Physical data models form the backbone for database design – they provide the structured blueprint for translating business needs into optimized data storage and access.
With data volume, diversity and usage surging globally, sound physical modeling is no longer just an IT concern. It can directly impact corporate performance. This 2,800+ word guide examines what exactly physical data models entail and arm practitioners with expertise for navigating modern data environments.
Topics include:
- Physical Data Model Components and Database Mapping Specifics
- Step-By-Step Process for Effective Translation from Logical to Physical
- Storage, Security and Access Optimization Best Practices
- Comparison of Modeling Approaches Across Data Warehouse, OLAP and OLTP Systems
- Emerging Trends Shaping Data Architectures like Data Mesh, Advanced Analytics and Automation
- Overview of Leading Data Modeling Platforms, Resources and Certifications
Along the way, real-world case studies and data spotlight how prudent data modelers grow in value – enabling agile, scalable data architectures as a competitive advantage.
Physical data models describe how business data gets structured internally within database systems to align both utility and performance.
Key characteristics include:
- Outlines database schema – tables, columns, data types, keys
- Maps relationships between tables
- Configures storage, access and integrity mechanisms
Its role falls between earlier conceptual models centered on entities and attributes and actual database implementation.
Where conceptual models capture business objectives and logical ones formalize detailed metadata rules and lineage, physical concentrates on optimizing IT execution.
Physical Data Model Elements and Database Mapping
Physical models comprise a number of interconnected components that collectively realize a robust data management paradigm:
| Model Element | Database Construct | Purpose |
|---|---|---|
| Tables | Database Tables | Structures holding instance data |
| Columns | Table Columns | Fields housing individual attributes |
| Relationships | Foreign Keys | Links between related records |
| Data Types | SQL Data Types | Validates contents format and size |
| Keys | Primary + Foreign Keys | Uniquely IDs tuples + defines table relationships |
| Constraints | Check, Not Null | Quality rules enforcing integrity |
| Indexes | Db Index Structures | Improves read performance on selective columns |
| Storage | Files, memory, caches | Physical housing of data on disk + in memory |
| Views | Virtual Views | Provide different logical representation of data |
| Partitions | Table Partitions | Improve maintenance/query perforamcne |
| Transactions | Units of concurrency/consistency | Ensure ACID guarantees for reliability |
Well constructed models optimally map business needs to the innate capabilities across these database constructs – enabling security, quality and speed.
For example, correctly sized character data types prevent overflow yet inefficient excess capacity. Multi-column indexes carefully weigh selectivity against cost. Referential integrity prevents unauthorized, inconsistent state changes via constraints.
Meanwhile, partitioning large tables on date ranges improves read/write speeds while facilitated maintenance. Caching hot data in memory, column stores and materialized aggregates fast-track reporting queries.
Translating Logical to Physical: A 6 Step Process
Moving from conceptual understanding to physical realization involves methodically transitioning through levels of detail while adhering to rigorous data management principles:
Steps include:
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Leverage existing logical baseline: Conceptual and logical models supply foundational understanding of core entities – their meanings, interrelationships and attributes. This provides the business context.
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Map entities to tables: Translate logical model groups into physical tables – the structures holding actual instances in rows/columns.
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Embed attributes and data types: Logical attributes now become columns, with appropriate SQL data types declaring valid contents format, size etc.
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Relate tables via keys: Foreign key relationships get defined using column references to primary keys in related tables. This maintains data integrity across junctions.
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Add ancillary constructs: Additional elements like indexes, constraints, caching and partitions are configured for performance and quality needs.
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Continuously reassess/refine: Performance monitoring and usage patterns direct ongoing tweaks to storage, access paths and queries.
Adherence to consistent data modeling principles and formal DM metadata tracking ensures coherence across business and technical perspectives along the continuum from logical to physical realization.
Now with clearer understanding of what comprises physical data models in practice, we can explore guidelines and trends in applying them effectively.
