Comparing Data Architectures: From Traditional Warehouses to Modern Data Platforms
- Rhys Hanscombe
- 4 days ago
- 3 min read
Choosing the right data architecture is crucial for handling the complexities of modern data ecosystems. From traditional warehouses to real-time streaming and decentralized models, each approach has unique strengths. In this blog, we compare key architectures based on criteria such as design principles, scalability, agility, auditability, and programming languages.
1. Traditional Data Warehousing Architectures
Operational Data Store (ODS)
Date: Introduced in the 1990s
Key People: Bill Inmon
Design Principle: Consolidates transactional data from multiple sources for operational reporting
Scaling: Moderate, not designed for large-scale analytics
Agility: Low, primarily supports structured data
Auditability: Basic, relies on transactional system logs
Preserves Raw Data: No, transforms data for operational use
Layers: Single-layer storage
Programming Language(s): SQL, ETL tools
Star Schema
Date: 1990s
Key People: Ralph Kimball
Design Principle: Dimensional modeling with fact and dimension tables for efficient querying
Scaling: Scales well for structured data, limited for unstructured data
Agility: Low, requires predefined schemas
Auditability: Moderate, historical tracking through Slowly Changing Dimensions (SCDs)
Preserves Raw Data: No, structured for reporting
Layers: Fact and dimension tables
Programming Language(s): SQL, ETL tools
Corporate Information Factory (CIF)
Date: 1990s
Key People: Bill Inmon
Design Principle: Centralized enterprise data warehouse for structured analytics
Scaling: High for structured data
Agility: Low, requires rigid schema design
Auditability: Strong, supports historical data tracking
Preserves Raw Data: No, data is transformed for analysis
Layers: Data warehouse, data marts
Programming Language(s): SQL, ETL tools
2. Modern Big Data and Streaming Architectures
Lambda Architecture
Date: Early 2010s
Key People: Nathan Marz
Design Principle: Combines batch processing with real-time streaming
Scaling: High, supports large-scale data processing
Agility: Moderate, requires managing two separate pipelines
Auditability: High, maintains both real-time and historical views
Preserves Raw Data: Yes
Layers: Batch layer, speed layer, serving layer
Programming Language(s): Java, Scala, Python, Apache Spark, Kafka, Hadoop
Kappa Architecture
Date: Mid-2010s
Key People: Jay Kreps
Design Principle: Streaming-first architecture using a single data pipeline
Scaling: High, designed for real-time processing
Agility: High, eliminates batch complexity
Auditability: High, maintains event logs
Preserves Raw Data: Yes
Layers: Single streaming layer
Programming Language(s): Java, Scala, Python, Apache Kafka, Flink
3. Data Lakes and Hybrid Storage Models
Data Lake
Date: 2010s
Key People: James Dixon
Design Principle: Store raw, structured, and unstructured data in a central repository
Scaling: Extremely high
Agility: High, schema-on-read flexibility
Auditability: Low, unless enhanced with governance tools
Preserves Raw Data: Yes
Layers: Single-layer, raw data
Programming Language(s): Python, Spark, SQL, Hadoop, AWS S3, Azure Data Lake
Data Lakehouse
Date: 2020s
Key People: Databricks
Design Principle: Combines data lakes’ scalability with data warehouses’ structured querying
Scaling: High
Agility: High, supports structured and unstructured data
Auditability: High, integrates governance and metadata management
Preserves Raw Data: Yes
Layers: Data lake, warehouse layer
Programming Language(s): SQL, Python, Spark, Delta Lake
Delta Lake
Date: 2019
Key People: Databricks
Design Principle: ACID transactions and schema enforcement for data lakes
Scaling: High
Agility: High, supports batch and streaming workloads
Auditability: High, maintains transaction logs
Preserves Raw Data: Yes
Layers: Raw, cleaned, refined
Programming Language(s): SQL, Python, Spark
4. Architecture Frameworks for Modern Data Management
Date: 2020s
Design Principle: Layered data refinement (Bronze, Silver, Gold)
Scaling: High
Agility: High, supports structured and unstructured data
Auditability: High, clear lineage between layers
Preserves Raw Data: Yes
Layers: Bronze (raw), Silver (cleaned), Gold (business-ready)
Programming Language(s): SQL, Python, Spark
Data Mesh
Date: 2019
Key People: Zhamak Dehghani
Design Principle: Decentralized data ownership with domain-based architecture
Scaling: Extremely high, suited for large organizations
Agility: High, teams control their data domains
Auditability: Moderate, relies on domain governance
Preserves Raw Data: Depends on implementation
Layers: Multiple domain-driven layers
Programming Language(s): SQL, Python, Spark, API-driven architectures
Date: 2000s
Key People: Dan Linstedt
Design Principle: Scalable modeling for historical tracking with hubs, links, satellites
Scaling: High, optimized for data warehouse automation
Agility: High, supports changes without schema redesign
Auditability: Extremely high, maintains historical changes
Preserves Raw Data: Yes
Layers: Hub (business keys), Link (relationships), Satellite (context)
Programming Language(s): SQL, ETL tools, dbt
Conclusion
Selecting the right architecture depends on your organization’s needs. Traditional data warehouses are structured and reliable, while modern architectures like Data Mesh and Data Vault offer scalability and agility. Hybrid models like Data Lakehouse and Delta Lake provide a balance between flexibility and governance.
Need help implementing Data Vault in your organization? Contact us today!