Python and Snowflake – Data Engineering in the Cloud: from Queries to Automation

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

Working with Snowflake in the cloud - You will learn how to navigate Snowflake confidently, understand its architecture, and use storage and compute layers in a practical way during everyday data engineering work.

Building data structures - You will practice creating schemas, tables, and views, and loading CSV, JSON, and Parquet files, so you can set up a complete data environment for analysis and integration.

Writing better SQL - You will master SQL with JOINs, CTEs, subqueries, and aggregations, and learn time travel, fail-safe, and optimization techniques to query and recover data more effectively.

Connecting Python to Snowflake - You will configure Python connections to Snowflake, work with different authentication methods, and learn how to open and close sessions safely in your scripts and applications.

Handling data from Python - You will send SQL queries from Python, load results into pandas DataFrames, and parameterize commands, so you can combine programming logic with efficient data operations.

Analytics and reporting - You will analyze Snowflake data with pandas, numpy, matplotlib, and seaborn, then prepare reports and exports to CSV or Excel that are ready for business stakeholders.

Automating pipelines - You will build simple ETL workflows in Python, review scheduling options, and see how to add logging, error handling, and alerting to routine data processes you run.

Secure data delivery - You will learn how to manage roles, credentials, and access policies, and how to audit activity, so you can reduce mistakes and protect data more effectively in real projects.

Training programme

1. Introduction to the Snowflake platform

architecture and basic concepts (data layer, compute, storage),
data storage models: shared data architecture,
differences between Snowflake and traditional databases,
access interfaces: Snowflake Web UI, SQL, API.

2. Creating and managing databases in Snowflake

creating schemas, tables, views,
loading data (CSV, JSON, Parquet etc.),
overview of tools: SnowSQL, Snowsight,
access management and data security (roles, users, policies).

3. Data processing and query optimization

writing SQL queries in Snowflake (JOIN, CTE, subqueries, aggregations),
Time travel and fail-safe – data versioning management,
query optimization: clustering, partitioning, caching.

4. Introduction to Snowflake integration with Python

environment requirements: installation of snowflake-connector-python,
authentication and establishing a connection to Snowflake (login/password, OAuth, tokens),
creating and closing connection sessions in Python.

5. Data operations in Snowflake using Python

sending SQL queries from Python,
retrieving query results into data structures (e.g. pandas.DataFrame),
parameterizing SQL queries (e.g. dynamic WHERE, LIMIT).

6. Data processing and analytics in Python

analysis of data retrieved from Snowflake in Python,
examples of using pandas, numpy, matplotlib, seaborn,
creating reports or exporting data to Excel / CSV.

7. Process automation

building simple ETL pipeline’s in Python,
task scheduling (e.g. cron, Airflow, Prefect, Dagster – overview of possibilities),
error handling, logging and alerting.

8. Advanced integration capabilities

use of frameworks such as SQLAlchemy for working with ORM,
working with external APIs and sending data to Snowflake,
integration with cloud services (AWS S3, Azure Blob Storage, GCP).

9. Best practices and security

secure management of credentials and keys,
restricting access to data at the code level,
audit, logging of user and application activity.

What are the prerequisites for participating in the training?

SQL basics - You should be comfortable writing simple SQL queries, using SELECT, WHERE, JOIN, and GROUP BY, and understanding how tables and relationships between data work.

Python basics - You should know Python syntax, variables, functions, loops, and libraries, and be able to run a simple script and understand the output it produces.

Working with data - You should understand tables, records, columns, and data file formats such as CSV and JSON, so you can follow import and analysis tasks without confusion.

Development environment - You should be able to install a Python package, use a terminal or code editor, and work on your own computer, so you can complete the hands-on exercises smoothly.