How can you effectively convert data between incompatible systems?

- Understand Source and Target Systems: Begin by thoroughly understanding the data structures, formats, and schemas used in both the source and target systems. This includes identifying data types, relationships, constraints, and any specific formatting requirements. - Define Data Mapping: Determine how each element in the source system maps to the target system. This may involve transforming data formats, values, or structures to ensure compatibility. Documenting this mapping is crucial for both the conversion process and future references. - Assess Data Quality: Evaluate the quality of the source data to identify any issues with accuracy, completeness, or consistency. This step may reveal the need for data cleansing before conversion.

Try to identify Risks and Impacts: Assess potential risks and challenges associated with data conversion, such as data loss, corruption, or inconsistency. Identify mitigation strategies to address these risks, such as data validation checks, error handling mechanisms, or rollback procedures. Consider the impact of data conversion on downstream systems, applications, and users, and plan accordingly to minimize disruptions.

To effectively convert data between incompatible systems: 1. Identify the data formats and structures of both systems. 2. Analyze the data mapping requirements. 3. Develop a data conversion plan with clear steps and timelines. 4. Implement data conversion tools or scripts. 5. Test the conversion process thoroughly. 6. Monitor the conversion process for errors and issues. 7. Document the conversion process for future reference. 8. Communicate the conversion results to stakeholders. 9. Evaluate the conversion process for efficiency and effectiveness. 10. Regularly update the conversion process for ongoing compatibility.

- Understand the data requirements of both the source and target systems. - Identify data types, formats, schemas, validation rules, encoding, and security standards. - Determine the scope, frequency, and volume of data conversion. - Assess potential risks and impacts of data loss, corruption, or inconsistency. - Clearly define data requirements to efficiently and accurately plan and design the data conversion process.

In my experience, it is important to adopt a technique to identify the data requirements, some that can be used are: -Data modeling: creating a graphical representation of the data structure and organization, using tools such as entity-relationship diagrams, class diagrams, or data flow diagrams. -Data dictionary: creating a document that describes the data elements, attributes, and relationships in detail, using tables, lists, or definitions. -Data analysis: exploring, and interpreting the data, using tools such as statistics, queries, or visualizations.

In my experience, conversion is generally a sub-optimal solution. Rather API-driven micro data services is the way to go. And then over the long-haul, analyze usage, purpose and redundancy between the two data sources, and only then, integrate them into one.

Consider the frequency and recurrence of the data conversion process. For one-time or infrequent conversions, manual methods may be sufficient. For recurring conversions, semi-automated methods offer efficiency gains by leveraging automation tools and scripts. For continuous or real-time conversions, fully automated methods are necessary to handle the ongoing data flow without human intervention.

Based on the data requirements and the systems involved, select the most suitable method for data conversion. This could involve using Extract, Transform, Load (ETL) tools, writing custom scripts, or employing middleware solutions. The method should account for the data volume, conversion frequency, and the need for real-time or batch processing.

- Convers?o manual de dados - Convers?o semi-automatizada de dados - Convers?o totalmente automatizada de dados - Ferramentas de software ou scripts para ETL - Adequa??o para diferentes tipos de convers?o de dados - Interven??o humana versus automa??o

Before the conversion process there needs to be some design thinking as to how the disparate data will 'sit and work' together. Is there a value of 'convert' without minimal viable design to conform to each other? Off course 'converting' should be only be done at a virtualized level to expose siloed data and not actually move/ convert at a physical level

Data Quality checks are critical to any conversion/ ETL process to ensure integrity of data and validity of insight. Tools such as dbt and great expectation simplify the data quality and integrity process through predefined and custom test executed at runtime.

Data quality is paramount during conversion. Implement checks and controls to ensure that the data is accurate, complete, and consistent before, during, and after the conversion. This may include validating data formats, checking for duplicates, ensuring data completeness, and maintaining data integrity.

- Perfil de dados para identificar características e problemas - Limpeza de dados para corrigir erros e duplicatas - Mapeamento de dados para transforma??o adequada - Valida??o de dados para garantir conformidade com especifica??es - Reconcilia??o de dados para verificar integridade e consistência

- Estabelecer casos de teste abrangentes - Utilizar dados de teste representativos - Monitorar resultados, erros e logs - Analisar os resultados do teste - Identificar e corrigir problemas ou lacunas - Repetir o ciclo de teste e depura??o

Before fully implementing the conversion, conduct thorough testing to identify any issues or errors in the process. This should include unit testing, system testing, and user acceptance testing to ensure the conversion meets the required specifications and is error-free. Debug any issues identified during testing to refine the conversion process.

Use a confluence page or wiki kind of documentation tool to be used for keep track of all the details including data dictionary.

Thinking of drastically reducing documentation time? Integration of dbt allows for easy and fast documentation generation through short and simple commands. The documentation will comprise data sources, destinations, field names and datatypes, dependencies, tests, and more.

Maintain comprehensive documentation of the data conversion process, including the methods used, data mappings, quality checks, and any issues encountered and resolved. This documentation is crucial for future reference, audits, and compliance. It also helps in reviewing the process for efficiency and effectiveness, allowing for continuous improvement.

Take in mind the legacy software requirements: sometimes you need to work with legacy software to define and store or extract data to solve the data conversion among systems incompatible. Part of the compabible problem come from your data teams hasn't required dependencies to implement the definition with one of systems.

6. Estabelecer uma estratégia de monitoramento contínuo é essencial implementar uma estratégia de monitoramento contínuo para garantir a seguran?a dos dados em um ambiente de microsservi?os. Isso envolve a implementa??o de ferramentas de monitoramento em tempo real que possam detectar atividades suspeitas, anomalias de tráfego ou viola??es de seguran?a. Além disso, é importante estabelecer alertas e notifica??es para que a equipe de opera??es possa responder rapidamente a quaisquer incidentes de seguran?a. Uma abordagem proativa de monitoramento contínuo ajuda a identificar e mitigar amea?as em tempo hábil, garantindo a integridade e a seguran?a dos dados em todo o ambiente de microsservi?os.