Capacity Planning - Learning Module

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Future Proofing

Building for an Uncertain Future

Technology changes. Business requirements evolve. The database infrastructure you build today will need to adapt to circumstances you cannot fully predict. Future proofing is the discipline of making architectural and operational choices that preserve flexibility, minimize lock-in, and position systems for graceful evolution.

Future proofing is not about predicting the future—it's about building adaptability into the foundation of your infrastructure. It's about making decisions today that leave options open tomorrow, even when the specific path forward is unclear.

This page explores strategies for building database infrastructure that remains viable and adaptable as requirements, technologies, and business conditions evolve over time.

What You Will Learn

By the end of this page, you will understand how to anticipate technology evolution, architect for flexibility, manage vendor relationships strategically, and build systems that can adapt to changing requirements. You'll learn to balance current needs against future optionality.

The Challenge of Long-Term Planning

Database infrastructure operates on uniquely long timescales. While application frameworks may change every few years, databases persist for decades. Data accumulated today will be accessed—and must remain accessible—for years or decades to come.

Why databases require long-term thinking:

Database Longevity Factors

•Data Persistence — Unlike stateless applications, databases hold irreplaceable state. Migrating decades of data is expensive and risky.
•Integration Depth — Databases integrate with many systems. Changes ripple across the entire technology stack.
•Schema Inertia — Schema changes in production databases are complex operations. Poor initial design becomes permanent burden.
•Skill Investment — Teams invest heavily in learning specific database systems. Switching has significant human capital cost.
•License Contracts — Multi-year Enterprise license agreements lock in technology and cost trajectories.

The prediction paradox:

Long-term planning is essential, yet long-term predictions are unreliable. Five years ago, who predicted the current state of serverless databases, cloud-native architectures, or AI-driven optimization? The key is not to predict the future but to build systems that can adapt to multiple possible futures.

Consider how technology has evolved:

Database Technology Evolution Over Decades
Era	Dominant Technology	Key Innovation	Prediction Accuracy
1990s	Relational + Client-Server	SQL standardization, ODBC/JDBC	Unexpected: rise of web
2000s	Enterprise RDBMS, Data Warehouses	Scale-up, HA clusters	Unexpected: commodity horizontal scale
2010s	NoSQL, Big Data, Cloud	Horizontal scale, schema flexibility	Unexpected: return to SQL (NewSQL)
2020s	Cloud-native, Serverless, Multi-model	Managed services, separation of compute/storage	Unexpected: AI/ML integration depth
2030s	???	???	Will surprise us again

Embrace Uncertainty

The goal isn't accurate 10-year predictions—it's building systems that remain viable regardless of which predictions prove correct. Optimize for adaptability, not for a specific future state.

Designing for Architectural Flexibility

Architectural choices made early become constraints later. Designing for flexibility means accepting some upfront complexity in exchange for future adaptability.

Principles of flexible architecture:

Flexibility Design Principles

•Abstraction Layers — Abstract database access behind well-defined interfaces. Applications shouldn't know (or care) about specific database implementations.
•Standard Interfaces — Use SQL standard syntax where possible. Avoid vendor-specific extensions unless essential.
•Modular Data Domains — Separate bounded contexts into distinct databases that can evolve independently.
•Schema Evolution Support — Design schemas that can accommodate new requirements without breaking existing functionality.
•Export/Import Capability — Ensure data can be exported in portable formats (CSV, JSON, Parquet) for potential migration.
•Automated Testing — Comprehensive tests enable safe refactoring of data layer implementations.

Database abstraction patterns:

database_abstraction.py
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"""
Database Abstraction Layer Patterns
 
Demonstrates abstraction approaches that enable database portability
and future migration flexibility.
"""
 
from abc import ABC, abstractmethod
from typing import List, Dict, Optional, Any
from dataclasses import dataclass
from datetime import datetime
 
 
@dataclass
class User:
    """Domain entity - database-agnostic"""
    id: str
    email: str
    name: str
    created_at: datetime
    metadata: Dict[str, Any]
 
 
class UserRepository(ABC):
    """
    Repository interface - defines what operations are needed,
    not how they're implemented. Enables swapping implementations.
    """
    
    @abstractmethod
    async def find_by_id(self, user_id: str) -> Optional[User]:
        pass
    
