No. SleekRank reads from your data source. If your sheet pulls tier data via a script on a schedule, those flow through on the cache cycle. SleekRank does not scrape bank pages or call APIs directly. The pattern is a separate import job that updates the sheet, and SleekRank renders whatever is current in the source after a cache flush.

Both page groups read from the same MMA sheet. The pairs page group joins two rows at render time using the slug pair from a pairs sheet. A row edit propagates to wherever the account is referenced after the cache cycle, including every pair page where the account is product_a or product_b. The data layer enforces consistency that manual page editing cannot.

Add columns for debit_card_network, debit_card_rewards_rate, and any reward category caps. Render a rewards callout on the per-MMA page via tag mapping, and join on pair pages to compare card programs side by side. A separate page group at /mma/with-rewards/ can filter accounts that include cashback or points.

No. The verdict is whatever is written in the sheet. SleekRank does not write content, it injects content. For longer verdicts that exceed a sheet's column comfort, store them in a separate JSON file keyed by account slug and join at render time. The verdict text is yours, the render layer is the responsibility of SleekRank.

Yes. Map an image URL column to og:image with the meta type. Each MMA page can render a custom social card via that mapping. For dynamic per-account OG images that overlay tiered APY, check-writing flag, and minimum balance over a styled background, pair with SleekPixel which renders OG images from data on the fly.

Update the monthly_check_limit and excess_check_fee columns in the sheet. Every page that references the account, including per-account, every pair page where it appears, and any feature-filtered roll-up, reflects the change after the cache window. Limit changes are quiet and easy to miss on manual reviews, and this is exactly the kind of edit a data layer makes propagate cleanly.

On pair pages, render tier tables for both accounts side by side and compute an effective APY at common balance levels via template logic on the breakpoint arrays. Readers can see at a glance which account pays more at five, twenty-five, or one hundred thousand dollars, which is the question that actually drives the comparison.

Yes. Store a time series of historical tier APYs in a separate JSON file keyed by account slug and render it as a chart via a chart library on the base template. The page reflects current tiers from the main row plus historical context from the side dataset, joined at render time, which helps readers judge consistency over rate cycles.