How can you handle data migration projects with multiple sources?

After a rich assessment, you must be able to prioritize your sources. Remember, migration from multiple sources could lead to inconsistency of data, so a good perspective on metadata will help you to prevent any possible conflicts and by correctly prioritizing sources, you can take action for any conflict resolution without any risk. So, in this step, metadata is more important than data. focus on them

KYD...Know your Data First we should complete schema, user, infrastructure segregations and parallel do the assessment of Compatibility between the source data store and sync. Compatibility in the means of infrastructure, db object compatibility, Database vs Application API compatibility etc. Once done with the assessment, need to have an assessment report. This report needs to be discussed with the Team along with Data Architect.

At my previous job, we had a data migration project where we were consolidating customer data from different legacy systems into a new SQL Server database. We started by mapping out the data structures from each source system and then created scripts to transform and load the data into the new database. One of the challenges we faced was ensuring that the data from the different sources was compatible and could be seamlessly integrated into the new SQL Server database. We had to deal with data format inconsistencies, duplicate records, and ensuring data integrity throughout the migration process. It required a lot of careful planning and testing to address these challenges effectively.

Assess sources upfront. Catalog systems detailing data format, schema, semantics, volume, interfaces, dependencies. Identifies schema diffs, extract methods needed, data volume for batch approach. Frames complexity, highlights focus areas like schema mapping, staged migration. Inventory becomes reference for migration design. Saves significant time identifying needs early. Design clear data mappings across sources and target. Automate transfers, validate quality. Test thoroughly in stages; monitor closely, have rollback plan. Retain legacy read-only as backup. Document thoroughly for repeatability. Evaluate for optimization. Key is understanding sources, mapping carefully, testing completely, and contingency planning

Evaluar las fuentes de datos es un paso crítico en cualquier proyecto de gestión de datos. Este proceso implica analizar la calidad, relevancia y confiabilidad de las fuentes de datos que se utilizarán para informar y respaldar la toma de decisiones. Los profesionales encargados de la evaluación de fuentes de datos deben considerar la integridad de los datos, la frecuencia de actualización, la consistencia y la precisión

Refer to Metadata and of course, make sure you test any suspicious mapping. these tests could be run as unit/module tests or E2E tests. so it's obvious that it's really hard to plan a solid and unified test scenario for all operations. Business logics dictate first but do not be careless about platform/technology/version differences

Designing data mapping for integration: Source and Target Structures: Clearly define structures of source and target data. Field-Level Mapping: Map each field from source to target. Data Transformation Rules: Specify needed transformations. Handling Null Values: Decide on null value handling. Incremental Loads: Plan for incremental data loads. Error Handling: Establish error detection and resolution. Testing Procedures: Develop procedures for data validation. Scalability: Ensure scalability for future growth. Documentation: Document the mapping design. Collaboration: Collaborate with stakeholders for insights. This ensures a seamless flow between source and target systems.

>Documented target CRM schema and compared to source schemas to create mapping specs. This identified adding custom fields to CRM to capture all source data. >Map source customer ID fields to new CRM customer ID to link records across systems. >Used different logic for handling duplicate customer data across sources, preferring newer marketing platform data. >Normalized address data into target CRM fields, using ETL tools for cleansing. >Planned joining ticket data to customer accounts in CRM using pipeline key. >Called out any transformations needed for data like concatenating first/last name into name field. >Detailed mapping decisions in the event of gaps or conflicts across sources. The clearer the mappings,smoother the implementation.

Migration is always a tricky tasks and needs proper planing and execution methodologies. Discovering correct source for data extraction and then using right migration tool be it is native or third party plays an important role in migration. Again, migration with performance and security is an important aspect. Doing data migration having feature of data encryption and decryption would be advisable considering the sensitivity of the data and business. Hence, right strategies during migration plannig is required.

you will have limited choice,BigBang or Incremental, but at the last step in all scenarios,you should consider the cutover plan. this term will guide you to choose the correct option. regardless of the medium,online/offline, or other options, you must consider the business continuity. each method could have impacts on a real-time business or in-future risks so choose it wisely. all database/data store vendors have their appliances which could help or do the task.so read the documents. of course, when you implement the methods manually, you have better control and debugging potential, but it has more chance of facing errors and bugs.Based on my experience, in 24/7 businesses, the incremental (but with a clear deadline)would be better choice

Choosing a data migration method from multiple sources: ETL: For complex transformations and cleansing. Data Replication: Continuous replication for real-time synchronization. Cloud-based Migration: Scalability and flexibility for cloud transitions. Virtualization: Simultaneous access to data from multiple sources. Consider factors like data complexity, real-time requirements, scalability, downtime tolerance, resource availability, data volume, and testing. Collaborate with stakeholders and monitor the process for adaptations as needed.