    @abstractmethod
    async def find_by_email(self, email: str) -> Optional[User]:
        pass
    
    @abstractmethod
    async def save(self, user: User) -> User:
        pass
    
    @abstractmethod
    async def delete(self, user_id: str) -> bool:
        pass
    
    @abstractmethod
    async def list_all(self, limit: int = 100, offset: int = 0) -> List[User]:
        pass
 
 
class PostgresUserRepository(UserRepository):
    """PostgreSQL implementation of UserRepository"""
    
    def __init__(self, connection_pool):
        self.pool = connection_pool
    
    async def find_by_id(self, user_id: str) -> Optional[User]:
        async with self.pool.acquire() as conn:
            row = await conn.fetchrow(
                "SELECT id, email, name, created_at, metadata FROM users WHERE id = $1",
                user_id
            )
            return self._row_to_user(row) if row else None
    
    async def save(self, user: User) -> User:
        async with self.pool.acquire() as conn:
            await conn.execute("""
                INSERT INTO users (id, email, name, created_at, metadata)
                VALUES ($1, $2, $3, $4, $5)
                ON CONFLICT (id) DO UPDATE SET
                    email = EXCLUDED.email,
                    name = EXCLUDED.name,
                    metadata = EXCLUDED.metadata
            """, user.id, user.email, user.name, user.created_at, user.metadata)
        return user
    
    # ... other method implementations
    
    def _row_to_user(self, row) -> User:
        return User(
            id=row['id'],
            email=row['email'],
            name=row['name'],
            created_at=row['created_at'],
            metadata=row['metadata']
        )
 
 
class MongoUserRepository(UserRepository):
    """MongoDB implementation - same interface, different storage"""
    
    def __init__(self, database):
        self.collection = database.users
    
    async def find_by_id(self, user_id: str) -> Optional[User]:
        doc = await self.collection.find_one({"_id": user_id})
        return self._doc_to_user(doc) if doc else None
    
    async def save(self, user: User) -> User:
        await self.collection.update_one(
            {"_id": user.id},
            {"$set": {
                "email": user.email,
                "name": user.name,
                "created_at": user.created_at,
                "metadata": user.metadata
            }},
            upsert=True
        )
        return user
    
    # ... other method implementations
    
    def _doc_to_user(self, doc) -> User:
        return User(
            id=doc['_id'],
            email=doc['email'],
            name=doc['name'],
            created_at=doc['created_at'],
            metadata=doc.get('metadata', {})
        )
 
 
class CachedUserRepository(UserRepository):
    """
    Decorator pattern - adds caching without changing interface.
    Demonstrates how abstraction enables cross-cutting concerns.
    """
    
    def __init__(self, delegate: UserRepository, cache):
        self.delegate = delegate
        self.cache = cache
        self.ttl_seconds = 300
    
    async def find_by_id(self, user_id: str) -> Optional[User]:
        # Check cache first
        cache_key = f"user:{user_id}"
        cached = await self.cache.get(cache_key)
        if cached:
            return User(**cached)
        
        # Fallback to delegate
        user = await self.delegate.find_by_id(user_id)
        if user:
            await self.cache.set(cache_key, user.__dict__, ex=self.ttl_seconds)
        return user
    
    async def save(self, user: User) -> User:
        # Invalidate cache, then save
        await self.cache.delete(f"user:{user.id}")
        return await self.delegate.save(user)
    
    # ... other methods delegate and manage cache appropriately
 
 
# Dependency Injection configuration
class DatabaseConfig:
    """
    Factory that creates appropriate repository implementation
    based on configuration. Switching database is config change, not code change.
    """
    
    @staticmethod
    async def create_user_repository(config: Dict) -> UserRepository:
        db_type = config.get('database_type', 'postgres')
        
        if db_type == 'postgres':
            pool = await create_postgres_pool(config['postgres'])
            repo = PostgresUserRepository(pool)
        elif db_type == 'mongodb':
            client = await create_mongo_client(config['mongodb'])
            repo = MongoUserRepository(client.database)
        else:
            raise ValueError(f"Unknown database type: {db_type}")
        
        # Add caching layer if configured
        if config.get('enable_cache'):
            cache = await create_redis_client(config['redis'])
            repo = CachedUserRepository(repo, cache)
        
        return repo
 
 
# Usage: Application code is database-agnostic
class UserService:
    """Business logic - has no idea what database is used"""
    
    def __init__(self, user_repo: UserRepository):
        self.users = user_repo  # Only knows the interface
    
    async def get_user_profile(self, user_id: str) -> Optional[Dict]:
        user = await self.users.find_by_id(user_id)
        if not user:
            return None
        return {
            'name': user.name,
            'email': user.email,
            'member_since': user.created_at.isoformat()
        }

Abstraction Trade-offs

Abstraction has costs—it adds layers, may prevent optimized database features, and requires upfront design effort. Apply abstraction strategically at system boundaries where future flexibility is valuable, not universally to every database operation.

