SleekData

  Hello Data Pros, and welcome back to another exciting episode of our Snowflake learning series! In our previous video, we discussed various options for ingesting data into Snowflake, including Tasks, Snowpipe, Snowpipe Streaming, and the Snowflake Connector for Kafka. Today, we’re diving into the world of data transformations, with a special focus on Dynamic Tables. Let’s jump right in!   -----Follow the link in the description for the SQLs used in this lab ------ --------------------------------------------------------------------------- ---------------- Setting Up Foundational Objects for the Lab--------------- --------------------------------------------------------------------------- -- Create the database CREATE DATABASE IF NOT EXISTS my_database ; -- Use the created database USE DATABASE my_database ; -- Create the schema CREATE SCHEMA IF NOT EXISTS my_schema ; -- Use the created schema USE SCHEMA my_schema ; -- Create the 'customers' table CREATE TABLE

from airflow import DAG from airflow.utils.dates import days_ago from airflow.providers.amazon.aws.sensors.s3 import S3KeySensor from airflow.providers.snowflake.transfers.copy_into_snowflake import CopyFromExternalStageToSnowflakeOperator dag = DAG(     's3_to_snowflake_dag' ,     default_args = { 'start_date' : days_ago( 1 )},     schedule_interval = '0 23 * * *' ,     catchup = False ) # Wait for the file in S3 wait_for_file = S3KeySensor(     task_id = 'wait_for_s3_file' ,     bucket_name = 'sleekdata' ,     bucket_key = 'oms/employee_details.csv' ,     aws_conn_id = 'aws_conn' ,     poke_interval = 10 ,     timeout = 60 * 60 * 5 ,     soft_fail = True ,     deferrable = True ,     dag = dag ) # Load the file from S3 to Snowflake load_table = CopyFromExternalStageToSnowflakeOperator(     task_id = "load_s3_file_to_table" ,     snowflake_conn_id = "snowflake_conn" ,     files = [ "e

  Hello Data Pros, and welcome back to another exciting episode of our Snowflake learning series! In our previous videos, we explored Snowflake's storage and processing layers, covering key concepts along the way! Now, let's shift our focus to the Cloud Services Layer - the mastermind behind everything that happens in Snowflake! --All commands and sample csv file in the video description-- create database SLEEKDATA ; create schema OMS ; create stage MY_STAGE ; create table MY_TABLE ( ID int , Name varchar ( 100 ), Age int , Region varchar ( 10 ) ); -- Run put command in SNOWSQL CLI -- put file://D:/mydata.csv @MY_STAGE; copy into MY_TABLE from @ MY_STAGE / mydata . csv . gz ; select * from MY_TABLE mydata.csv 1 , Alice , 25 , US 2 , Bob , 30 , EU 3 , Charlie , 28 , AP 4 , David , 35 , US 5 , Emma , 40 , EU 6 , Frank , 32 , AP 7 , Grace , 27 , US 8 , Henry , 45 , EU 9 , Ivan , 50 , AP 10 , Jasmine , 23 , US

Hello Data Pros, and welcome back to another exciting episode of our Snowflake learning series! In our previous video, we covered Snowflake Tasks, and demonstrated how you can automate processes within Snowflake. Today, we will explore  Snowpipes , a powerful feature in Snowflake that offers near real-time data ingestion. -----Follow the link in the description for the SQLs used in this lab ------ --------------------------------------------------------------------------- ---------------- Setting Up Foundational Objects for the Lab--------------- --------------------------------------------------------------------------- --Create Database and Schema CREATE OR REPLACE DATABASE order_db ; CREATE OR REPLACE SCHEMA order_db . order_schema ; -- Create Tables USE SCHEMA order_db . order_schema ; CREATE or replace TABLE order_raw (     order_id INT ,     order_date DATE ,     cust_fname VARCHAR ( 50 ),     cust_lname VARCHAR ( 50 ),     product_id INT ,     quantity INT ,