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What You'll Learn
What is Oracle Exadata?
Oracle Exadata is a high-performance database machine that’s the world’s most advanced architecture for Oracle Database deployments on-premises and in the cloud. It combines software and hardware to optimize database performance, storage, and networking.
It is best for enterprises that are looking for high-speed transaction processing, analytics, mixed workloads, and data warehousing. Oracle Exadata works best if you want to enhance operational efficiency and reduce IT administration.
This happens when you consolidate diverse Oracle Database workloads on Exadata platforms in enterprise data centers, Oracle Cloud Infrastructure (OCI), and multi-cloud environments.
Oracle Exadata has long been the recommended choice for organizations running high-performance Oracle databases. However, today, modern businesses like you are moving to cloud-first, scalable, solutions – a leading example of which is Snowflake.
Learn how to seamlessly migrate data from Oracle Exadata to Snowflake with this step-by-step migration guide. Learn what methods and tools you could use to facilitate seamless data transfer.
What is Snowflake?
Snowflake is a modern, cloud-based data warehousing platform which helps enterprises perform advanced analytics and report on customer data. To level up data management and query execution, the Snowflake AI Data Cloud platform leverages massively parallel processing (MPP) compute clusters.
Snowflake is built on three leading cloud platforms – Amazon Web Services, Google Cloud Platform, and Microsoft Azure. It has approximately 10,249 global customers, such as Honeywell, Open AI, and several large enterprises.
The customers have more than 5.3 billion average daily queries on the data cloud. Snowflake combines the best of both shared-disk and shared-nothing architecture.
To learn more about Snowflake’s architecture layers, visit this page.
Reasons to Migrate from Oracle Exadata to Snowflake
1. Popularity of Cloud-Native and Multi-Cloud Architecture
A recent report by Statista states that in 2024, around 50% of global businesses leveraged cloud-native in production. Almost 90% of large enterprises use a multi-cloud architecture. The shift to cloud-native and muti-cloud architecture is gaining speed.
When you work with Oracle Exadata, you get a cloud deployment option via Oracle Cloud Infrastructure. But as a modern enterprise, you might want multi-cloud flexibility to run workloads across AWS, Azure, and Google Cloud. This is possible with Snowflake.
Snowflake runs natively on AWS, Azure, and GCP and facilitates cross-cloud and cross-region data replication.
2. Reduced Operational Complexity with Fully Managed Service
To run Oracle Exadata, you require dedicated database administrators and IT teams to manage performance tuning and database optimizations.
And this is exactly why you would want to shift to a fully managed platform like Snowflake.
With Snowflake, you can enable automated performance optimization and scaleup and down without any downtime.
3. More Cost-Efficiency is Facilitated
One of the major challenges with Oracle Exadata is its total cost of ownership. Cost is incurred on hardware, software licensing, support, and maintenance. Exadata’s licensing model is based on CPU cores, feature enablement, and additional services. This often leads to unpredictable costs for enterprises. However, this problem can be easily solved with Snowflake.
The Snowflake AI Data Cloud Platform provides enterprises with a pay-as-you-go pricing model which helps reduce upfront infrastructure investments. Moreover, the compute and storage scales independently and this prevents over-provisioning. And the best part – you don’t have to spend extra on data sharing, semi-structured data support, or auto-scaling.
4. More Support for AI/ML Use Cases
Oracle Exadata is optimized for structured relational databases. But as a modern enterprise, you might want to process semi-structured (JSON, Avro, Parquet) and unstructured data. Snowflake natively supports this without any ETL transformations.
Plus, Snowflake’s Snowpark has built-in AI/ML capabilities that allow developers to use their preferred choice of language – Python, Java, and Scala directly within Snowflake’s environment. You do not need to move your data to external platforms to train and deploy machine learning models.
5. Quick & Secure Data Sharing
This is one of the powerful benefits you get by migrating from Oracle Exadata to Snowflake. When you use Oracle Exadata, you require custom ETL pipelines or Oracle-specific tools to move or distribute your data across teams. This issue is solved by Snowflake.
There’s no need for ETL processes to share data across teams and there’s instant data access for various business units, partners, or customers. The Snowflake platform provides role-based access controls that help you enhance your data governance and security.
Migrating from Oracle Exadata to Snowflake – Key Steps
Strategic Assessment
The first thing that you need to do is figure out the business drivers. Identify why you want to do this migration – whether it’s for cost reduction, performance optimization, adopt a multi-cloud strategy, or enable artificial intelligence or machine learning.
Next is to assess your current workload - categorize OLTP, OLAP, or ETL pipelines, running on Exadata. One thing that business decision-makers need to do is to analyze the total cost of ownership and ROI.
Make a list of savings you would be able to achieve on hardware, licensing costs, etc. with Snowflake’s consumption-based pricing.
Outlining Migration Strategy
Next step is to decide what migration strategy or approach you want to adopt. There are typically three types of migrations that enterprises do – lift-and-shift, re-platform, and rehost. In lift-and-shift migration, you move data as-is.
In re-platform, database architects and engineers are required to optimize schemas, queries, and workloads. Refactoring involves redesigning your applications to Snowflake’s separation of compute and storage. If you were to ignore any one approach – it could be lift-and-shift migration.
The reason is that Oracle Exadata combines compute, storage, and operating system services. One key decision that you must take while planning your migration strategy is whether you’d want to do a phased migration or a single cutover. In a phased migration approach, companies run Exadata & Snowflake in parallel for a transition period.
