Composable infrastructure for connecting, normalizing, synchronizing, and accessing data across services.
Data Platform provides a shared foundation for working with data distributed across external services, applications, APIs, and personal systems.
Rather than coupling integrations directly to individual applications, the platform provides reusable connectors, normalized data models, synchronization primitives, and stable access interfaces.
Its purpose is to turn fragmented data sources into a coherent and programmable data layer.
External services should become capabilities, not application-specific integrations.
Modern data is fragmented across many systems:
Calendar
Email
Files
Messages
Tasks
Notes
Contacts
Git repositories
Cloud services
APIs
Databases
Each service exposes different:
- Authentication models
- APIs
- Schemas
- Pagination strategies
- Rate limits
- Webhooks
- Error models
- Synchronization semantics
Applications integrating these services independently repeatedly solve the same infrastructure problems.
Data Platform centralizes those problems behind reusable boundaries.
External Services
│
▼
Connectors
│
▼
Normalization
│
▼
Synchronization
│
▼
Data Layer
│
▼
Applications / Agents / Automation
Capabilities should be independently useful and combine without requiring the entire platform.
Provider boundaries, canonical models, synchronization state, and public interfaces should have explicit contracts.
Applications should depend on stable platform interfaces rather than individual provider APIs wherever meaningful abstractions exist.
Synchronization, connector behavior, failures, and data movement should be inspectable.
Applications should be able to consume individual capabilities without migrating their entire data architecture.
The platform should clearly distinguish provider data, normalized data, application data, and synchronization state.
Access boundaries, credential handling, provenance, and data minimization are architectural concerns.
The platform separates provider-specific behavior from normalized domain data.
Provider API
│
▼
Connector
│
▼
Provider Model
│
▼
Normalizer
│
▼
Canonical Model
│
▼
Storage / Query / Events
Consumers can interact with stable interfaces even when the underlying providers differ.
Connectors isolate provider-specific behavior.
A connector may be responsible for:
- Authentication integration
- API communication
- Pagination
- Rate-limit handling
- Provider-specific schemas
- Error translation
- Webhook handling
- Incremental retrieval
- Provider capabilities
Conceptually:
Google ───────┐
│
GitHub ───────┤
│
Notion ───────┼──► Connector Interface
│
Slack ────────┤
│
Other APIs ───┘
Consumers should not need to understand the implementation details of individual providers.
Provider schemas should not automatically become application contracts.
Different providers may represent the same conceptual entity differently.
Provider A ──► Model A ──┐
│
Provider B ──► Model B ──┼──► Canonical Model
│
Provider C ──► Model C ──┘
Possible canonical domains may include:
- People
- Messages
- Documents
- Events
- Tasks
- Files
- Repositories
- Activity
- Organizations
Canonicalization should only be introduced where providers genuinely share useful semantics.
Provider-specific information can remain available when normalization would otherwise discard meaningful data.
Synchronization is a first-class platform capability.
A synchronization pipeline may resemble:
Fetch
│
▼
Validate
│
▼
Normalize
│
▼
Compare
│
▼
Persist
│
▼
Publish Change
Synchronization should operate incrementally wherever provider capabilities allow it.
The platform should avoid repeatedly importing complete datasets when only a small subset has changed.
Synchronization requires explicit state.
Examples include:
- Cursors
- Checkpoints
- Continuation tokens
- Last successful synchronization
- Resource versions
- Retry state
- Partial failures
Connector
│
├── Data
│
└── Sync State
│
▼
Next Operation
Sync state should be inspectable rather than hidden inside connector implementations.
Providers expose changes in different ways.
The platform may support strategies such as:
Webhooks ───────────┐
│
Polling ────────────┤
│
Incremental APIs ───┼──► Change Detection
│
Version Comparison ─┤
│
Content Hashing ────┘
The appropriate strategy depends on provider capabilities.
Consumers should receive a coherent change model above those differences.
Normalized data should retain information about its origin.
A record may include metadata describing:
- Source provider
- External identifier
- Connector
- Synchronization timestamp
- Original version
- Transformation history
Canonical Record
├── Data
├── Source
├── External ID
├── Retrieved At
└── Metadata
Provenance makes synchronized data traceable and helps consumers understand where information originated.
Consumers should be able to access normalized data through stable interfaces.
Application ──┐
│
Automation ───┼──► Query Layer ──► Data
│
Agent ────────┘
The query layer may support:
- Filtering
- Sorting
- Pagination
- Relationships
- Time ranges
- Source filtering
- Aggregation
Search-specific indexing, retrieval, and ranking remain owned by Search.
Data Platform should not unnecessarily bind its domain model to one persistence technology.
Data Platform
│
▼
Storage Interface
│
├── PostgreSQL
├── SQLite
├── Memory
└── Other Adapters
Storage adapters allow persistence to evolve without leaking implementation details throughout the platform.
Meaningful data-domain changes may produce events.
Examples include:
document.created
document.updated
message.received
sync.completed
sync.failed
connector.connected
connector.disconnected
Data Platform owns the semantics of its domain events.
Event Platform owns generic event contracts, transport, delivery, and communication infrastructure.
This keeps data semantics independent from event-delivery mechanics.
External connections frequently require user or service identity.
