Performance Improvement for Real time Updates from RDS to Snowflake - snowflake-cloud-data-platform

Question

As a part of an hourly process, we source data from a table in RDS and ingest into the raw data layer Snowflake.
After ingesting, we have to update certain fields in the final mart table based on new data ingested.
This mart table is a very wide table with more than 100 fields and the size of the table is around 10 GB.
So these updates are taking 6-7 minutes.
Edit: Update Query and Explain Plan:
update <target_table> tgt
set email_id =src.email_id,
first_name = (
case
when len(src.updated_first_name) < 2 then first_name
when src.updated_first_name is null or src.updated_first_name = '' then first_name
else src.updated_first_name
end ),
last_name = (
case
when len(src.updated_last_name) < 2 then last_name
when src.updated_last_name is null or src.updated_last_name =
'' then last_name
else src.updated_last_name
end),
link = src._link,
title = src.updated_title,
is_verified = 1,
last_verified = src.verified_time,
last_active_type_update = src.verified_time,
active_type = 'ENGAGED',
title_id = src.title_id,
function = src.function,
role = src.role
from <sourcetable> src
where lower(tgt.email_id) = lower(src.email_id)
and src.demographic_disposition in ('Updated Last Name','Updated
Title','Updated First Name','Verified','Updated
Country','Partial
Verify')
and src.verified_time > '<last_runtime>';
Explain Plan:
{
"GlobalStats": {
"partitionsTotal": 728,
"partitionsAssigned": 486,
"bytesAssigned": 9182406144
},
"Operations": [
[
{
"id": 0,
"operation": "Result",
"expressions": [
"number of rows updated",
"number of multi-joined rows updated"
]
},
{
"id": 1,
"parent": 0,
"operation": "Update",
"objects": [
"<target_table>"
]
},
{
"id": 2,
"parent": 1,
"operation": "InnerJoin",
"expressions": [
"joinKey: (LOWER(src.EMAIL_ID) = LOWER(tgt.EMAIL_ID))"
]
},
{
"id": 3,
"parent": 2,
"operation": "Filter",
"expressions": [
"(src.DEMOGRAPHIC_DISPOSITION IN 'Updated Last Name' IN 'Updated Job Title' IN 'Updated First Name' IN 'Verified' IN 'Updated Country' IN 'Partial Verify') AND (src.VERIFIED_TIME > '<last_runtime>') AND (LOWER(src.EMAIL_ID) IS NOT NULL)"
]
},
{
"id": 4,
"parent": 3,
"operation": "TableScan",
"objects": [
"<src_table>"
],
"expressions": [
"EMAIL_ID",
"LINK",
"UPDATED_FIRST_NAME",
"UPDATED_LAST_NAME",
"UPDATED_TITLE",
"DEMOGRAPHIC_DISPOSITION",
"VERIFIED_TIME",
"FUNCTION",
"ROLE",
"TITLE_ID"
],
"alias": "BH",
"partitionsAssigned": 1,
"partitionsTotal": 243,
"bytesAssigned": 1040384
},
{
"id": 5,
"parent": 2,
"operation": "Filter",
"expressions": [
"LOWER(tgt.EMAIL_ID) IS NOT NULL"
]
},
{
"id": 6,
"parent": 5,
"operation": "JoinFilter",
"expressions": [
"joinKey: (LOWER(src.EMAIL_ID) = LOWER(tgt.EMAIL_ID))"
]
},
{
"id": 7,
"parent": 6,
"operation": "TableScan",
"objects": [
"<target_table>"
],
"expressions": [
"EMAIL_ID",
"FIRST_NAME",
"LAST_NAME"
],
"partitionsAssigned": 485,
"partitionsTotal": 485,
"bytesAssigned": 9181365760
}
]
]
}
Now the new requirement is that this update jobs run every 5 minutes so that we can have near real time updated data in Snowflake.
However, we have tried all query optimisation and are unable to bring the execution time of the updates to less than 5 minutes.
We are using a Small warehouse in Snowflake and due to budget constraints we cant increase it further to fasten the update query performance.
Is their any other budget friendly way to do near real time updates(after ingesting the data) in Snowflake?