Snowflake is the next big thing, and it is becoming a full-blown data ecosystem. With the level of scalability and efficiency in handling massive volumes of data and also with several new concepts in it, this is the right time to wrap your head around Snowflake and have it in your toolkit. This course not only covers the core features of Snowflake but also teaches you how to deploy Python/PySpark jobs in AWS Glue and Airflow that communicate with Snowflake, which is one of the most important aspects of building pipelines. In this course, you will look at Snowflake, and then the most crucial aspects of Snowflake in an efficient manner. You will be writing Python/Spark Jobs in AWS Glue Jobs for data transformation and seeing real-time streaming using Kafka and Snowflake. You will be interacting with external functions and use cases, and see the security features in Snowflake. Finally, you will look at Snowpark and explore how it can be used for data pipelines and data science. By the end of this course, you will have learned about Snowflake and Snowpark, and learned how to build and architect data pipelines using AWS. You need to have an active AWS account in order to perform the sections related to Python and PySpark. For the rest of the course, a free trial Snowflake account should suffice.