One of the major expenses of Web3 is the requirement that historical data be rebuilt on a regular basis. The monitoring tools, dashboards, as well as analytics platforms, must index the same events, logs, and metadata independently. History has to be constructed again when indexers are lost or forked or moved. This waste retards insights, divides analysis and causes people to over rely on centralized data providers.
Walrus resolves this through data availability in blobs. It enables large datasets in the history to be stored offchain yet remains consistently available overtime.
This particularly comes in handy with cross-chain analytics.
The majority of tools now directly read the chain and store private offchain databases. Restoring history can be several days or weeks in case of an indexer failure or loss of data. In the process, analytics are lost or made untrustworthy. Incomplete views are then depended on by researchers.
You do not have to reconstruct history with Walrus again and again.
Here’s a typical workflow.
A chain, rollup or app discards its past information- event logs, state snapshots or summed metrics.
Walrus stores which store such data as a blob and give it an availability window.
The same blob is then indicated by other analytics tools rather than being rebuilt by the analytics themselves.
The principal benefit is that history is not an independent liability per platform.
It saves money right away. Re-indexing is very compute intensive, storage intensive, and time intensive. A single storage and reuse of the data helps the teams in their analysis and not in rebuilding the data. Big providers enjoy the same context as small teams.
It also maintains uniformity of data. Errors reduce when numerous tools are using a common dataset. Analysts cease the debate on whose index is correct and operate out of a single reference. Walrus ensures that the data remains available when required.
Another enormous advantage is cross-comparison. With the division of the ecosystems, the observation of what is happening across the chains requires a common historical perspective. Walrus allows datasets of other chains to be stored and read without regard to the execution layer. Analytics does not have to create an indexing system on each chain.
Time limits are key. Not everything should remain permanent. There are sets that are significant during a research period or a market cycle. Walrus allows teams to determine the duration of availability of data, and thus costs are in line with actual analytical value.
Transparency also improves. The same historical data will be accessible to researchers and auditors without private APIs or permissioned systems. This simplifies the task of independent checks and dispersion of insight.
Walrus is not an analytics company. It is not interpreting data or creating dashboards. It only publishes historical information that is reliable. This competitiveness and strength of analytics is maintained.
I believe that Web3 analytics is not moving, not due to the lack of data, but as a result of re-creating the same history. That wastage favors the central providers, at the expense of everybody else. Walrus is the transformation of data into common infrastructure, rather than redundant work.
Walrus allows teams to be faster, compare better and trust results more by anchoring analytics to a trusted layer. It might not be glamorous, but it will eliminate one of the real bottlenecks in almost every serious analytics project in the current day.
