Paweł Bogumiło bogumilo

Original standard exclusions from Arq Backup:

.DocumentRevisions-V100
.MobileBackups
.MobileBackups.trash
.Spotlight-V100
.TemporaryItems
.Trash
.Trashes
.dbfseventsd

✅ 1. Import input.csv into any SQL database

✅ 2. Check transformations.xls schema description. Left columns contains description of input data. Right columns contains description of expected output data.

✅ 3. Provide SQL query, which will read input data, make requested transformations and show calculated output.

I have chosen BigQuery as my database of choice. Fields formatted '12/2/2016' are NOT accepted by the database as DATE data type field as suggested in the instruction. I used workaround and table with instructed Input Feed Schema and data types has been placed in staging folder: models/staging/stg_customer_details.sql

Create GCP bucket and copy input.csv file there:

	with
	w_prev_transaction as (
	select
	*,
	lag(transaction_ts) over( partition by marketplace_merchant_id, credit_card_id, amount
	order by transaction_ts
	) as prev_transaction_ts
	from transactions
	)
	select

	select
	concat(p1.accessory_name,',', p2.accessory_name,',',p3.accessory_name,',',p4.accessory_name) as car_equipment_version,
	p1.feature_cost + p2.feature_cost + p3.feature_cost + p4.feature_cost as total_cost
	from car_features p1
	inner join car_features p2
	on p1.accessory_name < p2.accessory_name
	inner join car_features p3
	on p2.accessory_name < p3.accessory_name
	inner join car_features p4
	on p3.accessory_name < p4.accessory_name

	with
	w_first_purchase as (
	select
	u.user_id,
	u.signup_date,
	first(o.order_date) over (
	partition by o.user_id order by o.order_date
	) as first_order_date
	from users u
	join orders o using ( user_id )

	with
	calls_by_customer as (
	select customer_id, count(call_id) as calls_cnt from callers group by 1
	)
	select count(*) as customer_count
	from calls_by_customer
	where calls_cnt > 2

	with
	non_categorised as (
	select
	*,
	case
	when call_category in ('payments', 'deliveries', 'orders', 'website')
	then 0
	else 1
	end as na_flag
	from callers

	with
	count_top10_by_author as (
	select
	author_name, row_number() over (partition by author_id) as appearance_count
	from global_book_rank
	left join books using (book_id)
	left join authors using (author_id)
	where global_book_rank.rank < 11
	),
	top_authors_appearance as (

	select
	round(avg(calls_count), 0) as mean,
	mode() within group (order by calls_count) as mode,
	percentile_cont(0.5) within group (order by calls_count) as median
	from customer_service_stats

	with
	with_previous_call as (
	select
	c.*,
	lag(call_received) over (
	partition by customer_id order by call_received
	) as previous_call
	from callers c
	),
	callers_flag as (