“Only 36% of data migration projects keep to the forecasted budget, and only 46% were delivered on time.” -Forbes
A data-driven business model becomes a critical aspect in today’s competitive marketplace. Be it small, medium businesses, or enterprises, they fetch data from customers and harness them for various exploratory and predictive analysis. So, in order to unify their siloed data layer and achieve matured data models with streaming solutions, organizations migrate to the modern data landscape.
Modernizing the legacy data platforms such as Oracle requires a streamlined migration roadmap. And when it comes to modern data platforms, Snowflake is the go-to recommendation!
Exceeding the budget or project deadlines can be mitigated with proper planning and streamlined execution of data migration projects.
While implementing Oracle to Snowflake migration, you must kick-start the execution with a defined migration strategy.
Organizations can determine the Oracle to Snowflake migration strategy by deciding on key factors such as:
Conclude on how to connect source touchpoints with Snowflake. (Third-party/ native integration tools!)
Future Data Model
Rationalize the existing data models by considering the future architecture. (Refactoring/ Retaining data model)
Historical Data load
Assess the size of existing data warehouse and decide on a historical load approach. (Online/Offline)
Decide on legacy cutover based on your organizational model. (Big bang/Trickle migration)
Based on the business needs, you can build a Snowflake migration strategy and proceed with the execution. We aim at achieving a cohesive data model with scalability and data security as a top priority.
In this blog post, we unveil how our data engineers follow a standard execution process for Oracle to Snowflake migration project.
We gear up the Snowflake environment by creating a replica of Oracle databases, schemas, and objects. Successively, we execute data definition language (DDL) scripts in the Snowflake platform to create database objects.
Consecutively, our team creates separate virtual warehouses for each function based on the information gathered during the discovery phase. Based on the estimated warehouse sizing, we set up the maximum auto-scaling capabilities in the Snowflake platform. We employ resource monitors to track resource utilization and act on when limits are reached.
Our data engineers develop the migration pipelines and data flow based on the migration framework design. With minimal datasets in all the source systems, we execute the migration framework in lower environments.
Over the years of experience, our team has gathered vast experience in automating the migration scripts, building prefabs, migration tools, and data integration components. So, building a solid migration framework and iterating these scripts to load enterprise datasets is not a hard deal for us!
Considering the rationalization of the future data model, our test engineers build a data reconciliation framework for all the source systems. Upon loading the sufficient datasets to the Snowflake platform, we execute these data reconciliation frameworks and ensure data accuracy in source and target data platforms.
Our test engineers automate the test scripts to reuse them in multiple environments throughout the migration process. They document test coverages and validate the acceptance criteria to ensure a successful migration.
In order to completely modernize to Snowflake platform, we need to extract all the data from Oracle. If Oracle is hosted on an on-premises architecture and has terra or petabytes of data, then we may require AWS Snowball/Azure data box/Google Transfer Appliance to load the historical data in Snowflake platform. Our team schedules appropriate timelines to provide these data boxes, load them with data, transport them to the cloud data center, offload them to cloud servers, and load them to the snowflake platform.
While migrating terra or peta bytes of data, we ensure a sufficient time gap between historical and incremental loads to maintain data up to date. We plan for data subset migration than loading entire contents to ensure instant remediation of data changes.
After loading the historical datasets, we synchronize data from Oracle data warehouse to Snowflake platform till cutover. We create data synchronization schedules based on process dependencies defined in the discovery phase. By monitoring data load schedules, we understand the state of data. We evaluate the performance and process issues by analyzing the monitoring reports.
While migrating the enterprise data warehouse from Oracle to Snowflake, we plan to run the systems in parallel by synchronizing the source touchpoints to validate the performance and datasets. Our test engineers analyze both the data platforms to ensure zero data loss and efficient performance in the Snowflake platform.
Our team validates migration success by comparing the Oracle and Snowflake platform. With this analysis, we discover the migration issues and propose possible solutions to mitigate them. Our team wraps up by redirecting tools from Oracle to Snowflake platform and planning for cutover.
Based on the desired outcomes documentation, our team identifies success factors and deviations in the Snowflake migration. We prepare the root cause analysis and possible mitigation strategies to fix the migration issues.
Our team rectifies the migration issues by implementing the proposed mitigation strategy. We document the standard operation procedure (SOP) for each issue with the details such as a responsible person, technical lead, and third parties involved in fixing the issue and escalate them with proper follow-ups. With this defined approach, we slash the data discrepancies in data warehouse migration.
Our team has expertise in logging tickets, finding resources, and following the processes of Snowflake community. Based on the reports, our technical leads follow up and discuss the progress of remediation. We do focus on issues that must be resolved post-migration with exclusive documentation and bug fixes.
Our experts analyze the tools and information on the level of support each tool has for the Snowflake platform with the help of as-is architecture. We redirect tool connections by creating copies of existing oracle solutions and repointing them to Snowflake platform. Our test engineers validate the performance and output of tools in both data platforms and ensure the desired results are achieved.
Our team plans for the shutdown of the Oracle system. We insist on communicating the cut-over to the Oracle users in prior, so that they can switch to Snowflake platform and run the dependent tools on the target system.
Finally, we turn off the integration points that populate data into Oracle system and revoke the accessibility to Oracle data warehouse.
Your enterprise data is now completely migrated from Oracle to the Snowflake data platform!
Our team highlights the benefits and outcomes of Snowflake migration to the executive team. We wind off with guaranteed support based on the SLA!
Being a trusted Snowflake partner, with years of hands-on expertise in legacy data warehouses and Snowflake platform, our team has built accelerators and customizable strategies to make the migration quick and simple. Based on your business needs, our data engineers can build a customized migration and data reconciliation framework and modernize your legacy data warehouse to Snowflake platform with minimal sprints.
Without further delay, let’s understand your enterprise data model and help you win the competitive edge with matured data models by migrating to Snowflake!
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