Snowflake SnowPro Core Certification Preparation - COF-C02

Celebrate completing refreshed sections and preview ongoing improvements, including clearer explanations, modern visuals, and updated demonstrations aligned with the latest Snowflake features and best practices.

Snowflake stores data in micro partitions that are immutable and compressed in a columnar format. It uses min/max ranges and distinct counts to optimize queries and selective column access.

Explore Snowflake pricing with storage costs after compression, per-second virtual warehouse billing with a 60-second startup minimum, and serverless features plus data transfer and cloud services considerations.

Explore Snowflake's hybrid architecture with shared storage and on-demand virtual warehouses, featuring micro partitions, compression, and zero copy cloning for data sharing and performance optimization.

Explore Snowflake connectivity options, including web interfaces, snow scale command line interface, and drivers for Python, .NET, Go, Node.js, and Kafka, plus the modern web UI and Snow CD diagnostics.

Explore the worksheet view in Snowflake, understanding context settings—role, virtual warehouse, database, and schema—and how to write, run, and view query results and errors.

Install SnowSQL on Windows, connect to your Snowflake instance via the command line, and run a sample query to test connectivity using the account name from the Snowflake address.

Explore the modern Snowflake web interface, its redesigned worksheets and dashboards, interactive data analysis, and new administration screens for roles, shares, warehouses, and billing.

Learn data loading and unloading in Snowflake, including bulk and continuous loading with copy and snow pipe, staging concepts, semi-structured data handling, and basic ingestion transformations.

Explore the external stage by connecting Snowflake to cloud storage such as S3 using a storage integration, and learn how credentials and a csv format enable secure data loading.

Load flight detail data from an S3 bucket into a Snowflake table via a named external stage, then load 6298 rows from the csv into the table.

Learn how Snowflake internal stages work, especially named internal stages, to load on-prem data, customize file formats, and share access, while managing storage costs.

Load on-premises data into Snowflake using a named internal stage, create the L2 airport table, and execute a copy command to populate the table from the stage.

Describe table stages and user stages in Snowflake, how they load data into tables, and why they cannot be modified or dropped. Highlight copy command format options and tilde syntax.

Discover how external tables in Snowflake query data stored in external cloud storage, with metadata in Snowflake, read-only access, and optional materialized views to boost performance.

Learn how Snowflake unloads data with the copy command to internal or external stages, exporting to CSV, JSON, or Parquet with automatic compression and configurable encryption.

Use Snowflake’s copy command to perform basic data transformations during load, such as reordering, omitting columns, casting types, and truncating values; complex operations are not supported during ingestion.

Learn how snowpipe enables continuous, serverless micro-batch data loading from external or internal stages, triggered by cloud events or rest APIs, with automatic compute scaling.

Explore continuous data protection in Snowflake, including time travel, drop, and failsafe storage, and learn how cloning supports recovery and testing environments.

Explore how Snowflake time travel works by tracing immutable micro partitions, how updates create new partitions, and how deleted partitions remain readable during the time travel duration.

Explore Snowflake time travel extensions, using add and before clauses with timestamp, offset, or statement ID to access historical data and recover dropped objects or data before a specific query.

Learn snowflake time travel in a hands-on lab using at and before with timestamp and statement ID, and time offset to recover data after updates or deletes.

Explore Snowflake's time travel and undrop SQL extensions to recover accidentally deleted tables, schemas, and databases by restoring their metadata and reattaching data stored in cloud storage.

Learn how to use Snowflake time travel to recover a dropped table or an entire database, including restoring tables like customer and store, validating recovery via Android functionality.

Snowflake failsafe storage provides an extra protection layer by preserving data for seven days after time travel, with access restricted to Snowflake's support staff for recovery.

Describe how time travel and fail-safe storage incur costs as historical partitions are retained, with updates creating new micro partitions up to 90 days.

Snowflake offers temporary, transient, and permanent tables with time travel of one day for temporary and transient, and up to 90 days with seven days of failsafe for permanent.

Learn how Snowflake time travel lets you query and restore data as it existed, with up to 90 days retention on Enterprise Edition and above, and failsafe recovery.

Explore how Snowflake uses metadata and micro partitions to enable zero copy cloning, letting cloned tables share partitions until updates add new ones, with time travel preserved.

Learn to clone a complete schema and a complete database in Snowflake, including all child objects and tables, using the clone keyword and validating the results.

Explore Snowflake's secure data sharing, a metadata operation that shares data in place without moving or copying, uses three offerings to enable sharing, with automatic updates and no storage fees.

Explore Snowflake’s secure data sharing offerings—direct sharing, Snowflake marketplace, and data exchange—and how providers, consumers, and reader accounts access and pay for shared data without duplication.

Create a share object to expose tables and schemas to consumer accounts, grant select on tables and usage on the schema and database, then add consumers to enable read-only access.

Learn to share a table between Snowflake accounts by creating a share, granting usage and select, and enabling a consumer to create a read-only database from the share.

Learn to configure direct data sharing in the Snowflake web UI by creating a demo table as provider, sharing with a consumer account, and provisioning a read-only database.

Learn to share a table with a non-snowflake customer by creating a reader account, configuring private data sharing in the Snowflake web UI, and managing access with an account admin.

Navigate the Snowflake Marketplace interface to search data listings with filters and categories. Import a free dataset with account admin privileges and run a sample sql query.

Create and manage a private data exchange to share data with invited partners, defining providers and consumers, publishing listings that are free to access or require request and personalization.

Explore Snowflake performance features, from automatic caching and partition pruning to virtual warehouse scaling and multi cluster warehouses, and learn tuning tips to optimize query performance and control costs.

Explore how Snowflake executes queries—from cloud services compilation to virtual warehouse processing—through query plans, the optimizer, micro partition pruning, and caching for fast results.

Demonstrate query result caching in Snowflake by running a query, observing plan reuse on the second run, then disable cache with use cache result false.

Demonstrate query result cache in Snowflake by running a sample query. Disable the cache for the session by setting use cache result equal to false and compare the plan.

Explore how Snowflake's metadata cache tracks micro partitions by storing min and max column values and distinct counts. This enables selective reads and faster queries.

This hands-on lab demonstrates Snowflake's metadata cache by using count star and mean and max on date columns with metadata-based results, while max on a character column bypasses the cache.

The virtual warehouse caches data locally to speed up subsequent queries by reusing cached data rather than cloud reads, and its cache rebuilds over time after suspension.

Conclude the refreshed course section and preview ongoing updates that enhance explanations, visuals, and demonstrations to align with the latest Snowflake features and best practices.

Scale virtual warehouses up or down to match query workload and complexity, including multi-cluster setups that automatically spin up compute for higher concurrency while preserving existing queries.

Explains scaling policies for Snowflake multi cluster warehouses, comparing maximized and auto scaling, and detailing standard and economy policies, including when warehouses spin up or down.

Snowflake delivers built-in optimization with metadata, caching, and partition pruning, plus automatic clustering, metallized and materialized views, and scalable multi-cluster warehouses for concurrency.

Snowflake provides column level security with dynamic data masking and external tokenization via masking policies, applying them to columns and enforcing data visibility by role, row-level security and secure views.

Discover how to extend Snowflake functionality with user defined functions and stored procedures, including scalar and table functions, external functions, and calls to remote services.

Learn how secure user-defined functions protect data in Snowflake by restricting access to underlying tables, controlling visibility of UDF definitions, and balancing security needs with performance considerations.

Snowflake scripting extends SQL with procedural logic, enabling variables, expressions, result sets, loops, cursors, and exception handling within blocks for stored procedures or procedural code.