Many businesses are embarking on a Snowflake migration. Snowflake is a powerful data warehouse that has expanded its reach as a full blown data platform. Snowflake data quality problems can arise during migration if the processes beyind your data platform are not carefully connsidered.
.. Big moves, there’s always the risk of running into challenges, especially concerning data loss. When data doesn’t transition as expected, it can disrupt operations, leading to potential setbacks. Safeguarding your data during a migration isn’t just about avoiding mishaps it’s about ensuring business continuity and maintaining data integrity. Without proper precautions, you risk losing valuable information, which could impact decision-making and lead to setbacks.
In recent months Snowflake introduced Snowconvert AI. You can use SnowConvert AI to quickly migrate from legacy platforms like Oracle, SQL Server, or Teradata into Snowflake. This service will translate your existing SQL into Snowflake-native SQL. This saves a tedious and manual step and leans on Snowflake’s vast resources to help you stop wrestling with old tech and start taking advantage of everything Snowflake has to offer.
Currently, Snowconvert is available for the following source platforms:
Evaluating Your Data Before Migration
The first step in preventing data loss is evaluating your data before the migration process begins. By doing a comprehensive assessment, you’re not just understanding what you’re moving; you’re also getting a grasp on its quality and relevance. Think of it like packing for a big move. Before you move, it’s always wise to sort through your belongings, deciding what to keep, donate, or toss out. In a similar way, data evaluation lets businesses filter through what’s useful and what’s redundant.
Here are some steps to aid in this evaluation:
– Identify Critical Data: Determine which data sets are crucial for your operations and prioritize them during migration.
– Identify Sensitive: Determine any data sources or tables containing sensitive or highly sensitive data that would fall under both regulatory, legal, or internal governance policies.
– Data Accuracy and Quality Processes: Presumably your enterprise data team has processes in place to certify the accuracy and integrity of your data. Those processes will need to move along with data itself. A data migration is a wrong time to run a data quality initiative because you end up with conflicting priorities and severely high risks for delays that will block your final delivery and cutover.
– Review ETL and Application Connectivity: Inventorying all processes pushing and pulling data from your data platform is pivotal to governing your data and controlling costs. Hidden behind the curtains could be processes that continuously run against your legacy platform to simulate “real time.”
– Data Inventory / Archival: The cost of storage could plummet when you move to Snowflake vs other solutions, but you still should consider outdated or irrelevant data that no longer serves a purpose. This not only lightens the migration load but also improves the quality of your database.
Extend your Backup and Disaster Strategy
Protecting your data doesn’t stop at just evaluating and organizing it. One of the most effective ways to guard against data loss during a Snowflake migration is by establishing solid backup strategies. Backups serve as your safety net, ensuring that you have a fallback option if something goes wrong during the migration.
Consider Snowflake RBS (role based security) from your Legacy Security Model
One area that can cause hang-ups and delays in a data migration is a security model change. Legacy systems can have multiple generations of security, migrations, and that needs to be reconciled to ensure you adhere to security standards that govern principles of least privilege.
Get Data Analysts Involved Early and Often
A left and shift for your existing data platform to Snowflake may or may not render tools and code obsolete. Luckily many popular data and analytics tools you already use connect to Snowflake. There are however several purposely built platforms that make working with data in Snowflake much easier.
Monitoring and Verifying Data Post-Migration
Once your migration to Snowflake is complete, continuous monitoring and verification processes ensure your data’s integrity. Think of this as the post-move check—making sure everything is in its place and nothing’s missing.
Key steps include:
– Data Health Checks: Regularly verify data accuracy and consistency. This helps identify any discrepancies early.
– Automated Alerts: Set up notifications for unusual activity or errors. These alerts serve as an early warning system for potential issues.
– Routine Data Audits: Conduct audits to confirm that your data remains clean and well-organized. This ongoing care keeps systems efficient and reliable.
You can feel confident about your Snowflake migration’s success through diligent monitoring and verification. Your data remains secure and accurate, ready to meet the demands of your business.
Safeguard Your Data During Snowflake Migration
Data migration to Snowflake offers immense advantages, yet it also comes with its complexities like any data platform.
Embarking on a successful Snowflake migration a key step in modernizing your data infrastructure, and DataTools Pro is here to support every stage of your journey. Leverage our robust tools and strategies to ensure your data remains secure and transitions smoothly.