Schema Evolution Strategy

Database schemas evolve continuously as business requirements change. Poorly managed schema evolution creates technical debt, causes outages, and eventually makes systems unmaintainable. A disciplined approach to schema changes is essential for long-term viability.

Schema evolution principles:

Schema Change Principles

•Backwards Compatibility — New schema should support old application versions during rollout. Old code should function (perhaps with reduced features) against new schema.
•Forwards Compatibility — Where possible, schema should tolerate future additions without breaking. Nullable columns, optional fields.
•Non-Breaking Changes — Prefer additive changes (new columns, tables) over destructive changes (renames, type changes, removals).
•Expand-Contract Pattern — For breaking changes: first expand (add new alongside old), migrate, then contract (remove old).
•Version Awareness — Application should understand schema version and handle differences gracefully.
•Migration Automation — All schema changes via versioned, tested, automated migrations. Never manual DDL in production.

expand_contract_migration.sql
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-- Expand-Contract Pattern: Renaming a Column Safely
-- This demonstrates zero-downtime schema evolution
 
-- ============================================
-- PHASE 1: EXPAND
-- Add new column alongside old, sync data
-- Duration: Deploy immediately
-- ============================================
 
-- Add the new column (nullable initially)
ALTER TABLE orders ADD COLUMN customer_email VARCHAR(255);
 
-- Create trigger to keep old and new in sync during transition
CREATE OR REPLACE FUNCTION sync_email_columns()
RETURNS TRIGGER AS $$
BEGIN
    -- Sync from old to new
    IF NEW.email IS DISTINCT FROM OLD.email THEN
        NEW.customer_email := NEW.email;
    END IF;
    -- Sync from new to old
    IF NEW.customer_email IS DISTINCT FROM OLD.customer_email THEN
        NEW.email := NEW.customer_email;
    END IF;
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;
 
CREATE TRIGGER orders_email_sync
BEFORE UPDATE ON orders
FOR EACH ROW EXECUTE FUNCTION sync_email_columns();
 
-- Backfill existing data
UPDATE orders SET customer_email = email WHERE customer_email IS NULL;
 
-- Add index on new column
CREATE INDEX CONCURRENTLY idx_orders_customer_email ON orders(customer_email);
 
-- ============================================
-- PHASE 2: MIGRATE CODE
-- Update application to read from new column
-- Old code continues working (reads old column)
-- Duration: Deploy application changes
-- ============================================
 
-- Application code transitions:
-- v1: READ email, WRITE email
-- v2: READ customer_email, WRITE both (for rollback safety)
-- v3: READ customer_email, WRITE customer_email only
 
-- ============================================
-- PHASE 3: CONTRACT
-- Remove old column after all code is updated
-- Duration: After confidence period (days/weeks)
-- ============================================
 
-- Remove the sync trigger
DROP TRIGGER orders_email_sync ON orders;
DROP FUNCTION sync_email_columns();
 
-- Remove old column (only after all readers are updated)
ALTER TABLE orders DROP COLUMN email;
 
-- Rename new column to final name if desired
ALTER TABLE orders RENAME COLUMN customer_email TO email;
 
-- ============================================
-- ROLLBACK CAPABILITY AT EACH PHASE
-- ============================================
 
-- Phase 1 rollback: 
--   ALTER TABLE orders DROP COLUMN customer_email;
--   DROP TRIGGER/FUNCTION...
--   (No data loss - old column still primary)
 
-- Phase 2 rollback:
--   Deploy v1 application code
--   (Both columns exist, sync trigger keeps them aligned)
 
-- Phase 3 rollback:
--   Not easily reversible - this is the point of no return
--   (Hence the "confidence period" before Phase 3)

Never Skip the Expand Phase

The temptation to 'just rename the column' during maintenance windows leads to outages when rollbacks are needed. The expand-contract pattern takes longer but maintains rollback capability throughout. Always keep rollback paths open.

