with df as ( SELECT date_trunc('day', DATE) as DATE, CASE WHEN date >= '2022-01-01' and date < '2022-02-01' then 602419253 WHEN date >= '2022-02-01' and date < '2022-03-01' then 630769212 WHEN date >= '2022-03-01' and date < '2022-04-01' then 643424273 WHEN date >= '2022-04-01' and date < '2022-05-01' then 660441212 WHEN date >= '2022-05-01' and date < '2022-06-01' then 676076112 WHEN date >= '2022-06-01' and date < '2022-07-01' then 703648478 WHEN date >= '2022-07-01' and date < '2022-08-01' then 725911205 WHEN date >= '2022-08-01' and date < '2022-09-01' then 751093478 WHEN date >= '2022-09-01' and date < '2022-10-01' then 775580101 WHEN date >= '2022-10-01' and date < '2022-11-01' then 799891321 WHEN date >= '2022-11-01' and date < '2022-12-01' then 818414853 ELSE 840000000 end as "Circulating Supply", CASE WHEN date >= '2022-01-01' and date < '2022-02-01' then 1060814159 WHEN date >= '2022-02-01' and date < '2022-03-01' then 1065138316 WHEN date >= '2022-03-01' and date < '2022-04-01' then 1069224674 WHEN date >= '2022-04-01' and date < '2022-05-01' then 1073691631 WHEN date >= '2022-05-01' and date < '2022-06-01' then 1078103266 WHEN date >= '2022-06-01' and date < '2022-07-01' then 1082597192 WHEN date >= '2022-07-01' and date < '2022-08-01' then 1086886128 WHEN date >= '2022-08-01' and date < '2022-09-01' then 1091577068 WHEN date >= '2022-09-01' and date < '2022-10-01' then 1096208106 WHEN date >= '2022-10-01' and date < '2022-11-01' then 1100679711 WHEN date >= '2022-11-01' and date < '2022-12-01' then 1105196106 ELSE 1110000000 end as "Total Supply" from near.core.metrics_active_wallets where date >= '2022-01-01' group by 1,2,3), prices as ( select date_trunc('day', TIMESTAMP) as DATE, avg(PRICE_USD) as "Price USD" from near.core.fact_prices where date >= '2022-01-01' and SYMBOL = 'wNEAR' group by 1), tabl as ( select date_trunc('day', BLOCK_TIMESTAMP) as DATE, count(DISTINCT TX_SIGNER) as Users, count(DISTINCT TX_HASH) as Tx_Count from near.core.fact_transactions where date >= '2022-01-01' and tx_status = 'Success' group by 1) select df.date, "Circulating Supply", "Total Supply", "Price USD", Users, Tx_count from df left join prices on df.date=prices.date join tabl on df.date=tabl.date

NEAR Mega Dashboard Architecture