Perform data transformations using SQL and Snowflake features for data modeling and processing (18% of exam).
This domain covers performing data transformations using SQL within Snowflake, executing DDL and DML queries, implementing best practices for data modeling and schema design, utilizing Snowflake's features for data cleansing and enrichment, working with views, stored procedures, user-defined functions (UDFs), and understanding streams and tasks for data pipelines.
5 minutes
5 Questions
Data Transformations in Snowflake refer to the process of modifying, converting, or manipulating data as it moves through various stages of your data pipeline. Understanding data transformations is essential for the SnowPro Core Certification.
Snowflake supports transformations during data loading through the COPY INTO command, allowing you to perform SELECT operations on staged data before loading it into target tables. This means you can reorder columns, omit columns, cast data types, and apply SQL functions during the load process itself.
Key transformation capabilities include:
1. **Column Reordering and Omission**: You can select specific columns from source files and load them in any order, which is useful when source file structures differ from target table schemas.
2. **Data Type Casting**: Convert data types during loading using CAST functions or the :: operator to ensure data compatibility with target column definitions.
3. **String Functions**: Apply functions like SUBSTRING, CONCAT, TRIM, UPPER, and LOWER to modify text data during ingestion.
4. **Date and Time Functions**: Transform date formats and extract date components using functions like TO_DATE, TO_TIMESTAMP, and DATEADD.
5. **Conditional Logic**: Use CASE expressions and IFF functions to apply conditional transformations based on data values.
6. **Sequence Generation**: Generate unique identifiers using sequences during data loading.
7. **Flatten Operations**: Transform semi-structured data (JSON, Avro, Parquet) into relational format using the FLATTEN function and LATERAL joins.
Snowflake also supports ELT (Extract, Load, Transform) patterns where raw data is loaded first, then transformed using SQL queries, stored procedures, tasks, and streams. This approach leverages Snowflakes powerful compute resources for transformations.
For the certification exam, understand that transformations can occur at load time through COPY INTO with SELECT statements, or post-load using standard SQL operations, making Snowflake flexible for various data integration scenarios.Data Transformations in Snowflake refer to the process of modifying, converting, or manipulating data as it moves through various stages of your data pipeline. Understanding data transformations is essential for the SnowPro Core Certification.
Snowflake supports transformations during data loading…