Athena federated query executing about 40 concurrent lambdas for one query which is exhausting AWS RDS connections

I initially saw this error when trying to load up one of our Grafana dashboards, but it looks like Athena is invoking a lot of concurrent lambdas for queries which is exhausting our RDS connections. I tried to run a query directly in Athena, and I am seeing the error. Here is the error I see

GENERIC_USER_ERROR: com.google.common.util.concurrent.UncheckedExecutionException: io.trino.spi.TrinoException: Encountered an exception[com.zaxxer.hikari.pool.HikariPool$PoolInitializationException] from your LambdaFunction[arn:aws:lambda:us-west-1:----:function:athenafederatedcatalog_flight_data_prod] executed in context[retrieving meta-data] with message[Failed to initialize pool: FATAL: remaining connection slots are reserved for non-replication superuser, rds_reserved, and rds_superuser connections]
This query ran against the "public" database, unless qualified by the query. Please post the error message on our forum  or contact customer support  with Query Id: b92cd5ca-e660-4e7c-843e-56f70fc23182

Here is my query

 -- This is a standalone version of the query for testing directly in Athena.
-- All Grafana macros have been replaced with static values.

-- CTEs to get historical flight and warning data
WITH total_flights AS (
    SELECT 
        date_trunc('day', flight_timestamp) AS flight_timestamp, 
        count(distinct flight_uuid) AS total_flights 
    FROM flight 
    -- Replaced $__dateFilter with a static date range
    WHERE date_trunc('day', flight_timestamp) BETWEEN DATE '2025-08-18' AND DATE '2025-08-25'
    GROUP BY 1
),
total_warnings AS (
    SELECT 
        date_trunc('day', flight_warning.start_timestamp) AS warning_timestamp, 
        count(*) AS all_warnings
    FROM (
        SELECT DISTINCT 
            a.flight_uuid, 
            title || ': ' || description AS warning_desc, 
            date_trunc('day', start_timestamp) AS start_timestamp, 
            severity 
        FROM alert a
        JOIN flight f ON from_utf8(to_utf8(a.flight_uuid)) = from_utf8(to_utf8(f.flight_uuid))
        -- Replaced $__dateFilter with a static date range
        WHERE date_trunc('day', start_timestamp) BETWEEN DATE '2025-08-18' AND DATE '2025-08-25'
    ) flight_warning
    GROUP BY 1
),
-- 1. Combine historical data and create our numerical 'x' and 'y' values
historical_data AS (
    SELECT 
        f.flight_timestamp,
        -- Replaced $__timeFrom with a static timestamp
        CAST(date_diff('day', TIMESTAMP '2025-08-18 00:00:00', f.flight_timestamp) AS DOUBLE) AS x,
        COALESCE(round(CAST(w.all_warnings AS DOUBLE) / CAST(f.total_flights AS DOUBLE), 2), 0) AS y -- This is ALL_WARNINGS
    FROM total_flights f
    LEFT JOIN total_warnings w ON f.flight_timestamp = w.warning_timestamp
),
-- 2. Calculate the linear regression parameters (m and b)
regression_params AS (
    SELECT
    regr_intercept(h.y ,h.x) b,
    regr_slope(h.y, h.x) m
    from
    historical_data h
    
),
-- 3. Generate a full series of dates, including future dates
date_series AS (
    SELECT
        CAST(dt AS timestamp) AS full_timestamp
    -- Replaced $__timeFrom and $__timeTo with static timestamps for a week past and a week future
    FROM (SELECT TIMESTAMP '2025-08-18 00:00:00' AS start_date, TIMESTAMP '2025-09-01 00:00:00' AS end_date)
    CROSS JOIN UNNEST(sequence(start_date, end_date, interval '1' day)) AS t(dt)
)
-- 4. Final SELECT: Join historical data and project the trendline
SELECT 
    ds.full_timestamp AS "Timestamp",
    hd.y AS "ALL_WARNINGS", -- Actual historical data
    -- Calculate the trendline value for ALL dates (past and future)
    -- Replaced $__timeFrom with a static timestamp
    rp.m * CAST(date_diff('day', TIMESTAMP '2025-08-18 00:00:00', ds.full_timestamp) AS DOUBLE) + rp.b AS "Trendline"
FROM date_series ds
CROSS JOIN regression_params rp -- Join the calculated m and b to every row
LEFT JOIN historical_data hd ON ds.full_timestamp = hd.flight_timestamp
ORDER BY ds.full_timestamp;

when I was executing this query I am seeing 45 concurrent executions. I am not sure why a single query is generating so many lambdas. Is there something I am doing wrong?