Course Introduction
Snowflake Data Engineering Fundamentals Training Course provides a practical foundation for professionals who want to design, build, automate, and maintain modern data pipelines using Snowflake. The course introduces core data engineering concepts across data loading, transformation, orchestration, incremental processing, dynamic tables, streams, tasks, Snowpipe, and Snowpark, helping participants understand how Snowflake supports end-to-end data engineering workflows.
Participants learn how to move data from source systems into Snowflake and transform it into reliable, analytics-ready datasets. The training covers stages, file formats, COPY INTO, Snowpipe, Snowpipe Streaming, schema considerations, and practical ingestion patterns. Snowpipe supports automated loading of files as they become available, while Snowpipe Streaming provides direct row-level ingestion for applications and services requiring low-latency processing.
A central focus of the course is data transformation using SQL and Snowflake-native engineering capabilities. Learners work with tables, views, joins, aggregations, MERGE operations, incremental processing, and reusable transformation logic. The course also introduces Dynamic Tables, which allow engineers to define desired results with SELECT statements while Snowflake manages dependencies and refreshes according to configured freshness requirements.
The training provides practical experience with Streams and Tasks for change data capture and workflow orchestration. Streams record row-level changes to supported Snowflake objects, while Tasks execute SQL or stored procedures according to schedules or triggers. Together, these capabilities can support continuous and event-driven data pipelines, including workflows where newly changed records are transformed and loaded into downstream targets.
Participants also explore alternative approaches to pipeline development and learn how to select appropriate Snowflake capabilities for different engineering requirements. Dynamic Tables provide a declarative approach for SQL-based pipelines, while Streams and Tasks provide greater procedural control for scenarios involving custom orchestration, stored procedures, MERGE operations, or external integrations. Snowpark is introduced for more complex transformations using Python, Java, or Scala.
By the end of the course, participants will be able to design foundational Snowflake data pipelines, ingest batch and streaming data, perform transformations, implement incremental processing, automate workflows, and monitor pipeline operations. Hands-on exercises culminate in an end-to-end project that combines ingestion, transformation, data quality, orchestration, and production-oriented engineering practices.
Duration
5 days
Who Should Attend
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Aspiring data engineers seeking a practical introduction to Snowflake data engineering.
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Data engineers transitioning from traditional ETL and on-premises data warehouse platforms.
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ETL developers interested in modern cloud-native data pipeline development.
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SQL developers who want to build scalable data transformation workflows in Snowflake.
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Database administrators expanding their skills into cloud data engineering.
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Data analysts who need a stronger understanding of data ingestion and transformation processes.
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BI developers working with Snowflake-based analytical data environments.
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Cloud engineers supporting Snowflake data platforms and engineering workloads.
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Data scientists who need reliable, prepared datasets for analytics and machine learning.
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Software developers building applications that generate or consume Snowflake data.
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Technical architects evaluating Snowflake for enterprise data engineering solutions.
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Professionals preparing for Snowflake data engineering projects or certification-oriented learning.
Course Objectives
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Understand Snowflake's core architecture and data engineering capabilities, including warehouses, databases, schemas, stages, tables, pipes, streams, tasks, and dynamic tables.
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Develop practical skills for loading structured and semi-structured data into Snowflake using stages, file formats, COPY INTO, Snowpipe, and appropriate ingestion patterns.
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Build reliable SQL-based transformation workflows using joins, aggregations, filtering, conditional logic, MERGE operations, and reusable transformation components.
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Implement incremental data processing by identifying changed records, applying change data capture concepts, and delivering transformed data efficiently to downstream targets.
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Create and manage Dynamic Tables for declarative SQL pipelines while understanding target freshness, refresh behavior, dependencies, and incremental processing.
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Use Streams and Tasks to capture data changes and automate processing workflows through schedules, triggers, task dependencies, and task graphs.
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Understand Snowpipe Streaming and its role in ingesting continuously arriving row-level data from applications, services, devices, and other real-time sources.
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Apply foundational data quality, schema management, error handling, monitoring, and operational practices to create dependable Snowflake data pipelines.
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Explore Snowpark for Python, Java, and Scala and understand when programmatic transformations are appropriate for data engineering workloads.
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Design and implement an end-to-end Snowflake data engineering solution that integrates ingestion, transformation, incremental processing, orchestration, monitoring, and data quality.
Comprehensive Course Outline
Module 1: Introduction to Snowflake Data Engineering
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Understanding Snowflake architecture and the role of cloud data platforms in modern data engineering environments.
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Exploring databases, schemas, tables, views, warehouses, stages, pipes, streams, tasks, and other foundational Snowflake objects.