While intricate in implementation, a set of core tenets serves to guide successful data model deployment:
Design Logically First
Since physical constructs flow from business needs, logical entity analysis always comes first. This means:
- Map key business processes and data flows
- Model entities/attributes accurately reflecting organizational activities
- Define formal data element meanings and lineage explicitly
This grounds physical database design in enterprise objectives – ensuring relevance and extendibility.
Start Simpler, Then Scale
Resist overengineering complexity upfront. Focus on current requirements. Seek patterns leveraging redundancy and normalization appropriately. This facilitates agility responding to shifting needs via modular design instead of unwieldy monoliths.
Standardize Definitions
Reuse data types, naming conventions, relationship diagrams and integrity rules consistently. This amplifies understandability and cohesion – especially crucial as systems and associated models grow more intricate.
Embrace Iteration
Recognize initial releases will balance tradeoffs under uncertainty of actual usage, limitations only evident in practice. Continually reassess and tweak config based on monitoring – storage, sizing, indexes, caching etc.
Verify Requirements to Capabilities Mapping
Catalogue use cases and access types, then ensure model structures map accordingly – OLTP queries need different indexing than warehouses. Point lookups demand alternate data arrangement from keyword searches or statistical modeling.
Keep Documentation Updated
Closely track details like structure changes, sizing rationale, mapping artifacts. This rapid comprehension for new team members amid staff churn, avoiding subtle erosion losing optimization context.
While more technology-focused, the same key tenets revolving around governance, quality and adaptability that underlie logical design hold true for physical efforts in order to extract full business value.
Next we move up a level to compare how modeling philosophies converge and diverge across analytical, operational and hybrid systems.
While sharing foundational practices around fidelity, reuse and documentation – table/column mapping, foreign keys etc. – core modeling principles diverge across analytics-focused data warehouses, transactional databases and contemporary hybrid architectures.
We can spotlight distinctions across three areas:
Information Life Cycle
OLTPs house current, consistent production data from applications and serve user entry/editing needs. Highly granular, ACID compliance means meticulous referential integrity and version histories:
- Many-to-many cardinalities
- Type 2 slowly changing dimensions
- Multi-active cluster scale-out
Conversely warehouses manage refined, integrated data for historical analysis. Requirements include flexibility, analytic speed and completeness within queried time windows:
- coarser, combo dimensions
- Type 1 SCD overwrite
- columnar storage
Modern hybrid designs seek to converge the two – enabling real-time decisioning by unifying streaming, contextual analysis with persisted master records in a governed pipeline.
Workloads and Access Patterns
Transactional systems contend with intense concurrent read/write mixes and point queries against current state data. Index, storage and integrity structures revolve around facilitating these usage needs for responsiveness.
Analytics instead shifts focus to bulk throughputs of reads against historic tie-series loaded from upstream having undergone ETL brushing. So schemes optimize for read performance over space using approaches like column stores, materialized views and cubes.
Hybrid models accommodate analyzing not just bulk persisted but also streaming recent or event-trigger-initiated data – applying ML models for context. So storage splits across tiers by age and usage while still maintaining consistency.
Data Modeling Tools and Languages
Logical modeling forms conceptual foundation while various approaches later tune physical realization.
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ERwin, Oracle SQL Developer Data Modeler and SAP PowerDesigner offer full life cycle logical through physical modeling with integrated DDL code generation capabilities.
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Meanwhile ETL tools like Informatica, Talend and Pentaho simplify building data pipelines mapping to physical schemas.
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MongoDB and other NoSQL visualize document database representations rather than pure relational mapping.
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Data vault-based methods model complex historical tracking across distributed data lakes
Now with better grasp of how physical modeling options fit analytc vs operational vs converged needs, we look at what future trends change the game.
Beyond long standing tenets around governance, reuse and communication, cutting edge technological shifts now also rapidly transform data modelers‘ toolkits and approaches:
Automating the Tedium
Tasks like configuring storage settings, testing indexes and assigning security metadata grow increasingly automated via artificial intelligence. Machine learning model-driven recommendations enhance metadata compilation, query optimization and object tracking as external tools start to move embedded.
