Is Snowflake a Better Option Than Oracle for New Data Warehousing?

Many companies today are wondering if they should stay with their current Oracle systems. Or, should they switch to a cloud-born system like Snowflake? 

Oracle has long been a strong and dependable database for company use. This is especially true for transaction-based work (OLTP). However, Snowflake makes a strong argument for new analytical work (OLAP). It also fits data warehousing needs well.

Snowflake's design is cloud-born from the very start. It is constructed on public cloud setups (AWS, Azure, or GCP). Key distinct points include:

1. Division of Storage and Compute: This lets you adjust storage and compute amounts separately. You can do this flexibly, paying only for what you use. This is different from Oracle's more common design. In Oracle, compute and storage are often closely linked. This could lead to greater expenses for adjusting or for resources not fully used.
   
2. Pay-For-Use Costing: Snowflake's plan is based on real use for storage and compute. This can be more budget-friendly than Oracle's license and hardware upkeep expenses. This is particularly true for changing workloads.
   
3. Simpler Upkeep: As a fully managed SaaS product, Snowflake takes care of much of the administrative work. This work is linked to common databases. Examples are software fitting, updates, and basic adjustments. This lessens the call for deep DBA work.
   
4. Adjustability and Output: Snowflake's multi-group shared data design is made for high simultaneous use. It is also made for good output for analytical questions. It automatically adjusts to manage changing demands without a drop in output.
5. Data Flexibility: Snowflake natively accepts structured and semi-structured data. This includes JSON, Avro, XML, and Parquet within the one system. This simplifies data joining and study.
   
6. Data Sharing: Snowflake's Secure Data Sharing item lets companies easily and safely share live data. They can share with other Snowflake accounts (inside or outside). This happens without copying or shifting the data.
   
7. Modern Items: Abilities like Time Travel (getting to past data) and Zero-Copy Cloning are available. Zero-Copy Cloning means instantly making copies of databases, schemas, or tables for building or checking without copying data. Fail-safe also gives strong data safety and working nimbleness.

How to Migrate from Oracle to Snowflake: An 8-Stage Method

Migrating from Oracle to Snowflake needs careful thought and action. Here’s an orderly way to do it:

Stage 1: Look at Your Current Oracle Setup and Set Change Priorities

Before starting the change, a full look at your present Oracle configuration is important. This helps create an orderly path for the activity.

1. Pinpoint Key Parts: List all Oracle schemas, tables, views, and indexes. Also list stored procedures (PL/SQL), functions, packages, triggers, and current ETL/ELT flows.
   
2. Set Priorities by Business Effect: Look at parts based on their business importance. Note their use rate and what depends on them. This helps in spacing out the change well.
   
3. Study Intricacy: Check the complexity of PL/SQL code and difficult data types. Look at dependencies between databases and application connections. Pinpoint Oracle-only items or syntax that will need changing or new design for Snowflake. Examples are Oracle's (+) for outer joins versus ANSI SQL in Snowflake. Specific built-in functions like INSTR() versus POSITION() or REGEXP_INSTR() also need checking. This study lessens later output problems and working overhead.
   
4. Outline Change Phases: Group database items and applications into sensible change waves or phases. This method manages expenses. It permits learning step-by-step. It also lessens disturbance to business actions.

Stage 2: Design Your Snowflake Target Setup

Migrating from Oracle to Snowflake needs careful design. This is to use Snowflake’s good points.

• Schema Matching: Plan how Oracle schemas and items will correspond to Snowflake databases, schemas, tables, and views.
  
• Data Adding Plan: Settle on the best ways for initial (large) data additions. Also, plan for ongoing or step-by-step data syncing. Examples are batch ETL/ELT, change data capture (CDC), or Snowpipe for steady input.
  
• Virtual Warehouse Setup: Design Snowflake virtual warehouses (compute groups). Make them suitable for different kinds of work. Examples are data adding, reshaping, BI questioning, or data science. Think about warehouse dimensioning (T-shirt dimensions) and auto-adjusting rules.
  
• Safety and Governance: Outline roles, users, and access rights in Snowflake. These should reflect or improve your Oracle safety model. Plan for network rules, coding, and compliance needs.
  
• Code Change Plan: Plan the change of PL/SQL. Snowflake accepts SQL and JavaScript UDFs/UDTFs/Stored Procedures. It also accepts Snowflake Scripting (a SQL-based procedural language). Some difficult PL/SQL might need a new design. Or, it might need to move to an outside ordering tool or application part. Snowflake also has tools like SnowConvert to automate some of this change.

Stage 3: Get Data from Oracle

Getting data out of Oracle well and dependably is a basic stage.

Select Getting Tools/Ways:

• Oracle SQL Developer/SQL*Plus: Good for smaller, one-off exports to forms like CSV.
  
• Oracle Data Pump (expdp/impdp): Works well for large batch exports. But it can be hard for ongoing changes. It may also affect source database output.
  
• ETL/ELT Tools: Business tools (Informatica, Talend, Matillion, Fivetran, Hevo Data, Qlik Replicate) can be used. Open-source choices (Apache NiFi, Airflow) can also manage complex getting and reshaping. Many of these tools have specific links for Oracle and Snowflake.
  
• AWS Database Migration Service (DMS): Can be used for both full addition and ongoing copying (CDC) if placing in AWS S3.
  
