Application-Specific Data

Metagraphs support the ingestion, validation, and on-chain storage of application-specific data types. This data is submitted to the metagraph as DataUpdates, which are defined by the metagraph itself. These updates can represent any structured or semi-structured real-world data—from IoT sensor readings and business events to legal records, logs, analytics, or anything else meaningful to the domain the metagraph serves.

Each DataUpdate is a locally-defined transaction submitted to the metagraph’s Data L1 layer. This layer is responsible for parsing and validating the incoming data, enforcing application-specific logic, and participating in graph-based consensus rounds that produce blocks. Once a Data L1 block is finalized, it is sent to the Metagraph L0, where a second layer of validation and finalization takes place. The L0 layer aggregates blocks into a metagraph snapshot, which is the authoritative unit of state for the metagraph and is submitted to the Global L0 (Hypergraph) for final consensus and inclusion in a global snapshot.

Defining Custom Data Behavior

Metagraph developers define their custom data schema and logic by extending DataApplication in the Metagraph Framework. This allows developers to specify:

• How incoming data should be decoded

• What signature or authentication rules should apply

• Any domain-specific validation logic that determines whether a DataUpdate is accepted or rejected

• How updates should be grouped into blocks

• What data should be added to local, working memory (Calculated State)

• Whether and how data is stored on-chain (e.g., full payloads vs. notarized hashes)

These capabilities make metagraphs highly adaptable to a wide range of use cases. For example:

• A metagraph used in public safety might ingest vehicle telemetry and timestamped incident logs, notarizing them for legal verification.

• A supply chain metagraph might process signed delivery confirmations and proof-of-origin certificates.

• A healthcare metagraph might process encrypted patient records, with selective notarization for audit and compliance.

Design Considerations and Use Patterns

Because DataUpdates are handled within the metagraph’s own L1 and L0 layers, each metagraph can independently enforce its own trust assumptions, data models, and regulatory constraints—while still anchoring finalized state to the Hypergraph for global ordering and provability.

Developers also have control over how data is stored and queried. Not all data needs to be stored in full on-chain. Many metagraphs choose to store only hashes or references on-chain to preserve privacy or reduce storage costs, while maintaining cryptographic guarantees about the data’s authenticity and timestamp.

Custom data processing on metagraphs can also be linked with FeeTransactions, which enable pay-per-update business models. A FeeTransaction contains a cryptographic reference to the data it is associated with, allowing a metagraph to tie utility (data validation and storage) to token-based compensation in a verifiable way.

Metagraphs offer a powerful and flexible environment for building domain-specific data pipelines with on-chain guarantees. The combination of custom logic, horizontal scalability, and Hypergraph anchoring enables developers to build high-integrity, application-specific blockchain systems that are purpose-built for real-world workflows and data integrity requirements.

For more detailed information on implementing application-specific data processing on metagraphs, see Metagraph Framework Data.