Imagine trying to solve a puzzle without knowing which piece came from where. Data without versioning and lineage is just like that!
Organizations need a structured way to manage data history, track transformations, and ensure consistency, especially in cloud platforms like Snowflake, Google Big Query, Redshift and Databricks.
In this article, we’ll explore the challenges of managing data changes, the importance of versioning and lineage, and how Revefi’s AI Data Engineer handles these for you.
Challenges of Managing Data Changes
Handling data changes is a complex task. Without proper tracking, businesses may face serious issues that affect operations, compliance, and AI applications. Here are some key challenges organizations encounter:
1. Loss of Historical Data
Without versioning, past data is lost, making error recovery and trend analysis impossible. Businesses risk compliance violations if regulations require historical records.
Example: A retail company wants to compare last year’s sales with this year’s but cannot because past data wasn’t saved, preventing accurate trend analysis.
2. Untraceable Transformations
Data undergoes multiple transformations, making it hard to track errors, dependencies, and processing steps. Without lineage, businesses struggle with transparency and troubleshooting.
Example: A financial company finds errors in reports but lacks records of previous transformations, making root cause analysis difficult.
3. Regulatory Compliance Risks
Laws require businesses to track data changes for audits and security. Poor lineage leads to fines, security risks, and legal issues.
Example: Under GDPR, companies must track customer data. If they cannot provide modification records upon request, they risk non-compliance and penalties.
4. AI Model Inconsistency
Untracked data changes lead to unreliable AI models, drifting predictions, and biased decisions, reducing trust in machine learning.
Example: A healthcare AI model predicts disease risks. If patient data updates aren’t tracked, predictions change unpredictably, making the model unreliable.
5. Audit and Debugging Difficulties
Debugging errors, verifying decisions, and ensuring compliance become time-consuming without a clear history of data changes.
Example: A bank finds discrepancies in customer balances but cannot track past transactions, making it hard to determine if the issue is a fraud, system error, or manual mistake.
What is Data Versioning?
Data versioning is the process of keeping track of different versions of data over time. It works just like version control in software development, where each change is recorded. This enables users to revert, compare, or analyze past versions when needed.
Importance of Data Versioning and Lineage
Data versioning and lineage are crucial for businesses that depend on data for decision-making, compliance, and AI applications. Without proper tracking, organizations risk inconsistencies, compliance violations, and unreliable analytics. Here’s why they matter:
Ensures Data Consistency
Data is always changing—customer records, transactions, and inventories are updated frequently. Without versioning, these changes can cause inconsistencies, making reports unreliable. By maintaining historical records, businesses ensure decisions are based on accurate, verified data.
Improves Troubleshooting
Errors can happen at any stage—wrong entries, faulty transformations, or system failures. Without lineage tracking, finding the root cause is difficult. When businesses track their data’s journey, they can quickly find and fix problems. This reduces downtime and improves efficiency.
Enhances AI Model Performance
AI models rely on consistent and high-quality data. If data changes unpredictably, models produce unreliable results. A fraud detection system trained on old transaction data may fail if fraud patterns evolve. With versioning, businesses can track data changes, retrain models on updated datasets, and keep AI systems accurate and effective.
While data versioning focuses on tracking different versions of data, data lineage maps out how data moves and transforms across different systems. Let’s see how Snowflake handles these aspects.
Data Versioning in Snowflake
One of Snowflake’s key capabilities is maintaining historical data through Time Travel, Zero-Copy Cloning, and Fail-safe Recovery. These features ensure that businesses can track changes, recover lost data, and comply with regulatory requirements.
1. Time Travel: Accessing Historical Versions of Data
Snowflake’s Time Travel feature allows users to access and restore past versions of data for up to 90 days, depending on the edition. It automatically tracks inserts, updates, and deletions, creating new versions of tables. Users can retrieve previous states using SQL queries with the AT or BEFORE clause, ensuring seamless data recovery.
This feature is useful for recovering accidentally deleted or modified data, auditing changes for compliance, and debugging errors by comparing past versions. Time Travel applies to tables, schemas, and databases, ensuring comprehensive historical tracking across storage levels.
Using Time Travel in SQL Queries
To retrieve a past version of a table, users can specify a timestamp or a query ID.