Vendor Strategy and Lock-in Management

Database vendor relationships span years or decades. Strategic vendor management balances the benefits of deep platform investment against the risks of excessive dependency.

Spectrum of vendor lock-in:

Database Vendor Lock-in Spectrum
Lock-in Level	Characteristics	Migration Cost	Advantages
Minimal	Standard SQL, open formats, abstraction layers	Low	Maximum flexibility; may underutilize platform
Moderate	Some platform features, manageable dependencies	Medium	Balanced productivity and flexibility
Significant	Deep use of proprietary features, stored procedures	High	Full platform capabilities; significant rewrite to migrate
Extreme	Platform-specific everywhere, no abstraction	Very High	Maximum platform value; migration is major project

Lock-in Management Strategies

•Intentional Lock-in Decisions — Don't accidentally lock in. Each proprietary feature should be a conscious choice with documented rationale. 'We chose this because...'
•Periodic Migration Assessment — Every 1-2 years, estimate migration cost to alternatives. If it's growing faster than expected, address proactively.
•Multi-Vendor Competency — Maintain team skills in multiple database platforms. Run non-critical workloads on alternatives to preserve knowledge.
•Contract Terms — Negotiate data export rights, format specifications, and assistance with migration in contracts.
•Open Source Affinity — For areas where lock-in is unacceptable, prefer open-source databases with multiple providers.
•Exit Plan Documentation — Document what migration would require. The plan needn't be executed, but should be feasible.

Making lock-in decisions explicit:

lock_in_decision_template.md
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# Vendor Lock-in Decision Record
 
## Decision: Use Oracle Partitioning for Large Transaction Tables
 
### Date: 2024-01-15
### Decision Owner: Database Architecture Team
### Status: APPROVED
 
## Context
The orders table is projected to exceed 500GB within 12 months. 
Queries against recent orders must remain sub-100ms. Historical 
queries can tolerate longer latencies.
 
## Options Considered
 
### Option A: Table Partitioning (Oracle Feature)
- Automatic partition management and pruning
- Mature, well-documented feature
- Requires Oracle Enterprise Edition license
- **Lock-in Impact: MODERATE** - Partitioning syntax is Oracle-specific
 
### Option B: Application-Level Sharding
- Portable across databases
- Significant application complexity
- No additional licensing
- **Lock-in Impact: LOW** - Standard SQL
 
### Option C: Time-Series Database for Historical
- Separate hot/cold data stores
- Operational complexity increases
- Mixed technology stack
- **Lock-in Impact: LOW** - Standard interfaces
 
## Decision
**Chose Option A: Oracle Partitioning**
 
## Rationale
- Feature is mature and well-supported
- Team has existing Oracle expertise
- Migration cost is acceptable given Oracle investment horizon
- Partition pruning optimization is significant performance benefit
- Application complexity of Option B deemed higher risk
 
## Lock-in Implications
- Requires Oracle Enterprise Edition ($X/core/year)
- Partition DDL is Oracle-specific syntax
- Migration would require:
  - Rebuild tables without partitioning OR
  - Implement application-level date filtering
  - Estimated effort: 2-4 weeks engineering
 
## Mitigation
- Document partitioning scheme clearly  
- Abstract partition maintenance in operations runbooks
- Include in bi-annual migration cost assessment
- Ensure export procedures can extract data by partition
 
## Review Date: 2025-01
### Re-review if:
- Oracle licensing costs increase significantly
- Cloud migration timeline accelerates
- PostgreSQL partitioning matures

Lock-in is Not Always Bad

Deep platform utilization often delivers genuine value—performance, reliability, reduced development time. The goal isn't zero lock-in, but informed lock-in where benefits justify dependencies and exit remains feasible if needed.

Maintaining a Technology Radar

Future-proofing requires awareness of emerging technologies and industry trends. A technology radar is a systematic approach to tracking, evaluating, and selectively adopting new database technologies.

