Snowflake
Learn to store, process and analyse data on the Snowflake cloud data platform.
- Fees
- Contact us for fees
About this course
Snowflake is a cloud data platform that stores data and runs SQL on it, with storage and computing kept separate, so teams can load, transform, share and analyse large amounts of data without managing servers. This course teaches the platform from first login to a finished, secured pipeline.
You write plenty of SQL and work with real file formats, including JSON. You also learn what makes Snowflake different from a traditional database: virtual warehouses that start and stop on demand, automatic ingestion with Snowpipe, change tracking with streams and tasks, built-in sharing, and ways to keep costs under control.
What you will be able to do
- Explain Snowflake's architecture and how it differs from a traditional data warehouse.
- Create databases, schemas, tables and roles, and write the SQL needed for analysis.
- Load data from files and cloud storage with stages and COPY INTO, and keep loading automatically with Snowpipe.
- Query semi-structured data such as JSON with the VARIANT type and FLATTEN.
- Automate transformations with streams, tasks and dynamic tables.
- Size and manage warehouses, read query profiles and keep credit use in check.
- Apply role-based access, masking and secure data sharing, and recover data with Time Travel and cloning.
Syllabus
10 modules. Select a module to see its topics.
Module 1: Cloud data warehousing basics
- Warehouse versus transactional database
- How Snowflake separates storage, compute and services
- Editions and regions in outline
- Setting up a trial account
- The Snowsight interface
Module 2: Objects and SQL
- Databases, schemas, tables and views
- Data types
- Roles and privileges
- Joins, aggregates and window functions
- Worksheets and query history
Module 3: Virtual warehouses and cost
- Warehouse sizes
- Auto-suspend and resume
- Multi-cluster warehouses
- Caching
- Credit usage
- Resource monitors and cost-aware habits
Module 4: Loading data
- Internal and external stages
- File formats
- COPY INTO
- Validating and fixing load errors
- Loading from Amazon S3, Azure Blob Storage or Google Cloud Storage
- Unloading data
Module 5: Continuous ingestion
- Snowpipe and auto-ingest
- Snowpipe Streaming in outline
- The Kafka connector in outline
- Monitoring loads and handling failures
Module 6: Semi-structured data
- VARIANT, OBJECT and ARRAY
- JSON, Parquet and Avro
- FLATTEN and lateral joins
- Querying nested fields
- Schema detection
Module 7: Transformations and automation
- Streams for change tracking
- Tasks and task graphs
- Dynamic tables
- Stored procedures and user-defined functions
- ELT patterns
- dbt with Snowflake in outline
Module 8: Performance and design
- Micro-partitions and pruning
- Clustering
- The query profile
- Materialized views
- The search optimization service in outline
- Modelling data for analytics
Module 9: Security, governance and sharing
- Role-based access design
- Masking and row-access policies
- Tags
- Secure data sharing and reader accounts
- The Snowflake Marketplace in outline
- Time Travel, Fail-safe and zero-copy cloning
Module 10: Python, BI tools and capstone
- The Python connector
- Snowpark in outline
- Connecting Power BI or Tableau
- Capstone: load, transform, secure, share and report on a dataset
Tools and technologies
- Snowflake (Snowsight)
- SQL
- Cloud storage (Amazon S3, Azure Blob Storage or Google Cloud Storage)
- Python (Snowflake connector and Snowpark)
- Power BI or Tableau
- Git
- dbt (in outline)
Projects you will build
Set up a database with roles, a warehouse and a resource monitor.
Continuous loading pipeline from cloud storage using Snowpipe.
Analysis of JSON event data with VARIANT and FLATTEN.
Change-data pipeline with streams and tasks that keeps a reporting table up to date.
Secure data share, with a BI dashboard on top of it.
Capstone: an end-to-end data pipeline with access controls and a cost review.
Before you start
- This outline starts from the basics: SQL is taught in module 2, so no earlier SQL is assumed.
- Helpful but not required: comfort with spreadsheets or databases, and a laptop with a modern browser and reliable internet.
- Entry requirements and how students get Snowflake access are confirmed by Pathway Career when you enquire.
Career paths
Examples of roles that use these skills. They are not a promise of a job.
- Snowflake developer
- Junior data engineer
- Cloud data analyst
- ETL or ELT developer
- BI developer
- Data warehouse engineer
- Analytics engineer
Questions about this course
Do I need to know SQL first?
This outline assumes no SQL: it is taught from the start of the course, and people who already know it will move faster. Entry requirements are confirmed by Pathway Career when you enquire.
Will I get a Snowflake certification?
Snowflake runs its own certification exams, which are taken separately through Snowflake. This course does not claim any affiliation. Pathway Career confirms whether exam preparation is part of the course when you enquire.
Is Snowflake free to practise on?
Snowflake has offered trial accounts, and the terms can change. Pathway Career confirms how students get access, and whether any cost applies, when you enquire.
Do I need cloud experience?
No. Cloud storage is explained as it is needed.
How is this different from the Data Engineering course?
Data Engineering covers many tools, such as Python, Spark, Airflow and Kafka. This course goes deep on one platform and its features.
Which cloud is used?
The ideas are the same on every cloud. Pathway Career confirms which provider the class uses when you enquire.
Next step
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