Immutable Data Storage
A universal solution to record and store any critical and essential data used for evidentiary purposes as exhibit of specific events. Blockchain guarantees a tamper-proof record of data, further enabling an undisputed audit trail.
WHY THIS IS SO IMPORTANT?
Immutable data is a piece of information that cannot be deleted or modified. Most traditional databases store data in a mutable format, meaning the database overwrites the older data when new data is available.
In contrast, with blockchain, data will not be overwritten when new information becomes available. Blocks of data are linked together to form a continuous, chronological chain of unchanging records. The way data is secured and stored in blockchain is highly secure and useful for any audit or legal evidence purposes.
BENEFITS
Faster Operations
When data is stored as immutable, new data is appended to the previous one, along with a timestamp. This means the database can simply insert data without blocking the system to perform integrity checks. Data arrives continuously with minimum latency.
Faster Operations
When data is stored as immutable, new data is appended to the previous one, along with a timestamp. This means the database can simply insert data without blocking the system to perform integrity checks. Data arrives continuously with minimum latency.
Compliance
The solution ensures compliance with regulations related to the storage of critical information on so-called durable medium in addition to data authenticity confirmation for the purposes of forensic analysis.
Compliance
The solution ensures compliance with regulations related to the storage of critical information on so-called durable medium in addition to data authenticity confirmation for the purposes of forensic analysis.
Historical Context
Many organizations need to compare historical data with the latest version for better, more contextual analytics. Immutable databases preserve all historical data. It helps to create checkpoints in the past to which a system can be restored.
Historical Context
Many organizations need to compare historical data with the latest version for better, more contextual analytics. Immutable databases preserve all historical data. It helps to create checkpoints in the past to which a system can be restored.
Auditability
Many industries, especially healthcare and financial, might face random audits. It is crucial for them to preserve all the data, so they have the historical data handy if it’s required for an audit.
Auditability
Many industries, especially healthcare and financial, might face random audits. It is crucial for them to preserve all the data, so they have the historical data handy if it’s required for an audit.
HOW IT WORKS
Each company which is issuing data on blockchain holds a publishing node with additional publishing components like publishing application. It allows for recording, storage and retrieval of data in any formats, not only text. We provide easy and fast integration through REST or SOAP API
KEY FEATURES
Linkage of immutable DLT records with both on-chain, as well, as off-chain data supported permissioned onboarding process, users authentication and roles management provide flexibility during the use case modeling.
Linkage of immutable DLT records with both on-chain, as well, as off-chain data supported permissioned onboarding process, users authentication and roles management provide flexibility during the use case modeling.
The solution’s architecture is optimized to process many transactions simultaneously, with a highly scalable network
The solution’s architecture is optimized to process many transactions simultaneously, with a highly scalable network
History of the operations and events is captured chronologically, and it stays unchangeable and tamper-proof
History of the operations and events is captured chronologically, and it stays unchangeable and tamper-proof
The data privacy is assured by the DLT mechanisms of encryption and massive data decomposition
The data privacy is assured by the DLT mechanisms of encryption and massive data decomposition
