“Nearly 40% of projects fail due to poor planning.”- Team Gantt
Everything in and around us is “data” and someone is leveraging it for business purposes. With data piles growing every day, enterprises strive to harness these insights to drive ROI, customer experience, cut down efforts, and much more. The increasing demand to win the competitive edge in the marketplace pushes brands to adopt cloud and business intelligence solutions.
Legacy data platforms like Oracle fall off in this cloud war!
Modernizing your legacy data platform to a modern data cloud platform! And when it comes to modern data platforms, our go-to recommendation is Snowflake!
Snowflake aims at delivering a unified cloud data platform with a wide variety of workloads such as data lake, data applications, data engineering, data science, data warehouse, and data sharing.
Snowflake has designed the storage and compute resources to operate independently. With the workload isolation feature, users can deploy an unlimited number of virtual warehouses for various workloads in Snowflake platform.
The platform renders native connectors for leading third-party ecosystems such as Talend, Matillion, Fivetran, dbt, Spark, Java, Python, etc, for seamless data engineering operations. Also, with the “Unistore” modern approach, Snowflake facilitates a unified layer for transactional and analytical data.
Snowflake users can seamlessly share enterprise datasets with a zero-copy cloning feature to customers, third parties, and partners in Snowflake Data Exchange platform.
Snowflake offers a completely automated database platform. They bet on near-zero database administration and maintenance efforts with automated remedial activities, columnar compression, database statistics, query & performance tuning, scalability, 90 days of time travel, no partitioning/ indexing, and much more features.
Data platforms are excruciatingly difficult software to migrate. Migrating the entire data architecture that has been built over the years is not a cakewalk for any organization and the decision-makers get flinched to hear the need for data platform modernization.
In this blog post, we showcase how our data engineers plan for a successful Oracle to Snowflake migration project!
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 Data box/Google Transfer Appliance)).
Being a trusted Snowflake partner, with years of hands-on expertise in legacy data warehouse and Snowflake data cloud platforms, our data architects can help you in planning for Oracle to Snowflake migration project. Be it for a complete project or additional project management or Snowflake technical expertise, we can cater to your enterprise needs. We’re ready with industry-specific accelerators to make the Snowflake migration quick!
Let’s get over the call and proceed with planning for your data warehouse modernization! Talk to our Salesperson now!
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