Follow these steps to manage and optimize the migration lifecycle
Posted: Mon Feb 10, 2025 4:30 pm
data.world has a tight integration with Snowflake using an agile approach and can accelerate innovations through Data Governance. These tight integrations, powered by a Knowledge Graph, enable users to understand their current data estate in Snowflake and existing Spark resources, and understand object dependencies within Snowflake, providing better reporting and tracking of data migration projects. To learn more, my colleague Lofan also wrote a great article about accelerating innovation through Agile Data Governance.
To ensure a successful migration from Spark to Snowpark, organizations can follow these steps:
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Building an Inventory: Identify the scope of applications, including files, jobs/tasks, read/write status, and compute requirements. This inventory helps understand the complexity and scale of the migration effort.
Identifying Dependencies: Determine the dependencies between Spark jobs and the clusters that they run their compute on, and any upstream and downstream tables. This analysis ensures a comprehensive understanding of the migration requirements.
Prioritizing Backlogs: Prioritize the migration backlog based on value and complexity. Focus on high-value, low-complexity applications for quick wins allowing you to achieve success with high ROI to get buy-in for additional migrations, and adopt an agile and lean approach. A catalog based on a Knowledge Graph architecture is the most effective way to determine a data asset value and complexity:
High Value: data.world catalogs all such metrics to identify high-value applications. With knowledge graph architecture, users can quickly identify high-value applications based on query australia whatsapp number data and usage counts from upstream source systems, usage metrics within data.world, popularity tags, job language, and active status of all existing jobs.
Low Complexity: To determine the complexity of data assets, one can utilize a graph dependencies walk within the Knowledge Graph. This involves analyzing the extent of upstream dependencies found in downstream applications. If an application received data from a small set of easily viewed, and understood data sources, it can be considered to have a low level of complexity.
Assessing and Migrating: Assess the existing codebase and migrate it to Snowpark, utilizing SnowConvert to help assess and simplify the process.
Testing and Optimizing: Utilize visual aids during testing to validate the correct functioning of the migrated application and its dependencies. Conduct comprehensive testing to ensure the accuracy of the migration process. Thoroughly test and optimize the migrated applications to validate functionality and benchmark performance against the original Spark jobs.
To ensure a successful migration from Spark to Snowpark, organizations can follow these steps:
.
Building an Inventory: Identify the scope of applications, including files, jobs/tasks, read/write status, and compute requirements. This inventory helps understand the complexity and scale of the migration effort.
Identifying Dependencies: Determine the dependencies between Spark jobs and the clusters that they run their compute on, and any upstream and downstream tables. This analysis ensures a comprehensive understanding of the migration requirements.
Prioritizing Backlogs: Prioritize the migration backlog based on value and complexity. Focus on high-value, low-complexity applications for quick wins allowing you to achieve success with high ROI to get buy-in for additional migrations, and adopt an agile and lean approach. A catalog based on a Knowledge Graph architecture is the most effective way to determine a data asset value and complexity:
High Value: data.world catalogs all such metrics to identify high-value applications. With knowledge graph architecture, users can quickly identify high-value applications based on query australia whatsapp number data and usage counts from upstream source systems, usage metrics within data.world, popularity tags, job language, and active status of all existing jobs.
Low Complexity: To determine the complexity of data assets, one can utilize a graph dependencies walk within the Knowledge Graph. This involves analyzing the extent of upstream dependencies found in downstream applications. If an application received data from a small set of easily viewed, and understood data sources, it can be considered to have a low level of complexity.
Assessing and Migrating: Assess the existing codebase and migrate it to Snowpark, utilizing SnowConvert to help assess and simplify the process.
Testing and Optimizing: Utilize visual aids during testing to validate the correct functioning of the migrated application and its dependencies. Conduct comprehensive testing to ensure the accuracy of the migration process. Thoroughly test and optimize the migrated applications to validate functionality and benchmark performance against the original Spark jobs.