Time zone definition for streaming Google Analytics data into BigQuery

I'm streaming Google Analytics Data 360 into Big Query through two different views (and two different databases). I've set the streaming settings for both of my views the same way, but in Big Query:

First sight

Time Zone Country or Territory Setting: (GMT + 01: 00) Italian time

Large query: last partition available -> 2 days ago (created on 22/11/2018, 15:39:54 clock)

Second view

Time Zone Country or Territory Setting: (GMT + 01: 00) Italian time

Big query: last partition available -> yesterday (11/23/2018, 06:49:26 PM)

Why are the streaming times so different? How can I force the same streaming time?

To shorten the runtime of a bigquery query

I have two tables:

1. PEOPLE (PK, name, address, postal code, <>)
2. EMAIL (PK, name, address, postal code, e-mail)

This is a one-to-many table in which they are associated with name, address, and zip code.

What I need is:

PEOPLE (PK, name, address, postal code, <>, FK_Email1, Email1, FK_Email2, Email2, FK_Email3, Email3)

What I have so far is this:

#standardSQL
SELECT a.PK, a.FK, Source, First Name, Last Name, Middle Name, Suffix Name, Gender, Age, Date of Birth, Address, Address2, City, State, Zip Code, Zip4, Cleaned_HouseNumber, Cleaned_Street, Cleaned_County, Cleaned_State, Cleaned_State, TimeZone, Income, HomeValue, NetWorth, Marital Status, IsRenter, HasChildren, CreditRating, Investor, LinesOfCredit, InvestorRealEstate, Traveler, Pets, MailResponder, Charity, PoliticalParty, ATTOM_ID, GEOID, Length, Length, HomeLoan1Amount, HomeLoan2Amount, HomeValueRangeCode, HomeValueRangeText, HomeMarketValue, HomeAssessedValue, HomeLoanToValue, HomeSQFT, HomeLotSQFT, Home Year Built, HomePurchaseDate, HomeLoan1Date, HomeLoan2Date, Home Parcel Number, Home Property Type, DNC, HomeCompanyOwned, HomeTrustOwned, HomeOwnerOccupied, Home Type, Home pool, home garage, Home Heating, Home Cooling, HomeBedrooms, home bathroom, HomeNumberOfUnits, mailing address, mailing City, mailing State, MailingZip, MailingZip4, Married, Sch Education, Profession, Ethnicity, LANGUAGE, RELIGION,
FK_Email[SAFE_ORDINAL(1)] FK_Email1, emails[SAFE_ORDINAL(1)] Email1, FK_Email[SAFE_ORDINAL(2)] as FK_Email2, emails[SAFE_ORDINAL(2)] Email2, FK_Email[SAFE_ORDINAL(3)] as FK_Email3, emails[SAFE_ORDINAL(3)] Email3
FROM (
CHOOSE
P.PK, P.FK, P.Source, P. FirstName, P.LastName, MiddleName, SuffixName, Gender, Age, Date of Birth, P.Address, Address2, P.City, P.State, P.Zip, Zip4, Cleaned_HouseNumber, Cleaned_Street, Cleaned_Stadt, Cleaned_Cachy, Cleaned_Start, Cleaned_Status, TimeZone, Income, Channel, Lead Time, Lead Time, Eworth, Lead Time, Lead Time, Lead Time, Email Address, Lead Time, Email Address, Lead Time, E-Mail Address, lead time, e-mail address. SCORE, width, length, SpouseFirstName, SpouseLastName, HomeAvailableHomeEquity, HomeTotalLoans, HomeLoan1Amount, HomeLoan2Amount, Home Value Range Code, Home Value Range Text, Home Market Value, HomeAssessedValue, HomeLoanToValue, HomeSQFT, HomeLotSQFT, Home Year Built, HomePurchaseDate, HomeLoan1Date, HomeLoan2Date, Home Parcel Number, Home Property Type, DNC, HomeCompanyOwned, HomeTrustOwned, HomeownerOccupied, HomeType, HomePool, HomeGarage, HomeHeating, HomeCooling, HomeBedroom, HomeBathroom, HomeNumberOfUnits, MailingAddress, Mail ingCity, MailingState, MailingZip, MailingZip4, Married, Divorce, Education, Profession, Ethnicity, LANGUAGE, RELIGION
, ARRAY_AGG (E.Email) emails, ARRAY_AGG (E.PK) FK_Email
FROM `db.ds.table1` P
left Participate `db.ds.table2` E
ON P.FirstName = E.FirstName
AND P.LastName = E.LastName
AND P.Address = E.Address
AND P.Zip = E.Zip
From 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25 , 26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,44,45,46,47,48,49 , 50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74 , 75,76,77,78,79,80,81,82,83,84,85,86,87
) on

My problem is that this is beyond the timeout of six.
Is there anything there to do that faster?

Many Thanks!