SELECT * FROM employee_data AT (TIMESTAMP => '2025-03-20 12:00:00');
SELECT * FROM employee_data BEFORE (STATEMENT => '01a3b4c5-678d-9efg-h012-345678ijklmn');
2. Zero-Copy Cloning: Efficient Data Snapshots Without Duplication
Snowflake’s Zero-copy Cloning lets users instantly clone databases, schemas, or tables without physically copying data. Instead, the clone shares the same underlying storage as the source, ensuring no extra storage is used at creation. New storage is allocated only when changes are made, making cloning efficient and cost-effective.
This feature is ideal for development and testing, allowing teams to create test environments without altering production data. Cloning is instantaneous, regardless of dataset size, ensuring seamless data management.
Using Zero-Copy Cloning in SQL Queries
To create a clone of a table:
CREATE TABLE employee_data_clone CLONE employee_data;
To clone an entire database:
CREATE DATABASE sales_db_clone CLONE sales_db;
3. Fail-safe Recovery: Ensuring Data Protection Beyond Time Travel
Snowflake’s Fail-safe mechanism provides an additional layer of data protection beyond time. Snowflake’s Fail-safe retains historical data for seven days after the Time Travel period expires. Unlike Time Travel, Fail-safe does not allow direct access via SQL queries. Instead, Snowflake support must perform recovery. Fail-safe serves as a last-resort recovery option for disaster recovery and regulatory compliance.
The Role of Data Lineage for AI Model Consistency
Data lineage is essential for understanding how data flows, transforms, and evolves within an organization. Snowflake offers built-in tools and metadata tracking mechanisms to facilitate data lineage analysis, enabling organizations to track transformations, understand dependencies, and establish ownership of data assets.
1. Tracking Data Transformations: Understanding Changes Over Time
Snowflake tracks data transformations by logging every modification applied to raw data. The QUERY_HISTORY table records all executed queries, allowing teams to monitor SQL transformations over time. The ACCESS_HISTORY view provides visibility into user interactions with data, improving auditability.
Tracking transformations helps teams debug errors by reviewing past modifications and analyzing the impact of data changes on downstream reports.
Example: Querying Transformation History
To retrieve historical queries applied to a table:
SELECT query_text, start_time, end_time, user_name
FROM snowflake.account_usage.query_history
WHERE query_text LIKE '%employee_data%';
2. Understanding Dependencies: Managing Dataset Relationships
Snowflake tracks data dependencies to prevent unintended disruptions when datasets change. The OBJECT_DEPENDENCIES view in ACCOUNT_USAGE shows which views reference specific tables. Metadata for materialized views and derived tables helps analyze dependencies. The ACCESS_HISTORY view distinguishes between direct and base object access, revealing relationships within complex database structures.
Understanding dependencies aids in change management by assessing the impact of modifications before implementation. It optimizes ETL pipelines by reducing redundant dependencies and unnecessary computations.
Example: Finding View Dependencies
To find views dependent on a specific table:
SELECT referencing_object_name, referencing_object_type
FROM snowflake.account_usage.object_dependencies
WHERE referenced_object_name = 'EMPLOYEE_DATA';
3. Establishing Data Ownership: Assigning Responsibility and Accountability
Snowflake helps establish data ownership by using Role-Based Access Control (RBAC) to restrict modifications and access to authorized users. The ACCESS_HISTORY table logs dataset access, ensuring accountability. Tagging and metadata management allow organizations to label datasets with owners or business units.
Clear data ownership supports governance compliance by ensuring that only authorized users modify critical datasets. It aids incident investigations by identifying unauthorized changes and enhances collaboration by allowing teams to coordinate efficiently.
Example: Tracking Data Access by User
To identify who accessed a specific table:
SELECT user_name, object_name, access_time
FROM snowflake.account_usage.access_history
WHERE object_name = 'EMPLOYEE_DATA';
4. Ensuring Compliance
Industries like finance and healthcare must follow strict regulations such as GDPR and HIPAA, which require businesses to track how data is stored, accessed, and processed. Snowflake’s GET_LINEAGE function helps organizations monitor data movement, ensuring transparency and compliance.
