What is Data Lakehouse?

Definition of Data Lakehouse

Data Lakehouse is a modern data architecture combining the advantages of data lakes and data warehouses in a single, cohesive system. This architecture enables storing raw data in open formats while providing functionality typical of data warehouses, such as ACID transactions, schema management, and high analytical query performance. Data Lakehouse eliminates the need to maintain separate systems for different types of analytical workloads.

The concept was formally introduced by Databricks in 2020, though the underlying ideas had been evolving for several years. The core premise is straightforward: rather than operating two separate systems and copying data between them, a single platform can serve both the flexible storage needs of a data lake and the performance and governance requirements of a data warehouse. This simplification reduces costs, eliminates error-prone data movement, and accelerates time-to-insight.

Evolution from Data Lake and Data Warehouse

Data Lakehouse emerged as a response to the limitations of earlier architectures. Traditional data warehouses offer high performance and reliability but are expensive and limited to structured data. Data lakes allow cheap storage of any data but lack governance, query performance, and transaction support - leading to so-called data swamps where data becomes unusable.

The evolution can be traced through three generations:

Generation	Period	Architecture	Strengths	Weaknesses
1st	1990s-2010	Data Warehouse	High performance, ACID, SQL	Expensive, structured data only
2nd	2010-2020	Data Lake	Cost-effective, all data types	No governance, poor query performance
3rd	2020+	Data Lakehouse	Combines both advantages	Requires new skill sets

Typical enterprise architecture combined both systems in a two-tier approach, requiring costly data replication and complex ETL pipelines. Organizations often maintained a data lake for raw storage and data science, plus a data warehouse for BI and reporting. Data Lakehouse eliminates this redundancy, offering a single source of truth for all analytical workloads.

Key Data Lakehouse Technologies

The realization of Data Lakehouse architecture became possible thanks to the development of open table formats. These formats add a metadata layer on top of raw files in object storage, enabling warehouse-like capabilities:

Delta Lake, created by Databricks, introduces a transactional layer over Parquet files, providing ACID transactions, time travel (access to historical data versions), schema evolution, and schema enforcement. Delta Lake is tightly integrated with the Databricks ecosystem but also supported by other engines.

Apache Iceberg, originally developed by Netflix, offers similar functionality with emphasis on scalability and neutrality toward compute engines. Iceberg excels at partition evolution, hidden partitioning, and metadata management for extremely large tables. It has gained significant momentum in recent years with support from major cloud providers.

Apache Hudi (Hadoop Upserts Deletes and Incrementals) specializes in efficient upsert operations and incremental processing. It is particularly well-suited for use cases involving frequent data updates, such as Change Data Capture (CDC) from operational databases.

All these formats work on cheap object storage (S3, ADLS, GCS) and are supported by various compute engines, avoiding vendor lock-in.

Data Lakehouse Architecture and Components

A typical Data Lakehouse architecture consists of several layers, each serving a specific purpose:

Storage Layer: Built on cost-effective cloud object storage (Amazon S3, Azure Data Lake Storage, Google Cloud Storage), storing data in open formats like Parquet or ORC. The separation of storage and compute enables independent scaling and cost optimization.

Metadata Layer: Open table formats (Delta Lake, Iceberg, Hudi) manage transactions, schema, change history, and statistics. This layer is the heart of the lakehouse - it transforms simple object storage into a transactional data system.

Compute Layer: Different engines can be deployed based on use case:

• Apache Spark for batch and streaming processing
• Presto/Trino for interactive SQL queries
• Databricks SQL for optimized warehouse workloads
• Dremio for data lake queries
• Snowflake with native Iceberg support

Governance Layer: Provides data cataloging, access control, data lineage, and auditing. Tools like Unity Catalog (Databricks), Apache Atlas, or AWS Glue Data Catalog play central roles here.

Data Access Layer: APIs, JDBC/ODBC connectors, and SQL interfaces enable access for BI tools, notebooks, and applications.

This modular architecture allows independent scaling and optimization of each layer.

Medallion Architecture in the Lakehouse

A widely adopted pattern within the lakehouse is the Medallion Architecture (also called multi-hop architecture), which organizes data into three quality tiers:

• Bronze (Raw): Raw data is ingested unchanged from source systems. This layer serves as a complete archive and single source of truth for source data.
• Silver (Cleaned): Data is cleaned, deduplicated, validated, and conformed to a consistent schema. Business rules are applied, and data from different sources is joined.
• Gold (Business-Level): Aggregated, business-oriented datasets optimized for specific use cases such as reporting, dashboards, or ML models.

This pattern provides traceability, simplifies debugging, and enables data reprocessing when business logic changes.

Data Lakehouse Use Cases

Data Lakehouse works well across a wide spectrum of analytical applications:

• Business Intelligence and Reporting: Efficient SQL queries and seamless integration with BI tools (Tableau, Power BI, Looker) enable real-time dashboards and self-service analytics.
• Data Science and Machine Learning: Data scientists can work directly on data in the lakehouse without the need to copy it to separate environments. Feature stores and ML pipelines integrate natively.
• Stream Processing: Lambda and kappa architectures can be built using the same tables for batch and streaming, significantly reducing complexity.
• Real-time Analytics: Incremental data refresh capabilities enable near-real-time insights without full recomputation.
• Archiving and Compliance: Time travel enables access to historical data states and supports meeting regulatory requirements such as GDPR or industry-specific regulations.
• Data Sharing: Open formats facilitate secure data exchange between organizations and departments.

Business Benefits and ROI

Data Lakehouse adoption brings measurable business benefits to organizations:

Cost Reduction: Eliminating data duplication between data lake and warehouse, combined with cost-effective cloud storage, can reduce total cost of ownership by 30-50%. The separation of storage and compute enables pay-as-you-go scaling.

Accelerated Time-to-Insight: Architecture simplification and elimination of complex ETL pipelines significantly shorten the path from data ingestion to analysis. New data sources can be integrated faster.

Data Democratization: Different teams - analysts, data scientists, ML engineers - can work on the same data without relying on separate copies, reducing inconsistencies and enabling collaboration.

Reduced Complexity: A single platform instead of two or more separate systems simplifies operations, monitoring, and governance considerably.

ARDURA Consulting supports organizations in acquiring data engineering specialists with experience in Data Lakehouse technologies who can design and implement modern data architecture tailored to specific business needs.

Challenges in Adoption

Despite the numerous advantages, Data Lakehouse adoption comes with challenges that organizations should prepare for:

• Skills gap: Teams need expertise in both data engineering and warehouse concepts
• Technology selection: Choosing between Delta Lake, Iceberg, and Hudi requires careful evaluation based on existing ecosystem and use cases
• Legacy migration: Transitioning from existing architectures requires a well-planned migration strategy with minimal disruption
• Performance tuning: Optimizing queries on object storage requires specific knowledge in areas like partitioning, Z-ordering, and file compaction
• Cross-engine governance: Implementing effective access control and cataloging across different compute engines can be complex

Summary

Data Lakehouse represents the next generation of data architectures, combining data lake flexibility with data warehouse reliability. Thanks to open table formats like Delta Lake, Apache Iceberg, and Apache Hudi, along with modular architecture, organizations can build scalable, cost-effective analytical platforms without vendor lock-in. The Medallion Architecture provides a proven pattern for progressively refining raw data into business-critical insights. ARDURA Consulting offers access to experts helping with migration to Data Lakehouse architecture and maximizing value from data investments.