Snowflake Masterclass[Cortex AI+demos+Best practices+Labs]

This course is the result of my over 4 years of experience working with Snowflake. Snowflake is a fantastic tool to handle huge volumes of data. You can load, query and export, and run transactional queries on TB's and PB's of data easily.

You don't have to worry about infrastructure setup, security, performance, tuning, Garbage collection, node failures etc etc...

if you have already worked with Spark with EMR or with Data bricks you will be amazed by snowflake performance and simplicity.

But there is a catch, if you are not fully aware of how things works in snowflake, you will easily loose control and end up writing costly queries and end up using many of snowflake features wrongly.

I mentioned costly queries because  snowflake is build for performance.

Costly queries and wrong design decisions in snowflake will sky rocket your snowflake bill. I have made lot of such mistakes and learned from those mistakes.

This course is an effort to walk you through such experiences so that you don't end up doing same mistakes which i did!!!

Few things can only be learned by experience you can't find them in documentations this course will expose such details to you.

Hope you join me in this journey

High level overview of all modules with sections details is given below,

In module#1,  

    > Snowflake environment setup.

    > Snowflake architecture.

    > Snowflake caching , micro-partitions, clusters.

    > Snowflake clustering deep dive.

    > Snowflake virtual warehouse layer.

    > Performance tuning and DB characteristics.

In module#2,

    > Loading data in snowflake.

    > Loading data using internal stage.

    > Load data from aws s3.

    > Load data from google gcs.

    > Load data from Azure.

    > Copy command options.

    > Working with copy command.

    > Assignments.

    > Loading unstructured data

In module#3,

   > Snowpipe in snowflake.

   > Data sharing in snowflake.

   > Time travel.

   > Fail safe.

   > Clone feature.

   > Data sampling.

   > Tasks in snowflake.

   > Streaming in snowflake.

   > Continuous data load.

   > Materialized views.