Technology radar quadrants:

Database Technology Radar Structure
Ring	Description	Action	Example Technologies
ADOPT	Proven, recommended for default use	Use for new projects	PostgreSQL, Redis, managed cloud SQL
TRIAL	Worth pursuing; proven in limited scope	Use on appropriate new projects	Serverless databases, NewSQL platforms
ASSESS	Worth exploring; not yet proven	Research and prototype; don't deploy to production	Vector databases, AI-integrated databases
HOLD	Not recommended; phase out over time	Avoid for new projects; plan migration for existing	Legacy versions, deprecated features

Radar maintenance process:

•Quarterly Review — Assess each technology's position. Has anything moved between rings? What's new to track?
•Industry Monitoring — Follow database vendor roadmaps, conference announcements, and analyst reports.
•Prototype Projects — For 'ASSESS' technologies, dedicate time for hands-on evaluation. Use sandbox environments.
•Community Input — Gather feedback from team members who have explored or used specific technologies.
•Document Rationale — Record why technologies are placed in each ring. This context is essential for decision-making.
•Publication — Share the radar with the organization. Transparency enables informed decisions across teams.

technology_radar_example.yaml
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# Database Technology Radar - Q1 2024
# Last Updated: 2024-01-15
# Owner: Database Architecture Team
 
adopt:
  - name: PostgreSQL 15+
    category: RDBMS
    notes: Default choice for new relational workloads
    since: 2022-Q2
    
  - name: Redis 7+
    category: Caching/KV
    notes: Session cache, rate limiting, pub/sub
    since: 2021-Q1
    
  - name: AWS RDS / Azure SQL
    category: Managed Services
    notes: Preferred over self-managed for most workloads
    since: 2023-Q1
 
trial:
  - name: CockroachDB
    category: NewSQL
    notes: Evaluating for globally distributed workloads
    since: 2023-Q3
    use_cases: ["geo-distributed apps", "global consistency needs"]
    contact: "@db-architecture"
    
  - name: Serverless Aurora
    category: Managed Services
    notes: Cost-effective for variable workloads; evaluating scale-to-zero
    since: 2023-Q4
    use_cases: ["dev environments", "variable traffic apps"]
    
  - name: TimescaleDB
    category: Time-Series
    notes: PostgreSQL extension for IoT/metrics; replaces InfluxDB
    since: 2023-Q2
    use_cases: ["application metrics", "IoT data"]
 
assess:
  - name: Vector Databases (Pinecone, Milvus)
    category: AI/ML
    notes: Required for AI features; evaluating options
    since: 2023-Q4
    research_owner: "@ml-platform"
    
  - name: Neon / PlanetScale
    category: Serverless
    notes: Interesting database branching capabilities
    since: 2024-Q1
    
  - name: DuckDB
    category: Embedded Analytics
    notes: Potential for in-process analytical queries
    since: 2023-Q4
 
hold:
  - name: MongoDB Atlas (new projects)
    category: Document Store
    notes: "Existing deployments maintained; new document needs review PostgreSQL JSONB first"
    since: 2023-Q3
    rationale: "Consolidation on PostgreSQL for simpler operations"
    
  - name: Self-managed databases (production)
    category: Operations
    notes: "Use managed services unless specific requirement for self-managed"
    since: 2022-Q4
    
  - name: Oracle (new projects)
    category: RDBMS
    notes: "Cost prohibitive for new projects; PostgreSQL preferred"
    since: 2023-Q1
    migration_plan: "Existing Oracle workloads assessed for migration individually"
 
upcoming_reviews:
  - technology: PostgreSQL 16 Features
    review_date: 2024-Q2
    owner: "@db-architecture"
    
  - technology: AlloyDB
    review_date: 2024-Q2
    owner: "@cloud-team"
 
retired:
  - name: MySQL 5.7
    date: 2023-Q4
    replacement: "PostgreSQL or MySQL 8.0"
    
  - name: Elasticsearch (self-managed)
    date: 2023-Q2
    replacement: "Elastic Cloud or OpenSearch"

Radar is Advisory, Not Mandatory

The technology radar provides guidance, not rigid rules. Exceptional circumstances may warrant exceptions. The value is in making technology choices intentional and well-informed, not in restricting innovation.

Skills and Knowledge Investment

Future-proof infrastructure requires future-ready teams. Investing in skills diversification, continuous learning, and knowledge management ensures human capabilities evolve alongside technology.