>Legacy CRM: Export nightly diffs after initial bulk extract to minimize downtime. Use scripted migration tool to transform and load into new CRM. >Marketing platform: Export CSV files and batch load into staging DB for cleansing. Use ETL tool like Informatica for data migration into CRM. >Support system: Export XML files of ticket data and transform into target structure. Migrate into CRM via API integration. >New CRM: Staged loads validated in test environment before final migration. APIs available for integration. >Tracked volumes and timed test runs to split work into manageable batches that can run daily. >Automated validations before and after each transfer to ensure data quality. >Built rollback procedures in case of issues.

La gestión efectiva de proyectos de migración de datos con múltiples fuentes requiere una planificación cuidadosa y la elección de métodos de migración adaptados a la complejidad y los requisitos específicos del proyecto. La flexibilidad y la adaptabilidad son clave para abordar desafíos a medida que surgen durante el proceso de migración.

Essential data migration testing: Data Integrity: Verify accurate transfer. Business Rules: Confirm rule application. Performance: Assess speed and efficiency. Error Handling: Simulate errors for resolution. User Acceptance (UAT): Involve users for validation. Scalability: Test varying data volumes. Security: Verify security measures. Rollback: Test rollback procedures. Compatibility: Check with existing systems. Documentation Review: Ensure comprehensive records. Post-Migration Monitoring: Set up tools for behavior analysis. Thorough testing ensures a successful data migration. #DataMigration #TestingData

Data validation : Need to compare data using multiple use case queries on the sync and retrieve the result in the spreadsheet. Same queries when executed on source should tally the sync output during off business hours. Users and programmable object validation Flagging for Data integrity : Using flag column for SCD types is important to make sure data integrity is in place. Data sensitivity Make sure masking is properly re completed. TDE : Make Sure TDE is in place Sequences, Unique keys are properly migrated. Performance comparison of benchmark query output.

>Set up clone test environments for all source and target systems to validate migration without impacting production data. >Developed test cases and test data to validate expected results across use cases.Checked schema, data accuracy, integrity. >Ran migration on subsets of test data across all systems end-to-end. Measured speed, failure rates. >Fixed bugs, retested until results matched expected. Used bug tracking tool for managing issues. >Completed user acceptance testing with the business before production migration. Confirmed reports match legacy. >Ran final migration in test, checked all use cases. Compared reports before and after. >Signed off on test migration before proceeding to production. The staged testing approach is better.

Las pruebas de migración de datos son una salvaguarda esencial para garantizar el éxito de la transición de sistemas. Un enfoque riguroso, que aborde una variedad de escenarios y condiciones, es clave para minimizar riesgos y garantizar la calidad de los datos migrados.

Using monitoring tools helps in tracking migration status and the progress made. But also, a structured a way of addressing data migration errors and bottlenecks. Addressing right error codes is an key during migration and monitoring tools can helps in tremendous way. Generally migration involves number of stakeholders from different sources like application owners or vendors, data migration experts or even platform team if performance bottlenecks pops up. So monitoring becomes a global aspects withing hetro teams and needs proper sync up to avoid any critical issues or errors while doing migration.

Entender os requisitos da migração, estabelecer metas e realizar o planejamento e traçar estratégias para transferência e transformação dos dados, realizar backup dos dados de origem antes da migração para garantir a recuperação em caso de problemas , realizar testes para garantir que os dados estão sendo migrados corretamente e não ocorrem perdas ou erros, executar a migração em fases para manter riscos controlados e realizar o monitoramento para identificar e corrigir problemas imediatamente, ao fim da migração realizar uma avaliação e manter um suporte efetivo em caso de problemas ou duvidas que possivelmente irão surgir e muito importante realizar o processo de documentação da migração incluindo mapeamentos, testes e etc.