Parquet and ORC are columnar file formats widely supported in Snowflake for efficient data loading and unloading operations. Both formats offer significant advantages for analytical workloads due to their columnar storage architecture.
Parquet is an open-source columnar format developed by Apache.…Parquet and ORC are columnar file formats widely supported in Snowflake for efficient data loading and unloading operations. Both formats offer significant advantages for analytical workloads due to their columnar storage architecture.
Parquet is an open-source columnar format developed by Apache. Snowflake provides native support for Parquet files, allowing users to query staged Parquet data using the INFER_SCHEMA function to automatically detect column definitions. When loading Parquet files, Snowflake can leverage the embedded schema metadata to simplify table creation. The COPY INTO command supports Parquet with various options including MATCH_BY_COLUMN_NAME, which maps source columns to target table columns based on names rather than position.
ORC (Optimized Row Columnar) is another Apache format originally designed for Hadoop ecosystems. Snowflake handles ORC files similarly to Parquet, supporting schema detection and efficient data extraction. Both formats benefit from built-in compression and encoding schemes that reduce storage requirements and improve query performance.
For loading these formats, users typically stage files in internal or external stages, then execute COPY INTO statements. The FILE_FORMAT parameter should specify TYPE = PARQUET or TYPE = ORC accordingly. Snowflake can also query these files in place using external tables, enabling schema-on-read patterns.
When unloading data, Snowflake supports writing to Parquet format using COPY INTO location commands with appropriate file format specifications. This enables seamless data exchange with other analytics platforms and data lakes.
Key considerations include understanding that Parquet and ORC store data in binary columnar format, which provides excellent compression ratios and allows Snowflake to read only required columns during queries. Semi-structured data handling in these formats integrates with Snowflakes VARIANT data type, enabling flexible schema evolution. Both formats support nested data structures, which Snowflake can flatten or preserve based on loading configurations.
Parquet and ORC File Handling in Snowflake
Why Parquet and ORC Handling is Important
Parquet and ORC are columnar file formats widely used in big data ecosystems. Understanding how Snowflake handles these formats is crucial for the SnowPro Core exam because they represent common data ingestion scenarios from data lakes, Hadoop environments, and modern data pipelines. Efficient handling of these formats enables seamless data integration and optimizes storage and query performance.
What are Parquet and ORC Files?
Apache Parquet is a columnar storage format designed for efficient data storage and retrieval. It provides excellent compression and encoding schemes, making it ideal for analytical workloads.
Apache ORC (Optimized Row Columnar) is another columnar format originally developed for Hadoop. It offers high compression ratios and fast read performance.
Both formats store data in a column-oriented manner, which aligns well with Snowflake's internal architecture.
How Snowflake Handles Parquet and ORC Files
1. Schema Detection: Snowflake can automatically detect the schema from Parquet and ORC files using the INFER_SCHEMA function. This eliminates manual schema definition.
2. Loading Methods: - Use COPY INTO command to load data from staged Parquet/ORC files - Specify FILE_FORMAT with TYPE = PARQUET or TYPE = ORC - Data can be loaded into existing tables or queried on the fly
3. Querying Staged Files: You can query Parquet and ORC files in stages using the $1 notation with the colon syntax to access nested columns (e.g., $1:column_name).
4. Variant Columns: When loading semi-structured Parquet/ORC data, Snowflake stores it in VARIANT columns, preserving the hierarchical structure.
5. File Format Options: - COMPRESSION: Specifies compression type (AUTO, SNAPPY, GZIP, etc.) - BINARY_AS_TEXT: Controls how binary data is handled - TRIM_SPACE: Removes leading/trailing whitespace
Key COPY INTO Syntax for Parquet/ORC:
COPY INTO my_table FROM @my_stage/path/ FILE_FORMAT = (TYPE = PARQUET);
Exam Tips: Answering Questions on Parquet and ORC File Handling
1. Remember Schema Inference: Know that INFER_SCHEMA works with both Parquet and ORC files to automatically detect column names and data types.
2. File Format Specification: Always specify the correct TYPE in FILE_FORMAT. Snowflake does not auto-detect file types for loading operations.
3. Columnar Format Advantages: Understand that Parquet and ORC are columnar formats, which provides efficient compression and faster analytical queries.
4. VARIANT Usage: When questions mention loading nested or hierarchical data from Parquet/ORC, the answer often involves VARIANT data type.
5. Stage Requirements: Files must be staged (internal or external stage) before loading. You cannot load Parquet/ORC files from local systems through the COPY command.
6. Match Columns Carefully: Know the MATCH_BY_COLUMN_NAME option, which allows loading Parquet/ORC data by matching column names rather than position.
7. Compression Awareness: Parquet files often use SNAPPY compression by default. Snowflake handles this transparently when COMPRESSION = AUTO.
8. Transformation During Load: You can transform data during the COPY INTO process using SELECT statements with column expressions.
9. Error Handling: Know options like ON_ERROR (CONTINUE, SKIP_FILE, ABORT_STATEMENT) for handling malformed records.
10. Unloading Limitations: When unloading data TO Parquet format, remember that Snowflake supports this through COPY INTO with FILE_FORMAT TYPE = PARQUET for external stages.