Skills investment strategy:

Depth Skills

•Deep expertise in primary platforms
•Internals, tuning, troubleshooting
•Advanced features mastery
•Architecture for specific workloads
•Performance at scale

Breadth Skills

•Familiarity with multiple platforms
•General database concepts
•Emerging technology awareness
•Cloud platform basics
•Polyglot persistence patterns

Practical Learning Approaches

•Rotation Programs — Rotate team members through different database platforms. Production support on unfamiliar platforms builds real expertise.
•Sandbox Environments — Maintain easily-provisioned sandboxes for all radar technologies. Remove friction from exploration.
•Learning Time Allocation — Dedicate percentage of time (10-20%) to learning and experimentation. Make it explicit and expected.
•Conference Participation — Database-specific conferences (PGConf, Oracle OpenWorld, re:Invent) provide industry context.
•Internal Tech Talks — Regular presentations on technologies, techniques, and lessons learned. Share knowledge across the team.
•Documentation Culture — Document tribal knowledge into persistent, searchable formats. Reduces bus-factor risk.

Cross-Training for Resilience

No individual should be the only person who can perform critical operations. Cross-training ensures operations continue during vacations, illnesses, and departures. It also improves overall team quality through knowledge sharing.

Building Capacity for Change

Ultimately, future-proofing means building organizational capacity to execute change when needed. The best architecture and technology choices are worthless if the organization can't actually implement changes.

Change capacity elements:

Organizational Change Enablers

•Automation Infrastructure — CI/CD pipelines, infrastructure-as-code, automated testing. Changes are low-friction to implement.
•Safe Deployment Patterns — Blue-green, canary, feature flags. Changes can be rolled out gradually with rollback capability.
•Observability — Comprehensive monitoring and alerting. Changes' impacts are visible immediately.
•Incident Learning — Blameless postmortems, continuous improvement. Failures inform future change approaches.
•Change Budget — Time and resources allocated for improvement work. Not everything is feature delivery.
•Risk Appetite — Leadership support for calculated risk-taking. Paralysis from fear of change is itself a risk.

Migration Readiness Assessment
Capability	Low Readiness	High Readiness
Schema Changes	Manual, risky, avoided	Automated, tested, routine
Data Migration	One-off scripts, manual verification	Repeatable processes, automated validation
Platform Upgrades	Multi-day events with downtime	Rolling upgrades during business hours
Environment Creation	Weeks, manual setup	Hours, fully automated
Rollback	Hope it's not needed	Push-button, tested regularly
Knowledge Access	Tribal, in specific people's heads	Documented, searchable, maintained

Practice Changes Regularly

The best way to build change capacity is to practice changing. Small, frequent changes build muscle memory, refine processes, and reduce fear. Organizations that only change during crises are bad at changing.

Summary: Building for an Evolving Future

Future-proofing is not about predicting specific future states—it's about building systems and organizations that can adapt to whatever the future brings. Through architectural flexibility, strategic vendor management, disciplined schema evolution, and continuous skills investment, database infrastructure can remain viable across technology generations.

Key Takeaways

•Embrace uncertainty — The future is unpredictable. Optimize for adaptability, not for specific predictions.
•Invest in abstraction — Strategic abstraction layers enable database changes without application rewrites.
•Evolve schemas carefully — The expand-contract pattern maintains rollback capability throughout migrations.
•Manage vendor relationships — Make lock-in decisions intentionally with documented rationale and exit plans.
•Track technology trends — A technology radar provides structured awareness of emerging options.
•Invest in people — Skills diversification ensures the team can adapt alongside technology.
•Build change capacity — Automation, processes, and culture that enable safe, routine changes.

Module Complete:

This module has covered the full spectrum of capacity planning—from estimating growth, to sizing resources, to choosing scaling strategies, to optimizing costs, to preparing for an uncertain future. Together, these disciplines enable database infrastructure that serves current needs while remaining viable for years to come.

Your practice:

Create a growth estimation model for a database you manage
Conduct a rightsizing analysis against current utilization
Document your vendor lock-in decisions with rationale
Build or update your team's technology radar
Assess your organization's change capacity

Module Complete: Capacity Planning

You have completed the Capacity Planning module. You now possess the knowledge to estimate growth, size resources, scale strategically, optimize costs, and build infrastructure that remains adaptable. Apply these practices to ensure your database systems thrive through growth and evolution.