An increasing number of customers are transferring their Oracle workloads to MongoDB due to the multitude of benefits MongoDB offers. MongoDB Atlas provides several advantages, such as:

1. Enhanced development speed

2. Intuitive data organization using document formatting

3. Horizontal Scalability

4. High performance

5. Consolidation of disparate solutions and simplification of data environments

What does an Oracle Migration entail?

Key Aspects of an Oracle Migration

Migrating Oracle databases requires a comprehensive approach that covers various aspects. Typical areas that must be addressed during migrations include:

Wekan has a portfolio of tools that help you with each stage of the Oracle migration journey…

Discovery

Database Discovery:

Designing effective MongoDB schemas can be challenging, especially when dealing with numerous source Oracle tables. Understanding the Oracle landscape and access patterns requires significant time and input from subject matter experts (SMEs) across various teams. 

To streamline this process, we have tools that provide comprehensive insights into Oracle usage and data access patterns

**Database - Discovery & InSights**

Our Oracle Intelligence tool offers valuable insights into the source Oracle workload to aid in the accurate design of the target MongoDB schemas. It analyzes the following key areas:

1. Oracle Data Profile: This component queries the source Oracle database to extract:

• List of tables, including column count and row count

• Table relationships (e.g., one-to-one, one-to-many, many-to-many)

• Identification of read-heavy and write-heavy tables

• Cardinality of relationships

• Stored Procedures 

• Indexes

2. Data Access Profile: By examining Oracle query logs, this tool identifies:

• Queries, sub-queries and their performance

• Frequently joined tables

• Frequently queried fields

• Types of joins

Application Discovery:

Applications that incorporate a well-defined Data Access Layer (DAL) tend to be more straightforward to migrate to alternative databases. However, real-world production applications often deviate from standard architectural patterns.

In our analysis of application code, we focus on identifying several key aspects:

• Entity Models : Understanding how data entities are structured within the application.

• Inline SQL Queries : Identifying direct SQL queries embedded within the code.

• Data Type Conversions : Recognizing any necessary conversions between data types.

• Date Formats: Noting how date and time information is handled.

• Number of APIs : Assessing the quantity and nature of application programming interfaces (APIs) used.

• Access Patterns : Analyzing how data is accessed and utilized within the application.

These tools play a crucial role in gaining a clear understanding of the level of effort required for migrating application code to a new database environment.

Design

MongoDB Design:

Our Database AI Designer plays a pivotal role in expediting the database design process by leveraging insights gathered from our Database Discovery tool. This innovative tool harnesses advanced algorithms to delve deep into your data landscape, extracting crucial information that informs the design decisions made by our AI Designer.

The AI Designer focuses on several key areas to ensure optimal MongoDB schema and query design:

• Optimized Schema/Data Model: By analyzing the data profiles and access patterns discovered through the Database Discovery tool, the AI Designer recommends an optimized schema and data model for MongoDB. It understands relationships between entities, identifies hierarchical structures, and ensures efficient storage and retrieval of data.

• Index Creation: Recognizing the importance of indexing for query performance, the AI Designer automatically suggests appropriate indexes based on query patterns and access frequencies observed in the source data.

• SQL to MongoDB Query Conversion: For applications migrating from SQL databases, the AI Designer seamlessly converts SQL queries into MongoDB queries, ensuring compatibility and efficiency in data retrieval.

• Transformation of SQL Operators: When dealing with stored procedures or complex SQL operations, the AI Designer translates SQL operators (such as logical and relational operators) into their equivalent MongoDB operators, preserving functionality and performance in the MongoDB environment.

By harnessing the capabilities of our Database AI Designer, we are able to streamline the transition to MongoDB.

Application Refactor:

The Code AI Designer focuses on the below areas to facilitate the migration of applications to MongoDB. They provide the following functionalities:

1. Streamlining the creation of read and write interfaces for MongoDB collections to expedite application setup.

2. Implementing decorators for seamless data type conversions from SQL formats to MongoDB equivalents.

Data Migration:

Transitioning from an Oracle database to MongoDB presents several complex challenges:

1. Schema Transformation: Moving data between these systems necessitates writing code to convert the schema, which can be intricate and time-consuming.

2. Change Capture: Continuously capturing and propagating ongoing changes (such as updates, inserts, or deletions) from the source to the destination database often requires custom development efforts.

3. Managing Dependencies: Coordinating dependencies between various migration tasks and maintaining the migration infrastructure can become unwieldy and prone to errors.

4. Data Validation: Verifying the integrity and accuracy of migrated data becomes increasingly time-consuming as the volume of records grows.

Wekan offers a comprehensive suite of migration tools designed to address these challenges effectively. Organizations can adopt an approach that best fits their specific needs, whether it's a one-time data transfer or ongoing synchronization to keep data consistent between Oracle and MongoDB environments. This flexibility streamlines the migration process and ensures data integrity throughout the transition.

Validation and Performance Optimization:

Customers require the target state schema designs, and query patterns to be performant so as to assure the new modernization data layer offers them the right foundation to grow upon. 

Wekan has the below tools available..

Performance Testing Tool and Data Generator

The Performance Testing Tool includes a Data Generator, which accelerates the creation of large, customizable datasets closely resembling real-world scenarios based on specific customer schemas.

This tool also facilitates MongoDB performance testing by simulating custom load profiles and executing targeted queries against generated datasets. This allows for a deep understanding of MongoDB's performance characteristics, enabling fine-tuning of schemas and indexes as needed prior to a freeze.

Key features of this integrated solution include:

Comprehensive Functionality: Combines data generation and performance analysis within a single tool, eliminating the need for multiple tools.
  
Realistic Data Generation: Produces datasets that closely mimic real-world data scenarios.
  
Schema Customization: Tailors generated data to match specific customer schemas.
  
Performance Analysis: Identifies the overall performance of the application endpoint and the database queries to identify bottlenecks impaction application performance.



Wekan offers expert, hands-on support for application migration, providing the flexibility to either independently manage migrations with minimal customer oversight or collaborate closely with customer engineering teams in a co-development model. This approach allows for tailored migration solutions that meet the specific needs and preferences of each customer.

Proof Point (Use the slide deck shared for data points)