“By 2022, 75% of all databases will be deployed or migrated to a cloud platform, with only 5% ever considered for repatriation to on-premises.” – Gartner.
Enterprises have realized the significance of infrastructure modernization with the outbreak of pandemic. Across the globe, organizations have stepped forward to break the limitations of legacy systems and move on to cloud-based architecture. With an increase in the amount of unstructured and semi-structured data from eCommerce, social media, and other platforms, enterprises must tune their legacy databases frequently. The enterprises maintaining on-prem data warehouses lag in the scalability and compatibility of analytical workloads.
Oracle users must purchase additional hardware and software counterparts to maintain the reliability and performance of the data warehouse. If there are any feature updates in Oracle, the enterprises must plan for downtime and then proceed with remedial patchworks to inherit those features in the data platform. Further, Oracle data warehouses require additional Online Analytical Processing (OLAP) counterparts for business analytics. Over the years, the enterprises are trapped in complex data models, patchworks, and licensing upgrades for their hardware & software resources.
With these limitations and complexities in legacy data warehouses, businesses are pushed to migrate to Snowflake data platform that is compatible with analytical tools to achieve matured data models and meaningful business insights. Snowflake provides advanced features like automated remedial database activities, separate storage & compute resources, zero-copy cloning, data sharing, and requires no hardware provisioning. In addition, Snowflake slashes the compliance management efforts to the minimal.
Even though enterprises decide to move on to cloud data platforms, they are concerned about migrating their legacy data platforms as there are possibilities of missing out enterprise datasets and compromising on data security. Being a trusted Snowflake partner, our team assures you a smooth and secure transition to Snowflake cloud data platform. We build a defined migration strategy to establish a single source of truth from multiple data sources with different data structures.
This article showcases our complete Oracle to Snowflake migration roadmap!
Migrating your data warehouse requires huge efforts and making it successful lies in understanding the nooks and corners of the existing architecture. Right from documenting the existing architecture to concluding the Snowflake data warehouse sizing, everything must be assessed before diving into the data platform modernization.
Documenting the existing architecture will expose your enterprise to identify the areas that require reengineering and can be moved as-is to the Snowflake platform. Our team dives in and analyzes your existing data architecture. We evaluate the Oracle databases, schemas, objects, data sources, data pipelines, processes, and tools that populate datasets in the architecture to establish a cohesive data model in the Snowflake platform. Successively, we analyze and rationalize the complexities of data pipelines in the existing data model.
Being the tech leaders for more than a decade, our data engineers are proficient in legacy and modern data platforms. No matter whether the data architects who designed your legacy data structure are available in enterprise or not, you have proper documentation or not, we can easily catch up on the existing data model, source codes, and job schedules.
For security implementation, we investigate roles, users, accessibility permissions, frequency of patches, and other maintenance operations in Oracle. Summing it up, we document all these details from Oracle and draft an “as-is” architecture diagram.
During the data warehouse migration, we might introduce or deprecate tools, change the development environments, and deployment process in the Snowflake platform. So, we document the below details from your existing architecture.
Further, our team architect the plans to incorporate these tools, deployment processes, and environments from Oracle to Snowflake platform.
Based on the existing architecture, we suggest the best-suited future architecture for your enterprise to implement Snowflake platform. Our future architecture includes various data sources, integration components, data warehousing platforms, and business intelligence tools. We curate the migration approach depending on these integration components, and data structures to comply with Snowflake architecture.
Finally, we assess the size of existing data warehouse to decide on the historical data load in Snowflake platform (Online (Cloud Data Migration Services) / Offline (AWS Snowball/Azure Databox/Google Transfer Appliance)).
Every enterprise has its unique requirements while modernizing its data platforms. Our data architects brainstorm with your executive team and determine the key factors to establish a custom migration strategy for your enterprise. Further, we step forward with designing the migration framework and showcasing the MVP.
Depending on the existing data warehouse architecture and business needs, we connect Snowflake platform to the data sources in two different approaches.
Our data engineers rationalize the data models by considering the future architecture. We categorize the future data models as:
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.
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.
Our team brainstorms with the executive team and plans for Oracle cutover. We categorize the legacy data warehouse cutover as:
By considering all these factors, our data architects develop a custom migration strategy for your enterprise Snowflake migration.
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.
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.
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
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 migrates 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.
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.
Our team kick-starts migration execution by setting up the Snowflake platform. We aim at achieving a cohesive data model with scalability and data security as a top priority.
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 data cloud platforms, our team has built tools and customizable strategies to make the migration quick and simple. Based on your business needs, our data engineers can build a customized migration 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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