Snowflake[A-Z] Zero to Hero Masterclass(Core+SQL+API+Visual)

Master Snowsight's web interface by navigating databases, schemas, and tables, while loading data from local files or cloud storage and exploring projects, worksheets, and notebooks.

Explore Snowflake’s default roles and how switching between account admin, sysadmin, security admin, user admin, org admin, and the public role changes access and capabilities.

Create and configure Snowflake virtual warehouses as compute engines for query execution, choose standard or Snowpark optimized types, adjust auto resume and suspend, multi-cluster limits, and QAS scaling, then get_ddl.

Compare standard and economy scaling policies for Snowflake virtual warehouses, showing how additional clusters reduce query queuing and affect credits and performance in production.

Analyze Snowflake pricing, including compute and storage separation and Snowflake credits. Learn edition variations, credits to dollars, and how pay-as-you-go serverless features affect storage and compute costs.

Learn how Snowflake credits measure and track consumption across virtual warehouses, cloud services, and serverless features, and how credits convert to dollars, with a 30-day free trial worth $400.

Compare Snowflake editions from standard to virtual private snowflake, covering data sharing and time travel. Highlight encryption, dedicated warehouses, federated authentication, data replication, and external functions.

Explore how Snowflake's serverless features consume compute resources and credits, from Snowpipe data ingestion to replication, materialized views, maintenance, automatic clustering, and search optimization, impacting total cost.

Explore Snowflake storage costs by comparing on-demand and pre-purchased plans, understanding pay-as-you-go pricing, regional price variations around $40 per month, and when to switch to pre-purchased for savings.

Choose between on-demand and pre-purchased storage plans for Snowflake, weighing region-based price variations and real usage to optimize monthly costs.

Compare on-demand and pre-purchase storage costs across regions in Snowflake, with examples from US East Northern Virginia, Asia-Pacific Singapore, and Australia East Sydney hosted on Azure.

Learn how Snowflake compute costs are calculated for virtual warehouses, including credits per hour, billing by the second with a one-minute minimum, and auto suspend to save credits.

Snowflake's three-layer architecture—storage, compute (virtual warehouses), and cloud services handling metadata, security, and optimization—offers cloud services at no charge, with automatic resource allocation and up to 10% daily compute credits.

Learn how Snowflake data transfers incur charges when moving data across regions or clouds, including external tables and data lake exports, with region and cloud-specific pricing.

Monitor Snowflake pricing and storage via information_schema and account usage. Compare transient and permanent tables by active, time travel, and failsafe bytes, and review credits by type and warehouse.

Discover cost-saving optimization methods for Snowflake, including region selection, auto suspension of virtual warehouses, resource monitors, data compression, and favoring transient tables to reduce storage and transfer costs.

Track Snowflake compute costs with resource monitors, cap credits for virtual warehouses, set daily, weekly, or monthly limits, trigger alerts or suspensions, and require account administrator privileges to create monitors.

Learn how to configure Snowflake resource monitors by setting credit quota, choosing a schedule (frequency, start, end), and selecting monitor level for account-wide or warehouse-specific tracking and alerts.

Configure resource monitor triggers by setting a credit quota threshold. Define actions, such as notify, notify and suspend, or notify and suspend immediately, to manage warehouse usage.

Learn how Snowflake resource monitors manage virtual warehouses by suspending and resuming them based on credit thresholds, intervals, and manual actions, including auto-resume after quota changes or detachment.

The lecture explains how resource monitors manage credit quotas across account and warehouses, trigger suspensions and alerts, and emphasizes per-warehouse monitors since a warehouse cannot be tied to multiple monitors.

Create a Snowflake resource monitor in the web UI with a budget. Schedule start and end dates, monthly resets, and alerts at 50%, 60%, 75%, and suspend at 80% consumption.

Learn to create and attach Snowflake resource monitors with SQL statements, configure credit quotas and triggers, and attach monitors to warehouses or accounts across multiple scenarios.

Understand how traditional warehouses partition tables into independent static partitions to speed data retrieval, noting maintenance overhead and data skewness. Learn how Snowflake's micro partitioning speeds up query processing.

Discover how Snowflake uses micro partitioning to overcome static partitioning limits, storing 50–500 MB per micro partition in columnar storage, with cloud services metadata guiding queries.

Discover how Snowflake's micro partitions are automatically derived, with up to 500 MB each, enabling fine grained pruning, columnar storage, and lower compute costs for large tables.

Explore the logical and physical structure of a snowflake table and how micro partitions are automatically created and stored in columnar blocks with per-partition metadata.

Explore how Snowflake processes queries through the cloud services, virtual warehouses, and centralized storage, tracing how micro partitions are selected, pruned, and read via header files to return precise results.

Explore data clustering in Snowflake, and how clustering keys optimize query performance by organizing micro partitions around natural dimensions like dates; learn about testing, auto re-clustering, and choosing effective keys.

