Oracle to Snowflake Migration Guide¶

Phase 1: Planning and Design¶

This initial phase is critical for establishing the foundation of a successful migration. Migrating from Oracle involves significant architectural shifts, and a thorough plan is essential to align stakeholders, define scope, and prevent budget overruns and missed deadlines.

Your Actionable Steps:

• Conduct a Thorough Assessment of Your Oracle Environment:

  • Inventory & Analyze: Catalog all database objects, including schemas, tables, views, materialized views, indexes, packages, procedures, functions, and triggers. Use Oracle’s data dictionary views (DBA_OBJECTS, DBA_SOURCE, DBA_TABLES, etc.) to gather this metadata.

  • Analyze Workloads: Use Oracle’s Automatic Workload Repository (AWR) reports and dynamic performance views (V$SQL, V$SQLAREA) to identify query patterns, user concurrency, performance bottlenecks, and resource utilization. This data is crucial for designing your Snowflake virtual warehouse strategy.

  • Identify Dependencies: Map all upstream data sources (ETL/ELT jobs, data streams) and downstream consumers (BI tools, applications, reporting services). Pay special attention to applications that rely heavily on PL/SQL packages.

• Define the Migration Scope and Strategy:

  • Prioritize Workloads: Classify workloads by business impact and technical complexity. Start with a high-impact, low-complexity workload (e.g., a specific data mart) to demonstrate value and build momentum.

  • Choose a Migration Approach: Decide between a “lift and shift” approach for a faster migration or a re-architecture approach to modernize and optimize data models, ETL/ELT pipelines, and procedural logic.

• Develop the Project Plan:

  • Establish a Team: Create a migration team with clear roles (Project Manager, Data Engineer, Oracle DBA, Snowflake Architect, Security Admin, Business Analyst).

  • Create a Timeline: Define realistic timelines and milestones for each of the nine phases.

  • Define Success Metrics: Establish clear KPIs to measure success, such as cost reduction, query performance improvement, increased concurrency, and user satisfaction.

Phase 2: Environment and Security¶

With a solid plan, the next step is to prepare the Snowflake environment and translate Oracle’s security model. This involves setting up accounts, networking, and a new role-based access control (RBAC) structure.

Your Actionable Steps:

• Set Up Your Snowflake Account:

  • Choose Edition and Cloud Provider: Select the Snowflake edition (e.g., Standard, Enterprise, Business Critical) that meets your security and feature requirements. Choose a cloud provider (AWS, Azure, or GCP) and region that aligns with your cloud strategy and minimizes latency to your users and other cloud services.

  • Design a Warehouse Strategy: Based on the workload analysis from Phase 1, create an initial set of virtual warehouses. Isolate different workloads (e.g., WH_LOADING, WH_TRANSFORM, WH_BI_ANALYTICS) to prevent resource contention. Start with T-shirt sizes (e.g., X-Small, Small) and plan to resize them based on performance testing.

• Implement the Security Model:

  • Map Oracle Users/Roles to Snowflake Roles: Translate Oracle’s user, role, and privilege model into Snowflake’s hierarchical RBAC model. This is a significant shift, as Oracle’s granular system-level and object-level privileges do not map directly. Create a hierarchy of functional roles (SYSADMIN, SECURITYADMIN) and access roles (BI_READ_ONLY, ETL_READ_WRITE).

  • Configure Network Policies and Authentication: Set up network policies to restrict access to trusted IP addresses (e.g., your corporate network or VPN). Configure authentication methods, such as federated authentication (SSO) with an identity provider like Okta or Azure AD.

Phase 3: Database Code Conversion¶

This phase involves converting Oracle’s DDL, DML, and extensive PL/SQL codebase to be compatible with Snowflake. This is often the most complex and time-consuming phase of the migration.

Your Actionable Steps:

• Convert DDL (Data Definition Language):

  • Tables and Views: Extract CREATE TABLE and CREATE VIEW statements from Oracle. Convert Oracle-specific data types to their Snowflake equivalents (see Appendix 2).

  • Remove Oracle-Specific Clauses: Eliminate Oracle-specific physical storage clauses like TABLESPACE, PCTFREE, INITRANS, STORAGE, and complex partitioning/indexing schemes. Snowflake manages storage and data layout automatically.

  • Re-implement Constraints: Snowflake enforces only NOT NULL constraints. PRIMARY KEY and UNIQUE constraints can be defined but are not enforced; they serve primarily as metadata for BI tools and optimizers. FOREIGN KEY constraints are not supported. All data integrity logic must be moved into your ETL/ELT processes.

• Convert DML (Data Manipulation Language) and Procedural Code:

  • Rewrite PL/SQL: Oracle’s PL/SQL (packages, procedures, functions, triggers) must be completely rewritten. Common targets include Snowflake Scripting (SQL), JavaScript UDFs/UDTFs/Procs, or externalizing the logic into a transformation tool like dbt or an orchestration service like Airflow.

  • Translate SQL Functions: Map Oracle-specific functions to their Snowflake counterparts (e.g., SYSDATE becomes CURRENT_TIMESTAMP(), NVL becomes IFNULL, VARCHAR2 becomes VARCHAR). See Appendix 3 for common mappings.

  • Replace Sequences: Re-create Oracle sequences using Snowflake’s SEQUENCE object.

  • Handle MERGE Statements: Review and test MERGE statements carefully, as the syntax and behavior can differ slightly between Oracle and Snowflake.

Phase 4: Data Migration¶

This phase focuses on the physical movement of historical data from your Oracle database to Snowflake tables. The most common approach involves extracting data to files and loading them via a cloud storage stage.

