Master Snowflake's data ingestion pipeline — from staging files through COPY INTO, Snowpipe, and Snowpipe Streaming — and connect Snowflake to the broader data ecosystem through connectors, drivers, and export tools.

Stages, File Formats, and COPY INTO

Classifying Stage Types

COPY INTO Error Handling

Snowpipe and Snowpipe Streaming

Choosing the Right Ingestion Mechanism

Ingestion Method Characteristics

Data Unloading and Connectivity

Unloading Data to Cloud Storage

Connectivity Mechanisms

Data Loading, Ingestion, and Connectivity

Build robust data pipelines using Snowflake-native orchestration — streams for change capture, tasks for scheduling, dynamic tables for declarative transformation, and external and Iceberg tables for open-format storage.

Streams and Change Data Capture

Querying a Stream

Choosing the Right Stream Type

Tasks and DAG Orchestration

Task Scheduling and Dependencies

Options

Warehouse-based task

Serverless task

Warehouse-Based vs Serverless Tasks

Dynamic Tables

Dynamic Table TARGET_LAG and Refresh

Streams + Tasks

Either

Choosing Your Orchestration Mechanism

Designing an End-to-End Pipeline

External and Iceberg Tables

When to Use External Tables

Native Snowflake Table

External Table

Iceberg Table

Table Types by Storage and Engine

Pipeline Orchestration and Data Objects

Query semi-structured JSON with dot notation and FLATTEN, aggregate data with GROUPING SETS, ROLLUP, and CUBE, compute window functions, and encapsulate reusable logic in UDFs and stored procedures.

Semi-Structured Data Querying

Extracting Nested Fields

Unnesting JSON Arrays

Intermediate Aggregate Functions

Writing a ROLLUP Query

Choosing the Right Grouping Extension

Window Functions

Ranking Records with ROW_NUMBER

RANK vs DENSE_RANK

Period-over-Period Change with LAG

Running totals with SUM

User-Defined Functions and Stored Procedures

Creating and Calling a SQL UDF

UDF vs Stored Procedure

Querying and Transforming Data

Profile queries in Snowsight, identify bottlenecks with QUERY_HISTORY, apply Search Optimization, Query Acceleration Service, Automatic Clustering, and Materialized Views, and write SQL that benefits from Snowflake's three caching layers.

Query Profile,  History, and Insights

Navigating Query Profile

Identifying  queries with QUERY_HISTORY

Investigating slow queries

Optimization Methods

Matching Optimization Tools to Query Patterns

Choosing Between Optimization Tools

What Method Works Best

How Caching Works in Snowflake

Identifying the Caching Layer

SQL Patterns: Cache vs Pruning

Congratulations!

Query Performance and Optimization

Build end-to-end data pipelines in Snowflake. Learn to ingest data using COPY INTO, Snowpipe, and Snowpipe Streaming, then automate workflows with streams, tasks, and dynamic tables. You'll also master querying semi-structured data, applying window functions, and optimizing query performance using Snowflake's profiling tools and caching layers.

Master the tools and techniques for building reliable, automated data pipelines in Snowflake. You'll start by learning how to ingest data at scale — configuring stages and file formats, loading data with COPY INTO, and choosing between batch loading, Snowpipe, and Snowpipe Streaming for different latency and volume requirements.
From there, you'll build end-to-end pipeline orchestration skills: capturing row-level changes with streams, chaining multi-step workflows with task DAGs, and creating declarative, auto-refreshed pipelines with dynamic tables. You'll also learn when to reach for external and Iceberg tables for multi-engine and cloud-native scenarios.
The second half of the course sharpens your querying and transformation skills — extracting and unnesting semi-structured JSON from VARIANT columns, applying grouping extensions and window functions for advanced analytics, and encapsulating reusable logic in UDFs and stored procedures.
Finally, you'll tackle query performance: reading Snowflake's Query Profile to pinpoint bottlenecks, selecting the right optimization tool — Search Optimization, Query Acceleration Service, clustering keys, or materialized views — and writing cache-friendly SQL that avoids the common anti-patterns that silently degrade performance.

Load, automate, and optimize data pipelines in Snowflake using COPY INTO, Snowpipe, streams, tasks, dynamic tables, and query performance tools.

Data Pipeline Automation in Snowflake

Essentials for Snowflake SnowPro® Core Certification

Data stored in Snowflake-managed micro-partitions with full partition pruning

Full read-write access via SQL; Snowflake manages compression and clustering

Data stays in cloud object storage; Snowflake reads files at query time

Query performance depends on partition columns derived from the file path structure

Stored as Parquet files with a metadata layer; readable by Spark, Flink, and Trino without conversion

Data is not locked to a single query engine

Table Types by Storage and Engine

Data Pipeline Automation in Snowflake

Hands-on interactive exercise