Explore clustering keys in Snowflake, defining keys on columns or expressions to co-locate data in micro partitions and speed queries. Snowflake maintains clustering automatically, but only when performance justifies it.

Explore how Snowflake partitions data into micro partitions, prunes unused partitions and columns, and stores clustering metadata such as partition counts, overlaps, and clustering depth.

Track the overlap of micro partitions and measure their average depth, where smaller depth signals better clustering and helps monitor health over time.

Discover how Snowflake automatically reclusters tables using the clustering key, maintains data locality in micro partitions, and lets you suspend or resume compute credits while improving date and type queries.

Explore Snowflake query history and caching, viewing 14 days of queries, with filters by user, warehouse, or duration, and download the prior 24 hours of result sets.

Explore Snowflake query history to see how metadata fetches from the cloud services layer, how compute and storage cooperate, and how caching and query profiles optimize performance.

Learn to fetch Snowflake query history using sql statements across session, warehouse, and user views, including filters, date ranges, and a seven-day range.

Understand how Snowflake uses metadata, results, and warehouse caches to speed queries, save costs, and persist results.

Explore how metadata, results, and warehouse caches optimize Snowflake queries with practical examples, showing when data comes from remote storage or local cache and the rules for reuse.

Explore Snowflake's results cache by contrasting a remote storage table scan with a cached result, and reuse results via last query id as shown in the query profile.

Create and rename sql worksheets in Snowflake using the Snow Sight Web UI, organize them with folders, write sql statements, and preview data from databases, schemas, and tables.

Set up SQL context and worksheet parameters in Snowflake, switch roles, select database and schema, manage warehouses, use semicolons, and run statements with keyboard shortcuts.

Set up the virtual warehouse, database, and schema, run your first sql to fetch ten rows from the customer table with select star from customer limit ten, and note timings.

Learn to read Snowflake worksheet results, view query details and column statistics, apply interactive filters and charts, and use search, copy, and CSV/TSV download features.

Create a free tier AWS account, explore over 60 services and database options like RDS, DynamoDB, and Elastic Cash, and learn about free tier duration and accessing the console.

Learn how to use AWS S3 to create and manage buckets for your Snowflake data lake, including selecting a region, setting access controls, organizing folders, and uploading objects.

Learn how identity and access management (iam) controls access to aws services by creating users, groups, roles, and policies, and assigning permissions to individuals or teams.

Create an IAM group and attach multiple policies, such as Amazon S3 full access and Glue, then manage policy attachments and plan to assign users later.

Create an IAM user with programmatic and console access, attach S3 full access, set a password, and download credentials CSV for login.

Learn to create an AWS IAM role to grant Snowflake access to S3 buckets, using trust relationships and an external ID for secure cross-account access.

Navigate to the AWS management console, open S3, and create a bucket with csv and parquet folders; upload health.csv and parquet files into their folders with read-write access.

Create a 26-column healthcare table schema with appropriate data types via DDL in Snowflake, including provider details and charges, then prepare to load data from AWS S3.

Create an integration object to connect AWS S3 with Snowflake and load data from S3 to Snowflake, configuring external stage, storage role, and storage allowed locations.

Describe the integration object to reveal seven properties, including the AWS IAM user ARN and external ID, then update the trust policy to enable Snowflake access to S3 buckets.

Create a csv file format and an external stage for S3 data. Use the copy command to load the data into a Snowflake table, handling comma-delimited values and errors.

Load complete data into Snowflake by switching to a pipe delimiter, uploading to S3, configuring a pipe-delimited csv file format, and executing a copy load to verify all rows.

Load parquet data from S3 into Snowflake using a parquet file format and an external stage, storing it in a single variant column and preparing for unwrapping to structured rows.

Load parquet data into Snowflake by defining a schema for semi-structured data, creating a healthcare table, and converting it to structured columns while preserving exact case.

switch to account admin to view external stages and file formats (parquet, csv, json) in the snowflake data warehouse, then grant usage privileges to sysadmin for these objects.

Load json data from S3 to Snowflake by creating a json schema, format, and stage, then copy to map json to a relational table with file name and load timestamp.

Explore Snowpipe, Snowflake's built-in serverless loader that ingests data from S3 in real time via notifications, enabling continuous loading from diverse channels into Snowflake tables without extra service charge.

Learn how Snowpipe ingests CSV data from an S3 external stage into Snowflake in near real time, automating loads via a pipe, stage, and auto ingest.

Ingest parquet data from S3 to Snowflake with Snowpipe, creating the health care parquet table, a parquet format, and a stage. Enable auto-ingest and monitor loads.

Set up snowpipe to auto ingest json data from S3 into a Snowflake table, defining json format, stage, and copy rules, then track file name, row number, and load time.