Become a master at access history and column lineage in Snowflake. Join our Snowflake Pros as we discuss and show:
Access History – How to:
- Utilize Data Discovery:
- Uncover unused data to decide whether to keep or delete it.
- Track Sensitive Data Movement:
- Follow the data journey from external cloud storage (e.g. Amazon S3) to Snowflake tables.
- Trace the movement of data within Snowflake tables.
- After tracing, implement policies (masking and row access) to protect data.
- Regulate access control settings for stages and tables.
- Tag stages, tables, and columns to maintain compliance.
- Data Validation:
- Validate the accuracy and integrity of reports and visualizations.
- Receive notifications before dropping or altering tables or views.
- Compliance Auditing:
- Identify Snowflake users who performed write operations and when.
- Meet compliance regulations such as GDPR and CCPA.
- Enhance Data Governance:
- Get a comprehensive view of data access, timing, and movement from source to target.
Column Lineage – How to:
- Protect Derived Objects:
- Easily tag sensitive columns without extra work.
- Protect tables containing sensitive columns with row access policies.
- Protect sensitive columns with masking policies or tag-based masking policies.
- Sensitive Column Copy Frequency:
- Determine the number of objects containing a sensitive column.
- Prove regulatory compliance standards (e.g. GDPR).
- Root Cause Analysis:
- Trace data to its source to solve problems.
- Pinpoint points of failure from poor data quality.
- Reduce columns to analyze during troubleshooting.