Migration failures often stem from overlooking legacy system complexities, including incompatible schemas, data quality issues, and hidden dependencies that disrupt the migration process. 

Codewave initiates a comprehensive pre‑migration assessment by conducting in-depth dependency mapping, identifying all objects (tables, views, stored procedures, triggers, and indexes) in the legacy system, and profiling data quality (checking for duplicates, inconsistencies, or outdated formats). 

We create a detailed migration strategy tailored to your business, choosing the best approach (big-bang, phased, or hybrid) to minimize disruption while meeting your exact needs.

We help enterprises understand the risks of data migration by identifying bottlenecks early and aligning the migration process with business continuity goals. We ensure data transfer meets the regulatory and compliance requirements specific to your industry.

Example: A financial institution will map all its core banking data systems and assess dependencies, ensuring there are no surprises during migration and that it remains fully operational throughout the process.

Migrating between platforms often means converting data structures, a process prone to errors when schema differences aren’t addressed. Database migration, especially from legacy systems such as Oracle to open-source solutions like PostgreSQL or MySQL, requires careful schema conversion. 

Codewave ensures a smooth schema mapping process by using tools such as the AWS Schema Conversion Tool (SCT) or custom scripts, and byvalidating the accuracy of field types, primary key constraints, and foreign key relationships.

Our approach also includes data transformation, where we clean and normalize data to ensure consistency and meet the target system’s specifications. 

Data integrity checks, such as referential integrity and data type compatibility, are validated during every step of the transformation. By performing data cleansing and formatting before loading, we ensure that the target database is free of legacy errors and prepared for long-term use.

Example: A retail enterprise will convert data between SQL Server and Amazon Aurora to ensure compatibility and optimize performance in a cloud environment.

Large datasets and complex schemas make data extraction, transformation, and loading (ETL) challenging during migration. Manual processes are prone to mistakes and delays.
Codewave builds automated ETL pipelines using open-source and proprietary tools such as Apache NiFi, Talend, and AWS Glue to accurately extract, cleanse, and transform data.

We ensure data integrity during the ETL process by applying automated validation checks, including consistency checks for data accuracy, uniqueness, and completeness. Data is processed in small batches to prevent failures from overload and maintain transactional consistency.

Example: A global manufacturer will use an automated ETL pipeline to extract sales and inventory data from legacy SAP systems and load it into a cloud-based Snowflake data warehouse, ensuring accuracy and integrity while streamlining data processing time.

Data inconsistencies between the source and target databases during migration can lead to severe performance issues. Real‑time data synchronization is crucial when migrating high-transaction environments, such as financial systems or e-commerce platforms. Codewave uses Change Data Capture (CDC) to monitor changes in the source database, ensuring data integrity and minimizing downtime.

CDC tools such as Debezium, AWS DMS (Database Migration Service), or Apache Kafka capture incremental changes (inserts, updates, anddeletes) from the source database and replicate them to the target in real time. This keeps both environments synchronized, preventing data loss during migration and ensuring business operations remain continuous.

Example: A telecom company migrating from an on-premise Oracle database to a cloud-based database will use real-time synchronization. This ensures their customer records remain up to date across both systems during the migration, preventing outages or data inconsistencies.