Training for the SnowPro Specialty: Snowpark Certification

Explore the Snowpark DataFrame API from lazy evaluation to transformations, including selections, actions, joins, dropna and fillna, functions, and aggregations, with emphasis on window functions for exam prep.

Explore the Snowpark data frame API architecture, key objects, and server-side capabilities, and compare Snowpark data frames with Python connectors and pandas data frames through two quick hands-on demos.

Explore how to create and manipulate Snowpark dataframes, perform selections and actions with lazy evaluation, including casting and column operations, and leverage asynchronous calls for efficient data processing.

Apply filtering and transforming data with Snowpark data frames, using scalar functions and operators. Sort, limit, and handle nulls with ascending/descending, nulls first/last, and case when then otherwise expressions.

Master joins and set-based operations on Snowpark dataframes with Python, covering inner, left, right, full, and cross joins, plus union, intersect, and subtract.

Learn cleaning and enriching data with Snowpark for Python, including dropna, fillna, and drop any or drop all. Explore replace, subset, threshold, and sampling with dataframes.

Explore dataframe aggregations in Snowpark: perform sum and count on employees data imported from a CSV, using group by, aliases, and advanced options like crosstab, pivot, and grouping sets.

Master window functions in Snowpark by translating SQL queries into Python, using over clauses, partition by, order by, row_number, rank, and applying cumulative sums and rows between.

Learn Snowpark DataFrame integrations by creating data frames from external sources, persisting results, and performing DML operations, while comparing Snowpark with Pandas and handling semi-structured data.

Compare snowpark dataframes and pandas on Snowflake to understand differences, conversions, and when to use each, including vectorization and in-database processing.

Create Snowpark data frames from Snowflake tables, Python objects, SQL queries, and files like CSV, Parquet, JSON, and XML. Define schemas and use sessions, stages, and notebooks to explore.

Persist Snowpark data frame results by saving to tables, stages, or views, and optimize performance with cache results and temporary tables.

Learn to perform granular DML operations with Snowpark data frames—insert, delete, update, and merge—via a hands-on notebook and table-based workflows.

Learn to process semi-structured data with snowpark data frames by loading json and ndjson from stage, flattening arrays, casting values, comparing sql and snowpark python approaches (dot vs bracket notation).

Learn stored procedures and user-defined functions in Snowpark, from SQL create procedure and create function to Python-driven calls, including vectorized UDFs, secure procedures, stages, file operations, and machine learning training.

Explore how to create and call stored procedures, UDFs, and UDTFs in SQL and snowflake scripting, with examples in SQL, JavaScript, and Python, using Snowpark.

Explore calling stored procedures, user defined functions, and table functions from Snowpark in Python, using wrappers, explicit function calls, and table function techniques, including udtfs and lateral joins.

Explore how Snowflake Python worksheets generate on-the-fly stored procedures, use Python packages like faker, and transition to permanent stored procedures for custom data generation in Snowpark.

Learn to create, register, and call Snowpark stored procedures in Python, using anonymous and named procedures, type hints, and file-based registration with stage and package management.

Explore creating and registering udfs and udtfs in Snowpark Python from stage files, calling them in SQL, and managing permanent versus temporary registrations with type hints.

Explore vectorized Python UDFs in Snowpark, compare them with scalar UDFs, and learn how batching with pandas data frame boosts performance in large-scale queries.

Learn how to secure stored procedures, functions, UDFs, and UDTFs in Snowpark by managing owner and caller rights, altering secure flags, and granting usage privileges across databases, schemas, and warehouses.

Explore Snowpark architecture and file operations for unstructured data, including Snowflake file objects, UDFs, UDTFs, and stored procedures.

Learn how Snowpark handles packages and imports, including Anaconda packages, Python modules, and UDFs and stored procedures, plus how to import, stage, and execute external code and secrets.

Explore how to operationalize Snowpark stored procedures with Python, build direct acyclic graphs of tasks, and run stored procedures using Snowpark and Snowflake core APIs, including demo notebooks.

Master the Snowpark configuration and environments, including optimized warehouses, by focusing on the DataFrame API, Snowpark sessions, observability and event tables, testing with PyTest, and Snowpark ML within Snowflake APIs.

Explore configuring Snowpark-optimized warehouses, including use cases for model training, properties, billing, and when to scale, with hands-on notes on suspension, resuming, and switching warehouse types.

Explore Snowpark architectures and client versus server side capabilities, compare Snowpark with the Python connector, and learn how data frames, stored procedures, and UDFs run on the Snowflake platform.

Learn Snowpark sessions and setup, including installation, Python environment, session builder configurations, authentication methods, and multi-tool connections from VS Code, notebooks, and the Snowflake ecosystem.

Explore observability in Snowpark by configuring and querying event tables, logging, metrics, and tracing with Python UDFs and Snowflake telemetry for troubleshooting and performance insights.

Explore Snowpark test environments using pytest and the local testing framework to mock Snowflake connections, run unit and integration tests, and validate data frames locally.

Explore snowpark for machine learning concepts, including data cleaning, encoding, pipelines, and training an XGBoost regressor, plus operationalizing with snowpark python stored procedures and a model registry for inference.

Explore sample Snowpark questions with explanations, covering Snowpark optimized virtual warehouses for machine learning training, Python UDF usage, DataFrame aggregation, Python stored procedures, and MFA security.

Conclude with 30 exam-like questions to practice toward 90%, plus a 100-question Udemy set with two 50-question tests, detailed answers, and references.