How to Migrate Data from Oracle to Snowflake Within Minutes?

How can you simplify complex data migrations?

Oracle powers on-premise data architectures but faces challenges with scalability, maintenance, and flexibility. In contrast, Snowflake is a cloud-native warehouse designed for scalability, parallel processing, and optimized storage.

This guide breaks down two methods to set up Oracle–Snowflake integration:

• Automated ELT pipeline using Hevo Data
• Manual ETL process

We’ll also discuss the benefits, best practices, and common challenges of Oracle–Snowflake integration to provide a complete understanding of complex data migrations.

What Are the Methods to Set Up Oracle to Snowflake Integration?

Oracle Data Pump exports and custom SQL scripts provide a traditional way to move data from Oracle to Snowflake. However, modern integration platforms streamline this process by automating pipeline setup and enabling real-time synchronization.

Below, we discuss two methods to set up Oracle to Snowflake integration:

• Using the manual ETL process
• Using Hevo’s automated data pipeline

Method 1: Using Hevo Data to Set up Oracle to Snowflake Integration

Hevo uses log-based CDC, automatic schema drift handling, and Snowflake Partner Connect to simplify Oracle to Snowflake integration. Here’s how to set it up:

Prerequisites:

• Oracle database access: Host/IP, port (1521), service name or SID, and system privileges (e.g., SELECT ANY TABLE). For CDC, access to Oracle redo logs.
  
• Snowflake account: Permissions for USAGE on database, schema, warehouse, plus CREATE TABLE, CREATE STAGE, and INSERT.
  
• Hevo account: Active workspace with Oracle Source Connector and Snowflake Destination Connector enabled.
  
• Network access: Ensure Hevo can connect to Oracle. 

1. For on-prem, open firewall/VPN. 
2. For cloud-hosted, whitelist Hevo IPs. Enable SSL for security.

Step 1: Configure Oracle as the source

The workflow:

• Add Oracle as the source connector in Hevo to begin the process.

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• Provide the following specifications:

1. Pipeline name
2. Host/IP and Port (default Oracle port: 1521)
3. Credentials with read access to required schemas
4. Service Name or SID

• Hevo validates the connection and fetches metadata, such as schemas, tables, and columns.
• This setup allows Hevo to understand the structure of Oracle tables before extraction.

Step 2: Connect Snowflake as the destination

The workflow:

• Now, configure Snowflake as the target destination.

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• The specifications required are:

1. Destination name
2. Snowflake account URL
3. Warehouse, Database, Schema
4. Username
5. Security key

• Hevo uses Snowflake’s COPY INTO command for bulk loading, staging data temporarily in cloud storage.
• Data staging ensures data is loaded in compressed, parallelized batches for better outcomes.

Step 3: Configure the pipeline

The workflow:

• Select the tables and custom queries in Oracle that you want to replicate.
• Hevo offers two loading modules:

1. Full load: Extracts existing records from Oracle and loads them into Snowflake, ensuring the target contains the complete dataset.
2. CDC: Captures new or changed records (INSERT, UPDATE, DELETE) from Oracle’s redo logs and syncs them into Snowflake in near real time.

• Pipeline configuration ensures initial migration and real-time synchronization of Oracle and Snowflake.

Step 4: Data transformation and schema mapping

The workflow:

• Hevo imports schema definitions into the pipeline when Oracle tables are ingested.
• For each column, Hevo retains the source-defined data type wherever possible.

Hevo applies internal transformations to avoid failure while loading:

• Compatible types are transferred to Snowflake in their native form.
• Incompatible types are either converted into Snowflake equivalents (commonly STRING) or promoted to a broader type if auto-mapping is enabled.
• Schema evolution syncs new columns from Oracle to Snowflake automatically.

Step 5: Initial load and incremental sync

The workflow:

• The tool performs an initial batch load of Oracle data into Snowflake.
• After the initial load, Hevo uses query-based CDC to fetch new inserts, updates, and deletes.
• Next, checkpoints are created to check database reliability and ensure no data is missed during syncs.

This step builds a simple setup, has less impact on Oracle, and doesn’t require specialized permissions.

Step 6: Monitoring and error handling

The workflow:

• The Hevo dashboard provides visibility into:

• Pipeline health (throughput, latency, error rates)
• Row-level error logs (e.g., rejected rows due to schema mismatch)
• Alerts for failures and lags

• The auto-mapping feature detects changes in Oracle and updates them in the Snowflake target schema.
• Get 24/7 technical support to troubleshoot issues such as configuration errors, schema changes, and CDC failures.

The entire workflow ensures reliability and minimal manual intervention once the pipeline is running.

Hear from Hevo customers:

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Method 2: Manual ETL Process to Set Up Oracle to Snowflake Integration

Manual ETL involves building a custom pipeline to transfer data from Oracle to Snowflake. Here’s how it works:

Step 1: Extract data from Oracle

The process starts with exporting Oracle data into a flat file format.

