Oracle to Snowflake migration Strategy – How to define a winning strategy?

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But does every data migration project wins? Its’ a big “?”

If your data platform migration strategy fails, you will be trapped in

What’s the solution?

You need a successful plan for Oracle to Snowflake migration!

Legacy data architecture

Evaluate the Oracle databases, schemas, objects, data sources, data pipelines, processes & toolsets and orchestrate them in an architecture diagram.

Integration points

Document their job schedules, process dependencies, connection strings, and configuration guidelines.

Deployment models

Analyze their development and deployment process.

Data security & governance

Explore roles, users, accessibility permissions, frequency of patches, and other maintenance operations in Oracle.

In this blog post, we showcase how our data engineers establish Oracle to Snowflake migration strategy!

Defining Oracle to Snowflake migration strategy – Building blocks of your modern data landscape!

Deciding on Key factors to establish a migration strategy

Integration approach

Depending on the existing data warehouse architecture and business needs, we connect Snowflake platform to the data sources in two different approaches.

Native SnowSQL

In this approach, we create a replica of Oracle SQL packages in SnowSQL. Then, by utilizing the connectors and drivers, we establish data source integration to Snowflake platform.

Third-party integration

If your enterprise already leverages third-party integration tools like Informatica, Talend, Azure Data Factory, and Databricks, we recommend retaining them to establish the connection to Snowflake platform.

Future data model  

Our data engineers rationalize the data models by considering the future architecture. We categorize the future data models as:

Refactoring data model
Requires reengineering to comply with Snowflake platform.

Retaining data model
Migrated as-is to the Snowflake platform.

With this rationalization, our team showcases the clear picture of efforts required to attain the future Snowflake architecture to the executive team of your enterprise.

Historical data load 

Based on the size of existing data warehouse, we discuss with the executive team and define the historical data load approach. We are flexible with online and offline historical data loads.

Online

Loading historical data by leveraging cloud data migration services and connecting periodically to synchronize source datasets till cutover.

Offline

Loading historical data by utilizing AWS Snowball/Azure Databox/Google Transfer Appliance and planning for incremental loads till cutover.

Cutover 

Our team brainstorms with the executive team and plans for Oracle cutover.  We categorize the legacy data warehouse cutover as:

Big bang migration

Complete data migration with planned downtime for live systems.

Trickle migration

Data migration in phases with parallel runtime of Oracle and Snowflake platform.

By considering all these factors, our data architects develop a custom migration strategy for your enterprise Snowflake migration.

Prioritizing datasets and data pipelines 

With the existing architecture and inventory, our team prioritizes datasets and pipelines based on process dependencies. Further, we document these process dependencies to identify the ongoing changes throughout the migration. We capture these ongoing changes with automated job schedules to cut off rudimentary efforts.

Defining project deadlines & budget 

Our team assesses the availability of resources and requirements of reengineering to attain the future data model in the Snowflake platform to set achievable deadlines. Your executive team can have business expectations and our team would figure out ways to establish realistic deadlines through consecutive brainstorming sessions.

Our team is ready with a Snowflake pricing estimator to assess the estimated annual cost based on the number of minutes a warehouse is expected to run each day and the number of days the warehouse is expected to run each week. Based on the data warehouse sizing and the number of compute clusters required during and post-migration, we can help you in concluding the budget planning for the Oracle to Snowflake migration.

Determining expected outcomes 

Our team determines the success factors in the Snowflake migration by discussing with the executive team. In addition, we document these expected outcomes, and benefits of Snowflake migration to validate the future data architecture.

Some of the common success factors of our clients were

• Establishing a single source of truth across the enterprise
• Visualizing enterprise datasets as meaningful business insights with a real-time reporting dashboard
• Meeting the SLA by delivering appropriate reports for businesses to operate

Creating migration framework 

Upon concluding the desired outcomes in Snowflake migration, our team ideates the possible solutions to achieve the future architecture. As a proof-of-concept, we take primary data sources and create data pipelines to Snowflake platform. With this proof-of-concept, we identify the defined patterns that migrate the source data structures to Snowflake platform and design the migration framework.

By executing the migration framework, we iterate and load the historical data to Snowflake platform effortlessly with minimal sprints.

Showcasing MVP 

Our team steps forward to present the MVP for the primary source system of your enterprise. By leveraging the migration framework, we bring in the datasets from source touchpoints to Snowflake platform and BI tools. Further, we validate and present this MVP to the executive team to communicate the benefits of migrating to Snowflake platform.

We’re here to help you build a custom migration strategy for Oracle to Snowflake migration project!

Being a trusted Snowflake partner, with years of industry excellence and customer journey, we can architect a custom Snowflake migration strategy for your business needs. From unveiling your legacy data platform to migrating & optimizing your modern data landscape with matured data models, our team delivers an end-to-end cloud data strategy solution. We’re ready with migration accelerators across the data lifecycle from ingestion to analytics for seamless data platform modernization.