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Reviewing the data engineering lifecycle from source extraction and ingestion through transformation, validation, orchestration, and delivery.
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Understanding how Snowflake separates storage and compute and how this architecture supports different engineering workloads.
Module 2: Data Ingestion Fundamentals
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Understanding stages, file formats, external storage locations, and the basic components required to load data into Snowflake.
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Using COPY INTO to load structured and semi-structured files from supported internal and external staging locations.
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Working with CSV, JSON, Parquet, Avro, and other common data formats while considering schema and loading requirements.
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Applying ingestion practices for validation, error handling, metadata capture, file management, and repeatable batch-loading processes.
Module 3: Snowpipe and Continuous Data Loading
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Understanding Snowpipe and how it automatically loads files from stages as they become available for near-continuous ingestion.
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Configuring pipes and COPY statements to create automated file-based ingestion workflows for cloud data engineering scenarios.
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Exploring event-driven file arrival patterns and the role of cloud storage notifications in automated Snowpipe processing.
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Comparing traditional scheduled batch loading with Snowpipe-based continuous ingestion and selecting appropriate approaches for different requirements.
Module 4: SQL Data Transformation
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Applying SQL transformations using filtering, joins, aggregations, conditional expressions, window functions, and common table expressions.
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Building clean and reusable transformation logic that converts raw or staged data into structured analytical and operational datasets.
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Using MERGE operations and other SQL techniques to support updates, inserts, deduplication, and synchronization between source and target tables.
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Designing transformation workflows that address inconsistent values, missing records, duplicate data, changing attributes, and business-specific rules.
Module 5: Dynamic Tables and Declarative Pipelines
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Understanding Dynamic Tables and how they materialize SELECT query results while automatically maintaining data freshness.
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Configuring target lag and understanding how Snowflake determines refresh timing and manages dependencies between dynamic tables.
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Building multi-stage transformation pipelines by connecting Dynamic Tables and allowing Snowflake to infer dependency relationships.
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Comparing Dynamic Tables with Streams and Tasks and identifying appropriate scenarios for declarative SQL-based data pipeline development.
Module 6: Streams and Change Data Capture
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Understanding Snowflake Streams and how they record inserts, updates, deletes, and other changes to supported source objects.
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Querying change data and using stream metadata to identify the type and state of records that require downstream processing.
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Designing incremental transformation patterns that consume changed records instead of repeatedly processing complete source datasets.
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Understanding stream offsets, consumption behavior, retention considerations, and practical approaches for maintaining reliable change-processing workflows.
Module 7: Tasks and Pipeline Orchestration
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Creating Snowflake Tasks to execute SQL statements or stored procedures according to schedules, triggers, and pipeline conditions.
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Building task graphs that coordinate dependencies between multiple processing steps and support structured data engineering workflows.
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Combining Streams and Tasks to create automated pipelines that process newly arrived or changed records as part of continuous data processing.
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Applying scheduling, dependency management, monitoring, failure handling, and operational practices to maintain dependable automated pipelines.
Module 8: Snowpipe Streaming and Real-Time Data Engineering
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Understanding Snowpipe Streaming and its architecture for directly ingesting row-level data from applications, devices, and services into Snowflake.
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Comparing Elastic Channels and Named Channels while considering throughput, ordering, delivery semantics, and application integration requirements.
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Designing low-latency ingestion scenarios for telemetry, event data, application activity, CDC workloads, and real-time analytical use cases.
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Comparing Snowpipe Streaming with file-based Snowpipe and selecting an ingestion approach based on source characteristics and latency requirements.
Module 9: Snowpark, Data Quality, and Pipeline Operations
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Introducing Snowpark and understanding how Python, Java, and Scala can support more complex data transformations within Snowflake.
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Applying data quality checks for completeness, uniqueness, validity, consistency, referential relationships, and business-rule compliance.
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Monitoring pipeline execution, investigating failures, reviewing processing behavior, and applying troubleshooting techniques to operational workloads.
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Designing maintainable data engineering solutions with appropriate naming, modular transformations, documentation, testing, error handling, and deployment practices.
Module 10: End-to-End Snowflake Data Engineering Project
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Designing an end-to-end pipeline that ingests source files or streaming records and prepares them for downstream analytical consumption.
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Implementing transformations, data quality controls, incremental processing, and curated target tables using appropriate Snowflake capabilities.
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Automating the solution with Dynamic Tables, Streams and Tasks, Snowpipe, or Snowpipe Streaming according to the project's processing requirements.
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Reviewing the completed pipeline for reliability, scalability, maintainability, observability, data freshness, and operational readiness.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
