Data Lakehouse Analytics: Combining Data Lake Flexibility with Warehouse Reliability

A data lakehouse is an architecture that combines the low-cost, flexible storage of data lakes with the performance, reliability, and governance features of data warehouses. Rather than maintaining separate systems for different workloads, lakehouses provide a unified platform for business intelligence, data science, and real-time analytics.

The lakehouse architecture emerged from the realization that organizations shouldn't have to choose between flexibility and reliability.

The Problem Lakehouses Solve

The Two-Tier Problem

Traditional architectures maintain separate systems:

Data lakes store raw data:

• Cheap object storage
• Any format or schema
• Flexible for data science
• But: unreliable, slow queries, no governance

Data warehouses serve analytics:

• Fast SQL queries
• ACID transactions
• Strong governance
• But: expensive, structured data only, limited data science support

Organizations copy data between systems, creating cost, latency, and complexity.

Data Swamp Reality

Many data lakes become data swamps:

• No schema enforcement
• Quality degrades over time
• Nobody knows what data means
• Queries are slow and unreliable

The lake's flexibility becomes a liability.

Cost Duplication

Maintaining two systems costs twice:

• Duplicate storage for the same data
• Duplicate compute for ETL between systems
• Duplicate tooling and skills
• Duplicate governance overhead

Organizations pay for architectural compromise.

Lakehouse Architecture

Open Storage Layer

Data lives in open formats on object storage:

Object storage: S3, GCS, Azure Blob - cheap, durable, scalable.

Open file formats: Parquet provides efficient columnar storage.

No vendor lock-in: Data readable by any compatible tool.

Open storage provides flexibility and cost efficiency.

Table Format Layer

Metadata layers add reliability:

Transaction logs: Track changes with ACID guarantees.

Schema evolution: Manage schema changes gracefully.

Time travel: Query historical versions of data.

Compaction: Optimize file layouts for performance.

Delta Lake, Apache Iceberg, and Apache Hudi provide these capabilities.

Query Engine Layer

Engines execute analytics workloads:

SQL engines: Query data using familiar SQL syntax.

Distributed compute: Scale across clusters for performance.

Optimization: Query planning and execution optimization.

Multiple engines can access the same data.

Governance Layer

Control and catalog data assets:

Access control: Row and column level security.

Audit logging: Track who accessed what.

Data catalog: Discover and understand data assets.

Lineage: Track data flow and dependencies.

Governance enables trustworthy self-service.

Lakehouse Benefits

Unified Analytics

One platform for all workloads:

Business intelligence: Fast SQL queries for dashboards and reports.

Data science: Direct access to data for ML and experimentation.

Real-time analytics: Streaming data alongside batch.

Ad-hoc exploration: Interactive queries without data movement.

Codd Integrations connect semantic layers to lakehouse architectures, enabling business context and governance on top of unified storage.

Cost Efficiency

Reduce total cost of ownership:

Cheap storage: Object storage costs fraction of warehouse storage.

No duplication: Single copy serves all workloads.

Elastic compute: Scale compute independently of storage.

Open formats: Avoid vendor-specific premiums.

Cost savings can be substantial for large data volumes.

Reduced Complexity

Simpler architecture to operate:

• One storage system instead of two
• No ETL between lake and warehouse
• Unified governance and security
• Consistent data across workloads

Simplicity reduces operational burden.

Data Science Enablement

Better support for ML workloads:

Direct access: Data scientists access production data directly.

Large datasets: Handle training data at any scale.

Feature storage: Serve features for ML models.

Experiment tracking: Version datasets alongside models.

Lakehouses remove friction from ML workflows.

Implementing Lakehouse Architecture

Choose Table Format

Select your open table format:

Delta Lake: Tight Databricks integration, mature ecosystem.

Apache Iceberg: Cloud-native, strong catalog support.

Apache Hudi: Strong streaming and CDC support.

Consider ecosystem, cloud provider support, and existing investments.

Design Storage Layout

Organize data effectively:

Bronze layer: Raw data as ingested, preserving source fidelity.

Silver layer: Cleaned, validated, integrated data.

Gold layer: Business-ready aggregations and marts.

Medallion architecture provides structure.

Select Query Engines

Choose engines for your workloads:

Databricks: Full-featured, tight Delta Lake integration.

Spark: Open source, flexible, broad ecosystem.

Trino/Presto: Fast interactive queries.

Warehouse engines: Snowflake, BigQuery support lakehouse integration.

Match engines to workload requirements.

Establish Governance

Control access and quality:

• Define access policies by role and data classification
• Implement catalog for discovery
• Track lineage across transformations
• Monitor quality continuously

Governance prevents data swamps.

Enable Self-Service

Let users access data appropriately:

• Discovery through catalogs
• SQL access for analysts
• DataFrame access for data scientists
• Proper training and documentation

Self-service maximizes value from lakehouse investment.

Lakehouse Use Cases

Unified BI and Data Science

One platform serves both communities:

• Analysts query curated tables via SQL
• Data scientists access raw and processed data
• Both work on the same underlying storage
• No data movement or synchronization needed

Unification enables collaboration.

Streaming and Batch Analytics

Combine real-time and historical data:

• Stream events into lakehouse tables
• Query real-time and historical data together
• Unified processing for both workloads
• Consistent semantics across time windows

Streaming adds real-time capabilities.

Machine Learning Features

Store and serve ML features:

• Compute features from lakehouse data
• Version feature datasets
• Serve features for training and inference
• Track feature lineage

Lakehouses become feature platforms.

Cost Optimization

Migrate from expensive warehouses:

• Move cold data to lakehouse storage
• Query across warehouse and lakehouse
• Gradually migrate workloads
• Reduce warehouse spend

Hybrid approaches provide transition path.

Lakehouse Challenges

Maturity

Lakehouse technology is younger than warehouses:

• Fewer best practices documented
• Tooling still evolving
• Skills less common
• Edge cases less understood

Expect some pioneering effort.

Performance Tuning

Getting good performance requires work:

• File sizing and compaction
• Partition strategies
• Statistics and indexes
• Query optimization

Performance doesn't come automatically.

Governance Complexity

Open formats complicate governance:

• Multiple access paths to control
• Catalog and format coordination
• Cross-engine policy enforcement
• Audit trail aggregation

Plan governance architecture carefully.

Skills Requirements

Teams need new skills:

• Distributed systems understanding
• Open format expertise
• Performance optimization
• Cloud infrastructure management

Invest in training and hiring.

Lakehouse and AI Analytics

Lakehouse architecture provides strong foundations for AI:

Training data access: ML models access large datasets efficiently.

Feature storage: Lakehouses serve as feature stores.

Model data requirements: AI often needs both structured and unstructured data.

Experimentation: Time travel enables reproducible experiments.

Cost efficiency: AI workloads can be expensive - lakehouse economics help.

Organizations building AI analytics capabilities find lakehouses provide the flexibility and scale that AI workloads demand while maintaining the reliability that production systems require.

Getting Started

Organizations considering lakehouse adoption should:

1. Assess workloads: What mix of BI, data science, and streaming do you have?
2. Evaluate current architecture: What exists today and what are its pain points?
3. Choose format and platform: Select table format and primary query engine
4. Start with new workloads: Pilot on new projects rather than migrating everything
5. Establish patterns: Define medallion architecture and governance early
6. Expand based on success: Migrate existing workloads as patterns mature

The lakehouse architecture represents a significant shift in how organizations think about data platforms, moving from specialized systems to unified platforms that serve all analytical needs.