• Use SQL Plus or Python scripts to export data from Oracle tables.
• Save extracted data in CSV, Parquet, or other compatible formats.
• Use incremental queries to fetch only changed or new records.

Exporting data to files ensures portability and tracks incremental extraction, reducing load on source systems. SQL Plus redirects query output to a file using the SPOOL command:

• SET COLSEP defines the column delimiter.
• SPOOL writes query output to a file.
• Incremental SQL filters rows modified since the last ETL run.

Step 2: Transform and standardize data

The workflow:

• Oracle data types are mapped to Snowflake equivalents.
• Date/time formats are standardized to handle complex data types (CLOB and BLOB).
• Character encoding is checked to ensure compatibility (UTF-8 or UTF-16). Preferably, UTF-8.

Snowflake supports standard SQL constraints (PRIMARY KEY, UNIQUE, NOT NULL) and complex types (ARRAY, VARIANT).

Step 3: Stage files in cloud storage

Snowflake loads data in parallel from staged files by running the following programme:

For external staging:

• Transformed files are uploaded to Snowflake’s internal stage or an external cloud stage (S3, Azure Blob).
• Staging enables Snowflake to parallelize loads and optimize query performance.

Step 4: Load data into Snowflake

The workflow:

• Using the COPY INTO command, data is loaded from staged files to target tables.
• The command handles file formats, delimiters, date/time formats, and compression.

COPY INTO ingests bulk data, allowing Snowflake to parse, validate, and store data:

• Multiple files are loaded at once.
• You can filter and load specific files using patterns, for example: PATTERN=’.*\.csv’.
• Cloud staging and parallel loading accelerate data transfer and lower compute usage.

Step 5: Handle incremental loads

This step ensures that only new or updated records are applied.

• Incremental data is first loaded into a temporary table.
• The changes are applied to the final table using SQL operations like MERGE, or UPDATE + INSERT.

Snowflake supports row-level operations and the MERGE statement:

• Compares students_table (target) with landing_table (staging).
• Matches rows using id.
• Ensures the target table stays in sync with new data.

Limitations of the Manual ETL Process

The limitations include:

• Cost: The cost of hiring an ETL Developer to construct an Oracle to Snowflake ETL pipeline might not be favorable in terms of expenses.

• Ongoing maintenance: Manual ETL pipelines require constant updates to handle Oracle schema changes, new data types, and Snowflake updates. Dependency upgrades (e.g., JDBC drivers) often break pipelines, consuming valuable engineering time.

• Scalability: A sudden 10X surge in Oracle logs may force query optimization and code rewrites because manual ETL pipelines don’t support auto-scaling.

• Error handling: Manual ETL setups lack robust error handling, logging, and monitoring. Detecting schema drift, failed loads, or data mismatches demands custom scripts and manual checks, which increases downtime risks.

• Latency: Batch-based processing often delays data availability in Snowflake, and achieving near real-time replication demands complex setups with log-based CDC.

Getting to Know Oracle

Oracle Database is a robust relational database management system (RDBMS) known for its scalability, reliability, and advanced features like high availability and security. Oracle offers an integrated portfolio of cloud services featuring IaaS, PaaS, and SaaS, posing competition to big cloud providers. The company also designs and markets enterprise software solutions in the areas of ERP, CRM, SCM, and HCM, addressing a wide range of industries such as finance, health, and telecommunication institutions.

Key Features of Oracle

1. Multimodel database: It has the facility to facilitate multiple data models, including relational, document, graph, and key-value, hence giving flexible facilities in managing diversified kinds of data.
2. ACID Compliance: Guarantees transaction integrity through Atomicity, Consistency, Isolation, and Durability; hence, it is reliable for critical business operations.
3. High Availability: Advanced features such as Real Application Clusters (RAC), Data Guard, and Flashback Technology ensure continuous uptime, disaster recovery, and data protection.

Getting to Know Snowflake

Snowflake is a cloud-based data warehousing platform designed for modern data analytics and processing. Snowflake separates compute, storage, and services. Therefore, they may scale independently with a SQL data warehouse for querying and analyzing structured and semi-structured data stored in Amazon S3 or Azure Blob Storage.

Advantages of Snowflake

• Scalability: Using Snowflake, you can automatically scale the compute and storage resources to manage varying workloads without human intervention.
• Supports Concurrency: Snowflake delivers high performance when dealing with multiple users supporting mixed workloads without performance degradation.
• Efficient Performance: Snowflake’s unique architecture allows for optimized query performance, with particular techniques applied in columnar storage, query optimization, and caching.

Why Snowflake Over Oracle?

• Cloud-native architecture: Snowflake is designed specifically for the cloud, offering superior scalability, flexibility, and performance.
• Separation of storage and compute: Snowflake allows independent scaling of storage and compute resources, enabling cost-effective usage and significant savings.
• Cost efficiency: You only pay for the compute and storage you actually use, avoiding unnecessary expenses.
• Simplicity and ease of use: Snowflake requires minimal maintenance, making it ideal for large-scale data operations.
• Support for diverse data types: Snowflake can handle a wide variety of data types, enhancing its versatility for modern data needs.
• Data sharing and collaboration: Snowflake’s robust data-sharing capabilities enable secure and seamless collaboration across teams.
• Strong security features: Snowflake provides enterprise-grade security to ensure safe data management and compliance.