• Custom Scripts: Python or other scripting languages with Oracle client libraries can be used.
  

Data Wholeness

Make sure of transaction wholeness. This is especially true for ongoing copying. Tools that accept CDC are usually better. They help lessen downtime and data differences.

File Forms and Placement 

Get data into common forms like CSV, Parquet, or ORC. Plan for a middle placement spot. This is usually cloud object storage (Amazon S3, Azure Blob Storage, Google Cloud Storage). Snowflake can reach this directly.

Stage 4: Reshape Data for Snowflake Suitability (ELT Way)

Snowflake is very suitable for an ELT (Extract, Load, Transform) way. Here, raw data is added to Snowflake first. Then it is reshaped using Snowflake's compute ability.

1. Add to Staging Tables: Add gotten raw data from your cloud storage spot into staging tables within Snowflake. Use the COPY INTO <table> command for this.
   
2. Data Type Change: Match Oracle data types to their Snowflake counterparts. 
   For example:
   NUMBER to NUMBER or FLOAT.
   VARCHAR2 maps to VARCHAR.
   DATE maps to DATE or TIMESTAMP_NTZ.
   CLOB maps to VARCHAR or is placed in S3. BLOB is likely placed outside.‍
3. Character Set Suitability: See that character set codings (e.g., UTF-8) are handled right. This is to avoid data damage. Snowflake mainly uses UTF-8.‍
4. SQL and Logic Reshaping:
   
   • Rewrite Oracle-only SQL questions and DML to Snowflake's SQL type (ANSI-SQL compliant).
     
   • Change PL/SQL logic to Snowflake Stored Procedures. Use JavaScript, Python (preview), Scala (preview), or Snowflake Scripting. Or, redesign the logic in your ELT tool or application part. Tools like SnowConvert can help automate code change from PL/SQL.
     
   • Deal with differences in function names, date handling, string work, and system views.
5. ‍Use Snowflake's Abilities: Use Snowflake items like VARIANT for semi-structured data. Use streams for CDC. Use tasks for scheduling reshaping.

Stage 5: Add Data into Snowflake Production Tables

Once data is placed and reshaping logic is set, add it into your target production tables in Snowflake.

• COPY INTO <table>: This is the main command for large data adding. It works from internal (Snowflake-managed) or external (S3, Azure Blob, GCS) spots into Snowflake tables. Make file dimensions good (100-250MB compressed is often suggested) for parallel adding.
  
• Snowpipe: For steady micro-batch input of data as it arrives in your placement spot, Snowpipe is a good, server-free choice.
  
• ETL/ELT Tools: Many data joining tools can order and make data adding into Snowflake better.
  
• Data Checking: After adding, check record numbers, checksums, and important data points. This is to see that data is whole between Oracle and Snowflake.

Stage 6: Change Schema Items, Code, and Applications

This stage means making the database structures. It also means rewriting or re-pointing applications.

• DDL Making: Create DDL scripts for Snowflake tables, views, sequences, etc. Base these on the reshaped Oracle DDL. Tools can help make these scripts.
  
• Code Putting In: Put changed stored procedures, UDFs, and other code items into Snowflake.
  
• Application Re-platforming:
  
  • Change database connection strings in your applications and BI tools. Make them point to Snowflake.
    
  • Alter any fixed SQL in application code. Make it suitable with Snowflake's SQL type.
    
  • Fully check application working that depends on database interaction.
    

Stage 7: Check and Test Data & Methods Fully

Full checking cannot be skipped. It is to see that a change is successful.

• Data Checking:
  
  1. By Quantity: Compare row numbers. Compare total values (SUM, MIN, MAX, AVG) for key number columns. Compare checksums between Oracle and Snowflake tables.
     
  2. By Quality: Do data sampling. Do spot-checks for data rightness and sameness.
     
  3. Referential Wholeness: Check relationships if not kept by rules in Snowflake. (Snowflake accepts setting rules but does not always keep them, except for NOT NULL).
     
• Method and Application Checking:
  
  1. Check all changed ETL/ELT jobs and data flows from start to finish.
  2. Check BI reports and dashboards for rightness and output.
  3. Do User Acceptance Testing (UAT) with business users. This is to confirm that the new system meets their needs and hopes.
     
• Output Checking: Run typical query workloads. This is to find output blockages. Make Snowflake virtual warehouse setups, query design, and clustering keys better if needed.

Stage 8: Put into Snowflake, Set Up Access, Go Live, and Take Out of Service

The last part means switching over to the new Snowflake setup.

• Switchover Plan: Plan your switchover with care. For example, a phased introduction by application or department. Or, run both for a time. Or, a direct "big bang" switchover. This often means a final data syncing.
  
• Final Data Sync: Do a final step-by-step data addition from Oracle to Snowflake. This is to get any changes since the last main addition.
  
• User Access and Rights: Put in place the set safety model. See that users have proper access to data and items in Snowflake.
  
• Watching and Making Better: Once live, always watch Snowflake use (credits), query output, and storage. Use Snowflake's built-in watching tools or outside solutions. Make setups better as called for.
  
• Papers and Instruction: Write down the new setup, data flows, and working ways. Give instruction to users and support people on Snowflake.
  
  
  • Take Oracle Out of Service: After a good switch and settling time, you can plan to take the old Oracle systems out of service. This is when you are sure about the Snowflake setup.