5. Enabling Auditability
Errors in AI models or reports can arise from incorrect data inputs, transformations, or access issues. Snowflake’s data lineage capabilities fully trace how data flows through the system, helping teams quickly diagnose and fix issues. With an end-to-end view, businesses can validate data accuracy, improve decision-making, and maintain trust in analytics and machine learning applications.
Key Challenges in Managing Data Versioning & Lineage
Despite having powerful tools, businesses still face some hurdles:
1. Storage Costs
Maintaining multiple versions of data requires significant storage, especially for large datasets with frequent updates. While Snowflake optimizes storage with its zero-copy cloning and compressed storage techniques, excessive historical data retention can still lead to unnecessary costs.
2. Complexity in Data Pipelines
Data moves through multiple transformations—cleaning, filtering, aggregating—before reaching its final form. Tracking these changes across ETL workflows, materialized views, and cross-database dependencies can be challenging. Without proper lineage tools like Snowflake’s GET_LINEAGE function, teams may struggle to pinpoint errors or understand how data flows through pipelines.
3. User Access and Security
Ensuring that only authorized users can modify or access lineage information is critical for data governance. Role-Based Access Control (RBAC) in Snowflake helps enforce restrictions, but organizations must also monitor ACCESS_HISTORY logs to detect unauthorized modifications.
Best Practices for Managing Data Versioning & Lineage in Snowflake
If you want to follow the best practices for data versioning and lineage, Revefi suggests the following strategies:
1. Enable Time Travel and Fail-safe
Snowflake’s Time Travel lets users restore past data versions for up to 90 days, while Fail-safe provides an extra seven-day safety net. These features prevent accidental data loss, maintain audit trails, and ensure compliance.
2. Use Descriptive Metadata
Clear metadata makes it easier to track and manage dataset versions. Organizations should label datasets with meaningful names, version numbers, timestamps, and ownership details. This reduces confusion, enhances traceability, and helps teams quickly locate the most accurate data for analysis and decision-making.
3. Automate Lineage Tracking
Manual lineage tracking is time-consuming and error-prone. Tools like Snowflake Horizon automate this process, providing a clear view of dependencies and transformations. Automated tracking improves governance and speeds up troubleshooting.
4. Set Access Controls
Restricting access to lineage data ensures security and prevents unauthorized changes. Snowflake’s role-based access control (RBAC) allows businesses to assign permissions, safeguarding sensitive information.
5. Regularly Audit and Clean Up Old Data Versions
Frequent audits help optimize storage and remove outdated data. While Time Travel and Fail-safe provide historical tracking, keeping unnecessary versions increases costs. Periodic clean-ups ensure businesses retain only relevant data, keeping data environments efficient.
By following these practices, organizations can create a secure, transparent, and efficient data management strategy, ensuring data remains reliable and accessible throughout its lifecycle.
How Revefi’s AI Data Engineer Enhances Data Versioning & Lineage in Snowflake
Revefi's AI Data Engineer Raden integrates with Snowflake, enhancing data versioning and lineage capabilities. By connecting to Snowflake's metadata, Revefi provides real-time insights into data transformations and dependencies, ensuring data integrity and compliance. This integration will enable your business to monitor data quality, optimize performance, and manage costs effectively. For detailed integration steps, refer to Revefi's Snowflake documentation.
In a notable case, Uplimit used Revefi's AI Data Engineer to integrate with Snowflake, achieving a 50% reduction in data spend and a tenfold improvement in operational efficiency. This was accomplished through automated monitoring and AI-driven insights, allowing the company to detect and resolve data issues swiftly. If you are curious to use Revefi for your use case, get a free demo or book a call with us now!
Conclusion
Data versioning and lineage are essential for maintaining accuracy, compliance, and trust in data-driven decisions. Without proper tracking, businesses risk inefficiencies, regulatory penalties, and unreliable analytics. Revefi’s AI Data Engineer bridges this gap by offering intelligent observability, proactive issue detection, and cost optimization all integrated with Snowflake.
Incorporating Revefi's AI Data Engineer into your Snowflake environment can significantly enhance your data management capabilities. By automating data observability, quality assurance, and performance optimization, Revefi ensures that your data operations are both efficient and cost-effective. This seamless integration allows your data teams to focus on strategic initiatives and be confident in the reliability and accuracy of your data. To explore how Revefi can transform your data processes, consider booking a demo.
