> For the complete documentation index, see [llms.txt](https://datatie.gitbook.io/datatie-whitepaper/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://datatie.gitbook.io/datatie-whitepaper/use-cases/long-term-data-availability.md). # Long-Term Data Availability DataTie effectively tackles the challenge of storing a substantial volume of dynamic data that surpasses the capacity of a single node, ensuring long-term data availability on the network. To achieve this, DataTie leverages dynamic data sharding and replication techniques. When data exceeds the capacity of a single node, it is partitioned into multiple shards. Each shard is then replicated on multiple physical disks, ensuring redundancy and preventing data loss. This distributed approach enables DataTie to handle large dynamic datasets, with the network's capacity potentially reaching tens or even hundreds of terabytes or petabytes. By combining dynamic data sharding and replication, DataTie ensures that data is stored and maintained across the network, even if individual nodes experience failures or go offline. This decentralized approach enhances data availability and resilience, as there are multiple replicas of the data distributed across different nodes. This long-term data availability is crucial for applications that rely on continuous access to historical data. Financial systems, for example, can benefit from DataTie by securely storing transaction histories, enabling accurate auditing and analysis. Supply chain management applications can leverage DataTie to store and access detailed records of product movements, ensuring transparency and traceability. Decentralized analytics platforms can utilize DataTie to store vast amounts of data for analysis and machine learning purposes, enabling insights and predictions based on historical information. DataTie's dynamic data sharding and replication mechanisms address the challenge of storing a large amount of dynamic data that surpasses the capacity of a single node. By ensuring long-term data availability on the network, DataTie opens up possibilities for various applications that rely on continuous access to historical data, such as financial systems, supply chain management, and decentralized analytics platforms. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://datatie.gitbook.io/datatie-whitepaper/use-cases/long-term-data-availability.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.