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Snowflake is a powerful enterprise Software as a Service (SaaS) offering, and like all SaaS technologies, it is particularly important to administer it correctly, from the perspective of both security and access control and of monitoring and attributing resource usage. Otherwise, costs and security vulnerabilities can quickly pile up as users, roles, and accounts increasingly multiply. In this course, discover how to manage the accounts and users within an organization. Then, explore how Snowflake allows users to configure various properties on objects, using parameters. Next, use resource monitors to impose limits on the number of credits consumed by Snowflake and examine what happens if that limit is breached. Learn how to configure single sign-on using Otka and implement federated authentication. Finally, perform data masking and investigate how Snowflake allows you to redact and censor data at the column level using masking polices.

Data is generally processed using a batch or stream methodology depending on how much time between data generation and processing is acceptable. The Snowflake feature Snowpipes processes data in micro-batches which fall in between these two scenarios. In this course, you will cover the implementation of Snowpipes when data is sourced from an internal Snowflake stage. You will kick things off by looking at data ingestion options in Snowflake from a theoretical standpoint, including the differences between bulk data loading and Snowpipes. Then, you get hands-on to set up the infrastructure for data ingestion: an internal stage for CSV data, a destination table for a data load, and a pipe to carry out the load in micro-batches. Next, you will ingest the data into the destination table and explore how this process can be monitored by tracking the pipe status. Finally, you will implement a Snowflake task to trigger a Snowpipe at regular time intervals.

The Snowpipe feature allows Snowflake to input micro-batches of data as it becomes available, generally within minutes of the data being added to a stage and submitted for ingestion. In this course, you will implement the auto-ingestion of CSV files from external Snowflake stages located on the AWS and Azure cloud platforms. You will begin by setting up a continuous data ingestion pipeline where the data source is located in an Azure Storage Container. This pipeline will include several components, such as queues, enterprise applications, and storage integrations as well as the permissions required to get these pieces to talk to one another. You will then implement something similar with an Amazon S3 bucket as the source of data. This set-up will involve AWS services such as IAM roles, SNS topics, as well as Snowflake objects such as notification integrations and pipes.

In the traditional approach to data sharing, consumers receive a copy of the provider data, which can allow for the creation of multiple copies of data. Secure Data Sharing in Snowflake enables the secure sharing of specific database objects without data duplication between accounts. In this course, explore the motivations behind Secure Data Sharing and its implementation, as well as how to create and share a Snowflake share. Next, learn how to use Snowflake Marketplace data, the specific permissions required to create and manage shares, and how to set permissions using queries. Finally, practice implementing cross-region data replication to enable Secure Data Sharing across cloud and geographical boundaries. Upon completion, you'll be able to securely share Snowflake objects with other Snowflake users.