate lake and warehouse systems that require constant synchronization and duplicate storage costs.

As cloud data platforms mature and open table formats continue advancing, the data lakehouse is becoming the default architecture for data-forward B2B SaaS companies. Organizations investing in lakehouse capabilities position themselves to leverage data strategically—supporting immediate business intelligence needs while building foundations for advanced analytics and AI applications. Understanding how lakehouses relate to ELT pipelines, data transformation workflows, and modern data stack components enables teams to architect scalable, cost-effective data platforms that accelerate insight generation and competitive differentiation.

Last Updated: January 18, 2026