Identity owns:
- Authentication
- Authorization
- Principal representation
- Access-control primitives
Data Platform owns:
- Connector authorization state
- Provider credential integration
- Data-source ownership
- Access to synchronized resources
Sensitive credentials should be delegated to appropriate secure storage rather than embedded directly into normalized records.
External services fail.
The platform should expect:
- Network failures
- Timeouts
- Expired credentials
- Rate limits
- Partial responses
- Provider outages
- Invalid payloads
- Schema changes
Connector operations should support appropriate:
- Retries
- Backoff
- Timeouts
- Idempotency
- Failure isolation
- Recovery
A failure in one provider should not unnecessarily compromise unrelated connectors.
Synchronization and connector behavior should expose domain-specific telemetry.
Useful signals may include:
- Synchronization duration
- Records processed
- Records changed
- API requests
- Rate-limit state
- Connector failures
- Retry counts
- Synchronization lag
- Webhook processing latency
Observability owns the generic logging, metrics, tracing, and telemetry infrastructure used to collect and analyze those signals.
A data platform may process sensitive information.
Privacy therefore belongs in the architecture.
Important principles include:
- Data minimization
- Explicit access boundaries
- Least privilege
- Source transparency
- Deletion support
- Credential isolation
- Auditable access
- Clear retention semantics
The platform should collect only the information required for a defined capability.
A major use case is providing AI systems with structured access to distributed data.
Instead of agents integrating independently with every external provider:
Agent
│
▼
Data Platform
│
├── Calendar
├── Documents
├── Messages
├── Tasks
├── Repositories
└── Other Sources
Data Platform provides a consistent capability layer while preserving authorization, provenance, and access boundaries.
Agents should interact with explicit interfaces rather than unrestricted raw data stores.
Agent Network owns agent execution and orchestration.
Data Platform owns the data capabilities those agents may consume.
Normalized changes can also drive deterministic automation.
External Change
│
▼
Data Platform
│
▼
Domain Event
│
▼
Event Platform
│
▼
Automation
Provider-specific changes can therefore trigger reusable workflows without coupling automation logic directly to external APIs.
The exact structure will evolve through concrete integrations.
A capability-oriented organization may resemble:
data-platform/
├── connectors/
├── models/
├── normalization/
├── query/
├── storage/
├── sync/
├── transforms/
├── validation/
├── integrations/
├── testing/
└── docs/
Directories should represent stable data capabilities rather than duplicate generic infrastructure owned elsewhere.
Data Platform owns:
- Connectors
- Provider adapters
- Synchronization
- Canonical data models
- Normalization
- Data provenance
- Data access abstractions
- Data-domain events
- Connector state
Data Platform does not own:
- Generic authentication
- Generic authorization
- Event transport infrastructure
- Search ranking
- Generic observability infrastructure
- Agent orchestration
- Application UI
- Product-specific business logic
Those responsibilities belong to their respective systems.
Data Platform composes with the broader infrastructure ecosystem.
Applications
│
▼
data-platform
│
┌────────────────┼────────────────┐
▼ ▼ ▼
identity event-platform search
│
┌─────────┼─────────┐
▼ ▼
observability agent-network
Relevant integrations include:
- identity — Identity, authentication, authorization, and access control
- event-platform — Shared event contracts and communication infrastructure
- observability — Logging, metrics, tracing, and diagnostics
- search — Search, retrieval, ranking, and discovery
- testkit — Shared testing utilities, fixtures, and harnesses
- cli — Development and operational commands
- agent-network — Agent access to structured data capabilities
- trust-platform — Trust, policy, risk, and safety infrastructure
- engineering — Shared engineering standards and architectural principles
Applications consume these capabilities without owning the underlying integration infrastructure.
Data Platform is not intended to become:
- A universal database
- A replacement for every provider API
- An application backend
- A generic ETL product
- A search engine
- An identity provider
- An event platform
- An agent runtime
Its responsibility is narrower:
Connect distributed data sources and expose them through coherent, reusable interfaces.
Potential areas of development include:
- Connector contracts
- Synchronization primitives
- Sync-state management
- Canonical models
- Change detection
- Provider adapters
- Storage adapters
- Query interfaces
- Provenance
- Webhook ingestion
- Conflict handling
- Connector testing
- Operational tooling
- Agent-facing capabilities
Capabilities should emerge from concrete integrations rather than attempting to model every possible data source upfront.
- Stable interfaces over provider details
- Normalize only where semantics genuinely align
- Explicit synchronization state
- Provenance by default
- Failure isolation
- Least-privilege access
- Observable data movement
- Provider independence
- Clear infrastructure boundaries
- Composition over duplication
The long-term goal is to create a programmable data layer across otherwise disconnected systems.
External services become capabilities rather than isolated integrations.
Applications consume normalized information without understanding every provider-specific API.
Automation reacts to changes through stable event contracts.
Agents access authorized information through structured interfaces.
New providers integrate through connectors rather than requiring architectural changes throughout every consuming application.
The result is reusable data infrastructure for applications, automation, developer tooling, and AI systems.
🚧 Early development.
Initial work focuses on defining connector boundaries, synchronization semantics, and canonical models through concrete integrations rather than attempting to model every possible data source upfront.
Licensed under the MIT License.