Everything Gets Real-time
Integrating stream analysis requires tweaked models to erase separation between near line event capture, contextual augmentation and far line persistence. Schema adapt to house transient message payloads alongside master records.
Cloud Scale and Portability
Multi-cloud adoption emphasizes highly elastic designs abstracted from specific environments using containerization and infrastructure-as-code techniques to enhance reproducibility and delegation of routine provisioning.
Data Mesh Emergence
Data and analytical models distribute across the organization – assigned to domain owners closest to the capabilities needing insights under centralized data governance standards. This breaks down massive monoliths into interconnected domains that align usage.
Privacy and Ethics Centrality
Regulations globalize while public and employee scrutiny heightens. Tools for enterprise metadata management, data masking and de-identification integrate deeper into modeling and downstream pipelines.
Deeper Model Convergence
MLOps operationalizes analytical models for embedded ML predictions that can drive personalization, contextual augmentation and automated decisions at scale. The boundaries between serve, store and model blur under unified data lifecycle management.
These open, secure and intelligent trends make incorporating cutting edge capabilities essential for competitive differentiation through data – fused with anchoring modeling fundamentals.
While concepts and high level methodologies compose universal foundations, a rich array of platforms exists for codifying complex modern data environments. Complementing leaders like Erwin and SAP PowerDesigner, niche players target specific needs around metadata management, data lineage and model-driven autoscaling.
Cambrian Explosion Visualizes Hundreds of Data/Analytics Startups
On the skills front, research firm Techstrong research forecasts over 90,000 new data modeling jobs annually needing filling over the next five years. Events like Enterprise Data World, DAMA CDMP certification and marketplace training sites like Udemy seek to expand practitioner ranks through education.
For self-starters, canonical books like Len Silverton‘s The Data Model Resource Book and The Data Warehouse Toolkit offer extensively detailed references on sound modeling techniques for transactional normalization and enterprise system design respectively.
To ground these concepts in visible form, we will walk through regional financial provider Banorte’s data modeling overhaul. Supporting operations across 1,150 branches, 20 million customers and $100 billion in assets, legacy infrastructure strained under product innovation pressures.
Siloed data and nationally distributed analytics teams impeded rapid, integrated insights. So CDO Emmanuel García Granados spearheaded adopting next generation cloud data mesh architecture.
Phased Pathway to Data Mesh Implementation
Their phased migration, completed mid-2022, involved:
1. Centralized meta-governance policies
Defined centralized data governance guardrails and measures for quality, security and compliance
2. Domain-aligned virtual data marts
Mapped business domains to specific product and operational areas. Created data pipelines and access views pipes by domain.
3. Self-service data marketplaces
Enabled lines of businesses easily discover, understand and directly leverage area data assets.
4. Distributed MLOps and analytics
Embedded ML models for usage within services for real-time personalization and decision intelligence
Outcomes Across Speed, Agility and Innovation
Via rapidly provisioned cloud infrastructure, orchestration and machine learning automation, the bank achieved substantial benefits:
- 80% decrease time-to-market for new data products
- 4X model implementation throughput with MLOps
- 90% self-service reporting throughput
Also critical – decentralized data ownership better empowered individual groups adapting to local and emerging needs.
By blending legacy systems with modern cloud analytics, data mesh principles reshaped the bank‘s data landscape for an agile future.
Physical data models provide actionable blueprints – translating business objectives into built systems optimizing for security, stability and performance. Their flexibility to adapt to new technologies like AI-based automation and distributed data mesh make them future-proof foundations for enterprise data.
Conceptual mastery of modeling techniques – both timeless statistics-oriented practices alongside leading-edge cloud-native and metadata management methodologies – creates well-rounded, coveted practitioners able to provide strategic value amid intensifying data complexities.
