What truly makes an article persuasive is not the fancy title, but the ability to clearly explain the actions. Chainbase provides me with three layers: address-level profiling, capital flow, and contract interaction paths. My most commonly used template is to link the four tables: address → assets → transactions → interactions, creating a 'time-sliced multi-chain profile.' The API can pull common fields together at once, while SQL is used for joins and aggregations.
Taking the old issue of 'the authenticity of popularity,' I will do three things:
First, activity and retention. Create a queue for daily active users, new users, and returning users to see if there is a cliff one week after the activity.
Secondly, funds actually arriving. By comparing bridge inflow amounts, trading pair depth, and LP changes, we can distinguish whether it is just 'loud noise without money coming in.'
Thirdly, ecological diffusion. Look at the diversity of contract calls to see if it has expanded from a single 'claim interface' to 'real business entry points.'
All of this can be completed in one console, without the need to piece things together.
I will also conduct a reverse falsification: if popularity relies on manipulation, there will be 'repeated patterns, similar amounts, and similar interaction trajectories from the same address family' on-chain; this can be quickly captured at the decoding layer. Previously, I used this method to identify a cluster of abnormal addresses in a certain activity, and the authorities later issued a risk warning. With data, paths, and verification, readers naturally won't complain.
Next, let's talk about data output. The platform allows results to be synced to its own warehouse/object storage, which is crucial for me to create a 'weekly leaderboard.' I will store last week's profile snapshot in S3 and then compare it with this week's changes; the charts in the article are not just temporarily pulled but have historical references. The sync targets are listed on the platform page, covering most common warehouses.
Regarding pricing strategy, I suggest content creators use 'query whitelist + sampling pre-run' to control volume:
— Solidify commonly used templates to avoid scanning the entire chain each time.
— First run a 10% sample to observe trends, then run the full dataset to generate the chart.
— High-frequency monitoring is handled by streaming, while low-frequency reviews are handled by SQL.
The platform has tiered packages, which are quite friendly for individual authors, allowing them to save money.
Lastly, I want to mention something that is also important for the industry: data integrity and provability. The document mentions that they have considered data proof and lake-warehouse storage at the architectural level, designing 'access interfaces, storage layers, and proof paradigms' together. For content creation, this means I am not 'citing a black box,' but can show everyone the chain of 'caliber—query—result—snapshot.'
@Chainbase Official #Chainbase $C