Schema Conversion and Data Preparation
Here you convert Oracle Data Definition Language to Snowflake-compatible structures. Oracle’s B-TREE indexes, materialized views, partitions need to be replaced with Snowflake’s automatic micro-partitioning.
Next is to convert Oracle-specific data types to Snowflake-supported equivalents. Also, one thing that database engineers and architects need to note is that Oracle PL/SQL is not supported in the Snowflake environment. Hence, you need to rewrite procedures in Snowflake scripting for SQL-based logic and Snowpark for complex ETL and ML transformations.
One more thing that data engineers need to decide is which business logic will remain in SQL and what will be handled by external processing engines like Snowpark.
Extracting & Validating Data
The fourth step involves extracting data from Oracle Exadata using Oracle Data Pump or AWS Database Migration Service to support structured datasets. Next, data engineers need to convert JSON, Avro, XML into Parquet or native Snowflake formats for efficient query performance.
Once this is done, you load data into Snowflake – either bulk load that involves one-time historical data migration or continuous sync using popular migration tools like Fivetran. As soon as the loading is completed, you need to validate all the data.
For instance, comparing row counts, aggregates, and query results in Oracle Exadata vs. Snowflake.
Query Optimization
At this stage, data engineers and architects rewrite Oracle-specific SQL constructs. Oracle’s CONNECT BY recursion is replaced with Snowflake’s recursive CTEs. CTE stands for Common Table Expressions. A recursive CTE in Snowflake joins a table to itself as many times as necessary for processing hierarchical data in the table.
The next thing is to update connections for Power BI, Tableau, or other BI & Analytics tools to Snowflake. Reports and dashboards are optimized for Snowflake’s caching and result set persistence.
Performance Testing & Security Deployments
One of the most critical steps of the entire migration process is performance testing and access control migration. You will be required to compare latency, concurrency scaling, and query execution plans between Exadata and Snowflake.
Next, map Oracle RBAC roles and privileges to Snowflake’s role-based access control (RBAC) model. You can configure data masking & row access policies for compliance.
Optimization, Optimization, and More Optimization
Whether you do a big bang migration or phased migration – continuous monitoring and optimization is a must. You can set up automated monitoring & alerting for query failures. You can implement query pruning strategies, clustering keys, and materialized views.
We recommend automating compute scaling with Snowflake’s auto-suspend/resume to optimize costs. Also, consider integrating AI/ML pipelines with Snowpark to enable predictive analytics.
What Tools to Use for Migration from Oracle Exadata to Snowflake?
You can migrate from Oracle Exadata to Snowflake by using either Snowflake’s native migration tools or third-party ETL/ELT solutions like Fivetran. With Snowflake, you get utilities for schema conversion, data ingestion, and real-time replication. For instance, Snowflake Schema Migration Utility automatically converts Oracle schema to Snowflake-compatible SQL. If you are looking to use a third-party tool, we recommend Fivetran.
Fivetran is a leading automated ETL tool that simplifies migration with real-time synchronization. It provides pre-built connectors that handle large-scale migration with automated schema evolution. Then there is real-time change data capture that continuously syncs changes from Oracle to Snowflake. Fivetran works best for large enterprises that need fully managed replication.
If you are an organization looking to do a one-time migration and your team can manage schema conversion, data extraction, and loading manually, we recommend using Snowflake native tools. If you need automated, real-time ELT with minimal engineering effort, Fivetran is the best choice.
Choose LumenData for Snowflake Migration
As a Snowflake Premier Services Partner, we help you migrate to the platform in record time. Together with Snowflake, we’ve enabled several large customers to modernize data warehousing and grow their instances. From setting up Snowflake to optimizing performance, we design solutions that fit your business—so you can make smarter decisions faster.
When you partner with LumenData, you get:
Multi-skilled delivery team with 75+ Snowflake certifications, including SnowPro Advanced, Advanced Architect, Advanced Data Engineer, and others.
End-to-end support for Snowflake Streams, SnowPark, SnowPipe, Streamlit, & more.
6–12-week QuickStart program tailored for healthcare, financial services, retail, public sector, manufacturing, higher education.
Accelerators for rapid data warehouse migration from legacy, superpipe for high-speed data ingestion, & data governance accelerators.
Our team at LumenData also has experience in implementing Snowflake x Informatica, Snowflake x Fivetran, and many other technological integrations. Check out more about our Snowflake migration services and resources here.
About LumenData:
LumenData is a leading provider of Enterprise Data Management, Cloud and Analytics solutions and helps businesses handle data silos, discover their potential, and prepare for end-to-end digital transformation. Founded in 2008, the company is headquartered in Santa Clara, California, with locations in India.
With 150+ Technical and Functional Consultants, LumenData forms strong client partnerships to drive high-quality outcomes. Their work across multiple industries and with prestigious clients like Versant Health, Boston Consulting Group, FDA, Department of Labor, Kroger, Nissan, Autodesk, Bayer, Bausch & Lomb, Citibank, Credit Suisse, Cummins, Gilead, HP, Nintendo, PC Connection, Starbucks, University of Colorado, Weight Watchers, KAO, HealthEdge, Amylyx, Brinks, Clara Analytics, and Royal Caribbean Group, speaks to their capabilities.
For media inquiries, please contact: marketing@lumendata.com.
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