Snowflake cloud database with ELT (Airflow+ Python+ Talend)

Learn to integrate ETL tools Snowflake & leverage Airflow ELT with snowflake.Process 250+ GB data.ELT flow. | Snowflake cloud database with ELT.

What you’ll learn

Snowflake cloud database with ELT(Airflow + Python + Talend)

Leveraging snowflake cloud data warehouse using Talend.
Talend basics.
Airflow basics.
Building airflow Dags.
Connect snowflake with python.
Auditing snowflake commands.
Capturing cost and performance metrics.
Virtual machine with preconfigured airflow.
End to End project to load Nyc traffic data(250 + GB ).

Requirements for Snowflake cloud database with ELT.

This course will use virtual box software.
A disk space of ~30 Gb is required to run the virtual machine.

Description for Snowflake cloud database with ELT.

In our previous course Snowflake Masterclass [Real time demos+Best practices+Labs] we deep-dived and understood the fundamentals of snowflake, solved a lot of assignments, and understood best practices to load data and unload data. Also, we closely evaluated most of the snowflake features understanding how they work under the hood. Through these discussions, you realized how to use Snowflake efficiently.

There was one missing piece, How to build and orchestrate ETL workflows on Snowflake. This course is just about that.

In this course, we are going to learn,

Build workflows in Airflow.
We will leverage Talend capabilities to build generic code to ingest data and process data in snowflake.
We will build audit tables and record every command we fire on the snowflake. Record the time consumed for each task and capture snowflake credits.
Once we build the framework we will build a workflow to process and transform 250 + GB volume of NYC traffic data.
At last, we will connect the Snowflake with python and write code to capture stats of data we loaded to the snowflake.
you will also get access to preconfigured Jupyter notebook to run your python code on the Snowflake.