Your Actionable Steps:

• Extract Data from Oracle to Files:

  • Use methods like Oracle Data Pump, SQL*Plus spooling, or UTL_FILE to extract table data to a structured file format (e.g., Parquet, compressed CSV).

  • For very large databases, consider using third-party data integration tools (e.g., Fivetran, Matillion, Talend, Informatica) that can efficiently extract data from Oracle.

• Upload Data to a Cloud Storage Stage:

  • Transfer the extracted files to a cloud storage location (Amazon S3, Azure Blob Storage, or Google Cloud Storage) that will serve as an external stage for Snowflake.

• Load Data from Stage into Snowflake:

  • Create External Stages: In Snowflake, create an external stage object that points to the cloud storage location containing your data files.

  • Use the COPY INTO Command: Use Snowflake’s COPY INTO <table> command to load the data from the stage into the target Snowflake tables. This command is highly performant and scalable.

  • Leverage a Sized-Up Warehouse: Use a dedicated, larger virtual warehouse for the initial data load to accelerate the process, then scale it down or suspend it afterward to manage costs.

Phase 5: Data Ingestion¶

Once the historical data is migrated, you must re-engineer your ongoing data ingestion pipelines to feed data directly into Snowflake.

Your Actionable Steps:

• Migrate Batch ETL/ELT Jobs:

  • Update existing ETL jobs (in tools like Oracle Data Integrator, Informatica, or Talend) to target Snowflake as the destination. This involves changing the connection details and rewriting Oracle-specific SQL overrides to use Snowflake’s dialect.

• Implement Continuous Ingestion:

  • For continuous data loading, configure Snowpipe to automatically ingest files as they arrive in your cloud storage stage. This is an ideal replacement for micro-batch jobs.

• Utilize the Snowflake Ecosystem:

  • Explore Snowflake’s native connectors for platforms like Kafka and Spark, or leverage partner tools to simplify direct data streaming and change data capture (CDC) from Oracle.

Phase 6: Reporting and Analytics¶

This phase involves redirecting all downstream applications, particularly BI and reporting tools, to query data from Snowflake.

Your Actionable Steps:

• Update Connection Drivers: Install and configure Snowflake’s ODBC/JDBC drivers on servers hosting your BI tools (e.g., Tableau Server, Power BI Gateway, Oracle Analytics Server).

• Redirect Reports and Dashboards:

  • In your BI tools, change the data source connection from Oracle to Snowflake.

  • Test all critical reports and dashboards to ensure they function correctly.

• Review and Optimize Queries:

  • Many dashboards contain custom SQL with Oracle-specific hints or functions. Review and refactor these queries to use standard SQL and leverage Snowflake’s performance features. Use the Query Profile tool in Snowflake to analyze and optimize slow-running reports.

Phase 7: Data Validation and Testing¶

Rigorous testing is essential to build business confidence in the new platform and ensure data integrity and performance meet expectations.

Your Actionable Steps:

• Perform Data Validation:

  • Row Counts: Compare row counts between source tables in Oracle and target tables in Snowflake.

  • Cell-Level Validation: For critical tables, perform a deeper validation by comparing aggregated values (SUM, AVG, MIN, MAX) or using checksums on key columns.

• Conduct Query and Performance Testing:

  • Benchmark Queries: Execute a representative set of queries against both Oracle and Snowflake and compare results and performance.

  • BI Tool Performance: Test the load times and interactivity of key dashboards connected to Snowflake.

• User Acceptance Testing (UAT):

  • Involve business users to validate their reports and perform their daily tasks using the new Snowflake environment. Gather feedback and address any issues.

Phase 8: Deployment¶

Deployment is the final cutover from Oracle to Snowflake. This process should be carefully managed to minimize disruption to business operations.

Your Actionable Steps:

• Develop a Cutover Plan:

  • Define the sequence of events for the cutover. This includes stopping ETL jobs pointing to Oracle, performing a final data sync, redirecting all connections, and validating system health.

• Execute the Final Data Sync:

  • Perform one last incremental data load to capture any data changes that occurred during the testing phase.

• Go Live:

  • Switch all production data pipelines and user connections from Oracle to Snowflake.

  • Keep the Oracle environment in a read-only state for a short period as a fallback before decommissioning it.

• Decommission Oracle:

  • Once the Snowflake environment is stable and validated in production, you can decommission your Oracle database servers to stop incurring license and maintenance costs.

Phase 9: Optimize and Run¶

This final phase is an ongoing process of managing performance, cost, and governance in your new Snowflake environment. The goal is to continuously refine your setup to maximize value.

Your Actionable Steps:

• Implement Performance and Cost Optimization:

  • Right-Size Warehouses: Continuously monitor workload performance and adjust virtual warehouse sizes up or down to meet SLAs at the lowest possible cost.

  • Set Aggressive Auto-Suspend Policies: Set the auto-suspend timeout for all warehouses to 60 seconds to avoid paying for idle compute time.

  • Use Clustering Keys: For very large tables (multi-terabyte), analyze query patterns and define clustering keys to improve the performance of highly filtered queries.

• Establish Long-Term FinOps and Governance:

  • Monitor Costs: Use Snowflake’s ACCOUNT_USAGE schema and resource monitors to track credit consumption and prevent budget overruns.

  • Refine Security: Regularly audit roles and permissions to ensure the principle of least privilege is maintained. Implement advanced security features like Dynamic Data Masking and Row-Access Policies for sensitive data.

Appendix 1: Snowflake vs. Oracle Architecture¶

Appendix 2: Data Type Mappings¶

Appendix 3: SQL & Function Differences¶