[FEATURE]: Evaluate using Merkle Tree as a form of integrity check

Use case

Merkle Trees are exceptional at ensuring the integrity of large sets of data. By using cryptographic hashes, they can quickly verify whether the data has been altered or tampered with. It is being used in Blockchains, p2p networks and databases.

Unlike traditional methods, Merkle Trees don't require the entire dataset to be downloaded for verification. Only a subset of hashes is necessary to prove the integrity of the data, making the process significantly faster and more scalable, especially for large datasets.

If any portion of the data is corrupted or inaccessible, Merkle Trees enable the retrieval of the remaining data without compromise. This ensures high availability and resilience, which are critical attributes for any data-driven project.

With Merkle Trees, auditing data changes and maintaining historical records is streamlined and efficient. The hierarchical structure allows easy tracing of any changes or updates, fostering accountability within the system.

By reducing the resources needed for data verification and lowering the risk of data corruption, Merkle Trees can significantly cut down on operational costs, making them a cost-effective solution for ensuring data integrity.

Links to Discuss, RFC or previous Issues and PRs

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Links to examples and research

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Current state

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Solution proposal

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Additional Information

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dgraph-io / dgraph