Oracle to Snowflake Migration: A Comprehensive Guide


With over 430,000 customers around the world, Oracle is a well-known name. When it comes to RDBMS, Oracle is the top pick for businesses everywhere.

Oracle has created a history with its immense impact on the field of relational database management systems (RDBMS), setting industry standards, and playing a pivotal role in shaping the evolution of enterprise-level data management solutions.

However, organizations are exploring new horizons as technology evolves and the cloud becomes integral to modern data strategies. Recognizing the need for innovation, many are now turning their attention to Snowflake—a cloud-native data platform that promises not just efficiency but a paradigm shift in data management.

According to Statista, In 2022, the global cloud storage market reached a valuation of 90.17 billion U.S. dollars. Anticipated growth projections indicate an increase to 108.69 billion U.S. dollars in 2023 and a substantial surge to 472.47 billion U.S. dollars by 2030 with a forecasted compound annual growth rate of approximately 23 percent.

In this context, the migration from Oracle to Snowflake emerges as a strategic move, aligning businesses with the agility and innovation demanded by the digital era.

In this comprehensive Oracle to Snowflake migration guide, we will delve into the details of the Oracle to Snowflake migration process, uncovering key considerations, best practices, and insights to ensure a seamless transition from Oracle to Snowflake.

Oracle To Snowflake Migration: Discovering Why

Before we start this guide, let’s discover the significant reasons behind this strategic migration.

  • Technology Advancement

Over time, the volume, variety, and velocity of data have changed notably. Simultaneously, storage and computation technologies have undergone significant advancements. The popularity of the cloud has also played a key role in motivating organizations to move their data and its management to cloud-native platforms like Snowflake.

While traditional platforms may lack some modern capabilities, modern platforms address today’s business needs such as availability, unlimited concurrency, scalability, and performance.

  • Cloud Era

With unlimited concurrency and high availability, the cloud has gained immense popularity, replacing traditional IT delivery models. With minimal intervention and management, the cloud seamlessly provides on-demand, flexible, and cost-effective models, standing as a beacon of efficiency in the modern tech landscape.

  • Demand for Cutting-Edge Analytics

In the era of data-driven decision-making, there is an ever-increasing need for a modern analytics solution that beats traditional boundaries. Businesses seek dynamic tools capable of extracting actionable insights from vast and diverse datasets, driving them toward a data-driven future.

  • Modern Data Infrastructure

In an ever-evolving landscape, each day brings forth new tools, data sources, and methodologies. While older platforms make attempts to enhance functionalities, their upgrades often fall short of a significant departure from traditional approaches. The paradigm of data infrastructure has undergone a profound transformation, demanding contemporary analytics solutions that are not only advanced but also intuitively responsive, allowing businesses to focus on insights rather than struggling with infrastructure complexity.

Having explored the driving forces convincing organizations to transition to cutting-edge platforms such as Snowflake, let’s now delve into key aspects that underscore why Snowflake services stands out as a preferred choice.

Why Snowflake: Decoding the Whys Behind Its Dominance in Modern Data Management

The motivation behind migrating from Oracle to Snowflake encompasses several strategic considerations:

  • Scalability and Flexibility:

Snowflake’s cloud-native architecture provides unparalleled scalability, allowing organizations to effortlessly scale their data infrastructure in response to evolving business needs. The flexibility of Snowflake accommodates dynamic workloads and ensures optimal performance.

  • Cost Efficiency:

The cloud-based model of Snowflake offers a more cost-effective solution compared to on-premises alternatives. Its pay-as-you-go pricing model allows organizations to align their expenses with actual usage, minimizing unnecessary costs.

  • Advanced Analytics and Insights:

Snowflake’s modern analytics capabilities enable businesses to derive actionable insights from their data. The platform supports advanced analytics tools and techniques and empowers organizations to make informed decisions.

  • Cloud-Native Advantages:

Leveraging the advantages of cloud technology, Snowflake ensures high availability, unlimited concurrency, and minimal management overhead. This aligns with the current trend of transitioning to cloud-native platforms for streamlined operations and resource optimization.

  • Adaptation to Evolving Technology:

The evolving landscape of data management demands solutions that can keep pace with technological advancements. Migrating to Snowflake allows organizations to adopt a future-ready data platform to ensure relevance and competitiveness in an ever-changing digital ecosystem.

  • Enhanced Performance:

Snowflake’s architecture is optimized for performance, offering improved query execution and data processing capabilities. This results in faster and more efficient data operations with enhanced overall performance.

  • Global Accessibility:

Snowflake’s cloud-based approach facilitates global accessibility to data, enabling seamless collaboration and data sharing across dispersed teams. This is especially crucial in today’s interconnected business environment.

  • Security and Compliance:

Snowflake prioritizes robust security measures, ensuring data integrity and compliance with regulatory standards. Migrating to Snowflake can enhance data security, providing peace of mind to organizations handling sensitive information.

  • Future-Proofing Data Infrastructure:

As the digital landscape continues to evolve, migrating to Snowflake represents a strategic move toward future-proofing data infrastructure. This ensures that organizations remain agile and capable of adapting to emerging technologies and trends.

  • Community and Ecosystem Support:

Snowflake’s growing community and ecosystem of integrations contribute to the motivation for migration. The platform’s compatibility with various tools and services enhances its value proposition.

Now, let’s move towards the actual roadmap to migration from Oracle to Snowflake.

Roadmap For Oracle to Snowflake Migration: Key Points To Consider

Creating a roadmap to migration is a crucial step, in ensuring a systematic and successful transition. Here are key points to consider before you carry out the process.

  • Conduct a comprehensive assessment of your current Oracle environment and define migration goals and objectives
  • Identify critical data, applications, and dependencies
  • Gain support from key stakeholders and decision-makers and communicate the benefits and objectives of the migration
  • Identify potential risks associated with the migration and develop mitigation strategies
  • Map Oracle data structures to Snowflake equivalents. Cleanse and optimize data for the target platform
  • Validate compatibility of applications and scripts with Snowflake
  • Conduct thorough testing to identify and resolve any issues
  • Choose appropriate migration tools and methods
  • Evaluate options for bulk data transfer and schema migration
  • Develop a phased rollout plan for the complete migration
  • Prioritize less critical systems for an initial migration
  • Implement monitoring tools for performance and usage
  • Continuously optimize configurations for efficiency
  • Fine-tune Snowflake configurations based on post-migration analysis
  • Optimize for ongoing performance improvements

Let’s discuss the methods and tools used for migration.

Migrating Your Oracle Enterprise Data To Snowflake

The initial phase of your migration involves the transfer of crucial database objects, encompassing databases, tables, views, and sequences. Essential tasks include the creation of users, account names, roles, and the assignment of object grants. Once you decide which objects to migrate, the subsequent step involves the selection of a suitable method to transfer your data model to Snowflake.

Different Methods to Migrate From Oracle to Snowflake:

  • Data Modelling Tool
  • DDL Scripts

Let’s delve into the explanations for the methods for migrating from Oracle to Snowflake:

  • Data Modelling Tool:

Utilizing a data modeling tool involves using software designed for creating, visualizing, and managing data models. These tools often provide a graphical interface to design database structures, and they can generate SQL scripts for creating or altering tables and relationships.

Steps to Follow:

  • Design the target schema in the data modeling tool, specifying tables, columns, and relationships.
  • Generate SQL scripts from the data model, capturing the necessary schema definitions.
  • Execute the generated scripts on the Snowflake environment to recreate the structure.


  • Offers a visual representation of the data model, aiding in understanding and validation.
  • Simplifies the process of creating DDL (Data Definition Language) scripts, reducing manual errors.


  • May require familiarity with the specific data modeling tool.
  • Limited to schema definition, and additional steps may be needed for data migration.
  • DDL Scripts:

The Data Definition Language (DDL) is a standard part of SQL used to define and manage the structure of a database, including creating, altering, or deleting tables and other objects. Writing DDL scripts involves manually writing SQL statements to replicate the schema from Oracle to Snowflake.

You can simply remove or edit your existing DDL scripts as per the Snowflake environment. You can utilize existing DDL scripts and need to check data types and optimize those according to Snowflake-optimized types.

Steps to Follow:

  • Analyze the Oracle database schema and structure.
  • Write DDL scripts to recreate the tables, indexes, constraints, and other database objects in Snowflake.
  • Execute the scripts in the Snowflake environment to replicate the schema.
  • If you have existing DDL scripts, you can optimize those for the Snowflake environment and remove unwanted code or features not required in Snowflake.


  • Offers fine-grained control over the schema creation process.
  • Allows for customization and optimization of the SQL statements based on Snowflake’s requirements.


  • Prone to human error, as manual script creation may introduce mistakes.
  • May be time-consuming, especially for large and complex databases.

Move Existing Data Set

Once you’ve created objects in Snowflake, it’s time to shift your old data from Oracle to Snowflake. There are a few ways to do this: you can use a special migration tool, an ETL tool, or just do it manually.

Think about how much data you’re moving. If it’s just a bunch, like tens or hundreds of terabytes up to a few petabytes, it might be easiest to put it into files and use a service like AWS Snowball or Azure Data Box. If it’s a massive amount, like hundreds of petabytes or even exabytes, you’ve got big options like AWS Snowmobile or Azure Data Box.

If you want to do it manually, you’ll need to get your data out of each Oracle table and into text files. Oracle has ways to do this using PL/SQL routines or SQLcl. Then, upload these files into an Amazon S3 staging bucket using the PUT command. Make sure these files are between 100 MB and 1 GB so that Snowflake’s parallel bulk loading can work its magic.

Once your data is in S3, you can start loading it into your Snowflake table using the COPY command.

What About Queries and Workloads?

As Snowflake operates with ANSI-compliant SQL, most of your current queries will run seamlessly on Snowflake without any modifications. Nevertheless, be cautious of certain conditions since Oracle uses specific extensions that may require your attention.

Let’s talk about moving the data pipelines & ETL processes.

Migrating an ETL process and pipeline from Oracle to Snowflake involves a systematic transition to accommodate the differences in data structures and query syntax between the two platforms.

Begin by analyzing the existing ETL logic and data flow, taking note of any Oracle-specific components. Update SQL queries, transformation logic, and data loading processes to align with Snowflake’s syntax and capabilities.

Modify connection configurations to ensure seamless integration with Snowflake. Thorough testing is crucial to validate the accuracy and efficiency of the migrated ETL process.

Additionally, consider leveraging Snowflake’s native features for improved performance and scalability. Finally, update any scheduling or orchestration tools to reflect the new Snowflake environment.

You can also consider incorporating Snowpipe, Snowflake’s continuous data ingestion service to automate the real-time ingestion of data.

If you need help with migration, we can help you do so. Ridgeant offers seamless data migration services combining the best of technologies and years of experience.

Here are some popular tools or services you can use to perform this migration.

List of Migration Tools: Seamlessly Transitioning from Oracle to Snowflake

Here are some popular migration tools that can be used to facilitate the migration from Oracle to Snowflake:

  1. AWS Database Migration Service (DMS):

 AWS DMS supports heterogeneous migrations, allowing for seamless movement of data from Oracle to Snowflake. It provides both one-time and continuous replication options.

  1. Talend:

Talend is an open-source ETL tool that supports data integration and migration tasks. It offers connectors for both Oracle and Snowflake, making it suitable for designing migration workflows.

  1. Informatica:

Informatica is a widely used ETL tool that simplifies complex data integration tasks. It supports Oracle and Snowflake, enabling users to design and execute migration processes.

  1. Microsoft Data Migration Assistant (DMA):

DMA is a free tool from Microsoft designed for migrating databases to Azure. While it’s built for Azure, it can be used for assessing and migrating from Oracle to Snowflake with proper configurations.

  1. Attunity Replicate:

Attunity Replicate is a data replication and integration tool that supports real-time data movement. It can be used for continuous replication from Oracle to Snowflake.

  1. Matillion:

Matillion is a cloud-native ETL tool that offers specific connectors for Oracle and Snowflake. It is designed for seamless data integration and transformation in cloud environments.

  1. Flyway:

Flyway is an open-source database migration tool that simplifies version control for database changes. It can be used to manage and execute schema migrations from Oracle to Snowflake.

  1. AWS Glue:

AWS Glue is a fully managed ETL service on Amazon Web Services. It can be used for data preparation and transformation tasks.

  1. SnapLogic:

SnapLogic is an integration platform that supports Oracle and Snowflake connectivity. It offers pre-built connectors and a visual design interface for creating migration workflows.

  1. Striim:

Striim is a real-time data integration platform that supports Oracle and Snowflake. It enables streaming data integration and can be used for continuous migration.

When selecting a migration tool, consider factors such as the complexity of your data, the scale of migration, and the specific features provided by each tool. Additionally, ensure that the chosen tool aligns with your organization’s data integration and migration requirements.

Good Read: Seamless Migration from SQL Server to Snowflake: A Comprehensive Guide

Concluding Your Oracle to Snowflake Migration Journey

The migration from Oracle to Snowflake represents a strategic leap toward a more agile, scalable, and cloud-native data management paradigm.

Throughout this guide, we’ve navigated the complex process, from understanding the motivations behind migration to exploring various methodologies and popular tools.

Whether you opt for ETL processes, leverage data modeling tools, or employ DDL scripts, each step is pivotal in ensuring a successful transition. Remember, the migration journey is not just about moving data; it’s an opportunity to optimize workflows, embrace modern analytics, and future-proof your data infrastructure.

As you begin this transformative journey, careful planning, testing, and the right choice of tools will pave the way for a seamless and efficient migration.

With your data now residing in the cloud-native ecosystem of Snowflake, you are well-positioned for the evolving landscape of data management and analytics.

Our dedicated team is here to guide you every step of the way, ensuring a smooth and efficient migration to Snowflake. Whether you’re aiming to harness the full power of Snowflake’s capabilities or seeking a seamless transition irrespective of your current data storage and platform, we’ve got you covered.


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