Choosing between data platforms is crucial, especially when integrating Oracle with databases such as Snowflake or Databricks to enhance your data architecture.

Limitations of the Manual ETL Process

Here are some of the challenges of migrating from Oracle to Snowflake.

• Cost: The cost of hiring an ETL Developer to construct an Oracle to Snowflake ETL pipeline might not be favorable in terms of expenses. Method 1 is not a cost-efficient option.
• Maintenance: Maintenance is very important for the data processing system; hence, your ETL codes need to be updated regularly due to the fact that development tools upgrade their dependencies, and industry standards change. Also, maintenance consumes precious engineering bandwidth,h which might be utilized elsewhere.
• Scalability: Indeed, scalability is paramount! ETL systems can fail over time if conditions for processing fail. For example, what if incoming data increases 10X? Can your processes handle such a sudden increase in load? A question like this requires serious thinking while opting for the manual ETL Code approach.

If you are using tools like Oracle SQL Developer, then you can export data to CSV using Oracle SQL Developer and use it to migrate it to Snowflake.

Benefits of Replicating Data from Oracle to Snowflake

Many business applications are replicating data from Oracle to Snowflake, not only because of the superior scalability but also because of the other advantages that set Snowflake apart from traditional Oracle environments. Many businesses use an Oracle to Snowflake converter to help facilitate this data migration. 

Some of the benefits of data migration from Oracle to Snowflake include:

• Snowflake promises high computational power. In case there are many concurrent users running complex queries, the computational power of the Snowflake instance can be changed dynamically. This ensures that there is less waiting time for complex query executions.
• The agility and elasticity offered by the Snowflake Cloud Data Warehouse solution are unmatched. This gives you the liberty to scale only when you need and pay for what you use. 
• Snowflake is a completely managed service. This means you can get your analytics projects running with minimal engineering resources. 
• Snowflake gives you the liberty to work seamlessly with Semi-structured data. Analyzing this in Oracle is super hard.

Oracle to Snowflake Migration Best Practices

1. Assessment and Planning: Begin by thoroughly assessing your Oracle database. Understand the data volume, complexity, and dependencies. This will help you plan the migration more effectively.
2. Data Cleansing: Before migration, ensure your data is clean and consistent. It’s easier to handle data issues before they enter Snowflake.
3. Incremental Migration: Instead of moving everything at once, consider an incremental approach. This allows you to validate the data at each stage and ensures a smoother transition.
4. Data Transformation: Leverage Snowflake’s capabilities to transform your data during migration. This could involve normalizing data types, applying business logic, or re-architecting the data model for better performance in Snowflake.
5. Testing and Validation: After migrating, testing and validating your data is crucial. This step ensures that the data has been accurately transferred and performs well in Snowflake.
6. Optimize and Automate: Post-migration, look for opportunities to optimize your Snowflake setup. Automate regular tasks like data loading and transformation using Snowflake’s built-in features and third-party tools.
7. Use CDC: Make sure Oracle CDC is enabled while replicating data to Snowflake to ensure real-time data sync for faster insights.

By following these best practices, you can ensure a seamless migration from Oracle to Snowflake, setting your data up for success in its new environment.

Common Challenges of Migration from Oracle to Snowflake

Let us also discuss the common challenges you might face while migrating your data from Oracle to Snowflake.

1. Performance Tuning: Optimizing Snowflake performance to Oracle’s performance levels requires knowledge of Snowflake’s capabilities and the tuning configurations it offers, among many other special features such as clustering keys and auto-scaling.
2. Architectural Differences: Oracle has a traditional on-premises architecture, while Snowflake has a cloud-native architecture. This makes adapting existing applications and workflows developed for one environment into another quite challenging.
3. Compatibility Issues: Oracle and Snowflake have differences in SQL dialects, data types, and procedural languages that will have to be changed in queries, scripts, and applications to be migrated for compatibility and optimal performance.

Learn More About:

• RDS Oracle PostgreSQL Integration
• How to migrate data from AWS RDS Oracle to BigQuery
• How to migrate your data from AWS RDS Oracle to Snowflake

Conclusion

In this article, you have learned about two different approaches to set up Oracle to Snowflake Integration. The manual method involves the use of SQL*Plus and also staging the files to Amazon S3 before copying them into the Snowflake Data Warehouse. This method requires more effort and engineering bandwidth to connect Oracle to Snowflake. Whereas, if you require real-time data replication and looking for a fully automated real-time solution, then Hevo is the right choice for you. The many benefits of migrating from Oracle to Snowflake make it an attractive solution. 

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FAQs to connect Oracle to Snowflake