Snowflake Masterclass: Pipelines, SQL, Streams, Tasks, DBT

Explore Snowflake architecture across storage, compute, and cloud services, where micro partitions and pruning speed data retrieval and independent compute clusters enable scalable, concurrent access to the same data.

Explore the Snowflake UI to create SQL and Python worksheets, notebooks, and dashboards, and manage ingestion with cloud storage options like S3, Blob, and Google Cloud, plus DBT integration.

Learn to use prebuilt AI models in Snowflake for time-series forecasting, train and run forecasts, and monitor results with query IDs, marketplace and catalog, and governance.

Create and manage Snowflake virtual warehouses, select sizes and types, enable auto-suspend and auto-resume, scale with multi-cluster pools, and monitor consumption and per-second compute pricing.

Snowflake uses role-based access control to assign privileges to roles and grant them to users, enabling an analyst to read data with select permissions while preventing create or drop actions.

Explore how databases store data and how schemas organize tables like folders, with objects inside schemas such as tables, views, materialized views, and formats that hold the data.

Explore the three Snowflake table types: regular, transient, and temporary, showing how time travel and fail safe affect drop and un-drop, retention time, and session data.

Explore how a view stores a query as an object and dynamically reflects changes in the source table, and compare regular views, materialized views, and secure views.

Define the file format before importing data into Snowflake to ensure proper handling of CSV, JSON, Avro, ORC, and XML files.

Explore internal and external stages in Snowflake, upload files to a stage, load them into a table, and use a directory table to view external stage contents.

Compare four data loading methods in Snowflake: copy into command, snowpipe, snowpipe stream, and external table, with external stages and near real-time versus real-time streaming.

Track changes with streams in Snowflake masterclass; create a stream to capture every insert, update, or delete for CDC. Compare standard streams versus insert-only streams.

Automate data loading in Snowflake by scheduling SQL with tasks, design a mini pipeline using external stages, snow pipe, streams, and tasks to refresh data.

Create a Snowpipe pipeline to automate data loading in Snowflake, aligning with pipelines, streams, and tasks concepts covered in the masterclass.

Clone a table in Snowflake using zero-copy cloning to duplicate only the table, not storage, and explore time travel with a demonstration.

Discover time travel in Snowflake by restoring a table to its prior state using a query ID or time, and see how cloning preserves the original 30 records.

Create basic dashboards in snowflake by building time series line charts and KPI cards from sales data, using sale date and sale amount.

Define the hotel booking business requirements and prepare clean, standardized data, transform to monthly aggregates, and build dashboards showing monthly revenue, bookings, and top revenue-generating cities with KPIs.

Explore the architecture of a fully Snowflake native pipeline—no external tools—ingesting hotel booking data into stage, building bronze, silver, and gold layers, and visualizing in SnowSite.

Create a photoDB database, define a csv file format, and establish a stage to load the csv file. Parse the data into columns and store it in a bronze table.

Create a bronze hotel booking table with a 13-column schema, load staging data via copy into using csv format with on-error continue, then verify by selecting 50 rows.

Create the silver table with properly typed columns, then clean data by fixing invalid emails, negative totals, and date inconsistencies, and apply formatting like init cap and trim.

Learn to transform data into the gold layer in Snowflake, build monthly revenue and bookings dashboards, and identify top revenue cities using SQL and Snowflake-native tools.

Master end-to-end real-time banking data engineering using Snowflake pipelines, SQL, streams, tasks, and dbt to build scalable, production-ready data workflows.