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Just a curious question in my mind and I thought of asking to Snowflake experts to clarify this question. We know that Snowflake default isolation level is read committed; I have one transaction let us A in which I am truncating data from Table T1 and Loading the Table T1 using transformed fresh data; at the same time I have another transaction say B is trying to read the data from Table T1 while getting this data truncated in transaction A; would I be able read the data from Table T1 in transaction B which it is still getting truncated in another transaction A.

My mind says yes; transaction B should be able to read it from Table T1 because transaction A still in progress and not yet committed.

I want to know if an object has been in the same location for >8 hours. Any ideas how to derive that from this data sample? Thx

ObjectID 23 should be returned because it was in the same location >8 hours

ObjectID 24 should not be returned. It may have been in the same location >8 hours, but based on our data we cannot be sure.

ObjectID 24 should not be returned. The 12:00 & 23:00 locations are the same, but the object was somewhere else in between (16:00).

Update: This is in Snowflake

I'm trying to make a complete copy of a Snowflake DB into PostgreSQL DB (every table/view, every row). I don't know the best way to go about accomplishing this. I've tried using a package called pipelinewise , but I could not get the access needed to convert a snowflake view to a postgreSQL table (it needs a unique id). Long story short it just would not work for me.

I've now moved on to using the snowflake-sqlalchemy package. So, I'm wondering what is the best way to just make a complete copy of the entire DB. Is it necessary to make a model for each table, because this is a big DB? I'm new to SQL alchemy in general, so I don't know exactly where to start. My guess is with reflections , but when I try the example below I'm not getting any results.

I have the following table in a Snowflake data warehouse:

I need to be able to count the number of unique Store_ID for each Client_ID in order of Appointment_Date. Something like following is my desired output:

Where I would be actively counting the number of distinct stores a client visits over time. I've tried:

I am trying to find a way for connection pool management for external connections created in Airflow.
Airflow version : 2.1.0
Python Version : 3.9.5
Airflow DB : SQLite
External connections created : MySQL and Snowflake

I know there are properties in airflow.cfg file

I'm trying to read data from snowflake database table into databricks. Below is my code:

I have a list: reward_coupons, which is a list that can range in length from 1- 9 and contain reward IDs.

I have a table (which I call from an identifier table name) which contains 9 columns named reward_id_01,reward_id_02...reward_id_09.

In my reward_coupons list, customers can recieve 1 up to 9 rewards, so I would like to create a loop which inserts the values I have in my list(in order) to the table (identifier ($table_name))

I am working on Snowflake, need to substract 2 hours from specifc date:

date time: 2021-06-10 14:07:04.848 -0400

'2021-06-10 14:07:04.848 -0400' - 2 hours

expected result: 2021-06-10 12:07:04.848 -0400 (now it's twelve o'clock).

Datediff didn't work:

I have a table ADS in snowflake like so (data is being inserted each day), note there are duplicates entries on rows 3 and 4:

I want to select all entries based on ID with the max REPORT_DATE - essentially I want to know the latest number of CLICKS and IMPRESSIONS for each ID:

This query successfully gives me the max DATE for each ID: