¿Cuáles son las mejores prácticas para extraer datos de tecnologías obsoletas?

1. Convert the data into an intermediary format that is widely supported. For example, if extracting from an old database, try to transform and export data to CSV or XML before converting it to a modern database format. 2. Implement checksums, hash verifications, or record counts to ensure that the data extracted matches the source data. 3. Before extracting data, ensure that you have a complete and verified backup. Data extraction processes can sometimes lead to data loss, so it's crucial to have a fallback

1)Identify if the source can push data or it works on a pull mechanism or both. 2)If its push mechanism, what are the available targets it can push data to. 3)If its pull mechanism ,then need to know the available clients/libraries that can pull data from source. 4)Once we determine the ways to connect, we need to evaluate the impedence factor at the integration point across various sources to assess the performance. 5)Legacy sources can be simulataneously used my multiple clients, so better to check the workload management in order to avoid runtime data cut. 6)Outdated system may not suit well with modern data extraction options, hence its better to pull data in smaller chunks for batch processing.

Employ reverse engineering techniques to understand system intricacies. Utilize custom scripts or legacy system emulators for data extraction. Implement robust error handling to manage potential compatibility issues. Employ data normalization and transformation methods to reconcile disparate data structures. Consider utilizing database migration tools for seamless data transfer. Prioritize security measures such as encryption and access controls during extraction. Leverage version control systems for tracking changes and ensuring data integrity. Collaborate with domain experts and leverage documentation for comprehensive understanding. Test extraction processes rigorously and iterate based on performance feedback.

Many sources have their own API and SDK. Be careful, as outdated sources often stop supporting them. This leads to the accumulation of vulnerabilities. If security is critical for you, you need to provide an additional layer of protection or completely abandon their use, especially if the sources are public.

In addition to understanding the characteristics of data sources described above, it helps to understand if the data sources have implicit support for extraction. E.g. using supported connectors for Spark data pipelines. Most modern data sources have built in support for data extraction as well as data ingestion.

In my opinion, we can extract data from outdated technologies by first understanding the characteristics of, requirements and limitations of the data source. We need to then analyse the data format, size , schema, update/change frequency, security aspects like whether the data is masked or is any security compliance added. Finally we need to set the priorities by understanding what data need to be migrated first and are we planning for any data archival before migrating the source data to destination.

In my view, extracting data from outdated technologies is challenging, as old systems generally do not have modern interfaces and may not have sufficient documentation and this will often require the challenge of adaptations. Given this, the practices that are generally useful for this challenge are: Obtain a deep understanding of the legacy system (architecture, data structure and how information is stored), know and determine which data is critical for your migration or integration project , use ETL tools that support the legacy system, perform data mapping and carry out tests on the extracted data in a controlled environment to identify and resolve any discrepancies or problems before migrating to the new system.

> Before extracting data from outdated technologies, need to understand the characteristics, limitations & requirements of each source. > We need to know the data format, size, schema, frequency, location, security, and dependencies of the source. >Depending on the type and complexity of the data sources, you may need different tools & frameworks to extract data from outdated technologies. For example, you may use ETL tools to connect to legacy systems, SQL to query relational DB, Python or R scripts to parse flat files. > Also may use cloud-based ETL services like AWS Glue or Azure Data Factory, to automate and orchestrate the data extraction process >The key is to select the tools and frameworks that suit business needs, budget & skills

To handle data extraction from outdated technologies effectively: - Evaluate Data Sources: Understand each source's format, size, schema, and security requirements. Assess Business Value: Determine the relevance and quality of the data for prioritization. - Define Extraction Scope: Set clear objectives based on the data source assessment. - Select Suitable Tools: Choose tools and methods compatible with the source's characteristics. This approach ensures efficient extraction while addressing the challenges associated with outdated technologies.

Before you start, verify the business value/issue the data will be used for to ensure the correct data will be recruited. Then leverage any existing tools the client already has accessible. Verify they can be configured appropriately. CLEAN the data prior to integrating it into an existing database or platform. Ensure that protocols are put in place to ensure that this data can be monitored and updated appropriately.

1) Check if anyone has had a similar problem with these data sources before you. Outdated does not mean rare. There may be many ready-made solutions and best practices to solve this problem. You don't need to reinvent the wheel. 2) Follow the KISS principle. For example, if the amount of data is small, it may be better to just manually upload it to an existing system once without automation. Use complicated tools only if it is rational 3) If the task is difficult and important, do research and deeply analyze the available tools and frameworks. A mistake in choosing a tool at this stage can be costly in the future

Try to use a containerized tool like docker to handle legacy or outdated tools: control your package based in the base images and implement new code based it to get new image to develop & deploy. So you just manage these base image and their derivated. Also you can get in your services catalog a copied package to use with outdated storage in the working.

I migrated DBF files (files from information system of the 80s!) to a new system. We started with Pentaho loading the to a DWH in PostgreSQL for more than 9 locations throughout Latin America and to a certain point and after many engineering and automation exercises, we made it work, but it took a long time and we still had errors... that's where I found Python specifically with Pandas (we are talking about the end of 2017 and beginning of 2018) and I entered the world of BigData (because it really was a lot of information) and it saved our lives because we reduced the processing time and use of data by at least 20x. resources by 80% (and at that time I knew very little about computing parallelization or else it would have been even more)

In many cases you may not find a ready-to-use tool for data extraction from legacy apps; however one can use Python or SQL using ETL to extract, transform and load the data to a cloud based BI tool.

Document Everything: Document the process extensively, including the steps taken, tools used, and any issues encountered. Consider Security: Pay attention to security considerations, especially if the data contains sensitive or confidential information. Take appropriate measures to protect the data during extraction and transmission. Test Extracted Data: Test the extracted data thoroughly to ensure that it meets the requirements and expectations of the intended use case. Involve Subject Matter Experts: If possible, involve subject matter experts who are familiar with the outdated technology or the specific data being extracted. Their insights can be invaluable in ensuring the accuracy and completeness of the extracted data.

For legacy systems, tools like Informatica PowerCenter or custom scripts might be necessary to handle specific extraction challenges. For relational databases, SQL-based tools or frameworks like Microsoft SSIS (SQL Server Integration Services) could be ideal. For web scraping, Python libraries like BeautifulSoup or frameworks like Scrapy, or even dedicated tools like Octoparse, may be suitable. For cloud-based integration, services like AWS Glue or Azure Data Factory offer managed ETL services that can simplify the data integration process.

𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐭𝐨𝐨𝐥𝐬 𝐚𝐧𝐝 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬: Selecting the appropriate tools and frameworks is crucial for efficiently extracting data from outdated technologies. Consider using extraction tools, connectors, or APIs specifically designed for the technology in question. If no dedicated tools are available, explore alternative methods such as custom scripts, data migration software, or database replication tools. Additionally, evaluate compatibility with modern data integration platforms or ETL (Extract, Transform, Load) tools to streamline the extraction process and ensure data quality.

When dealing with outdated data technologies, selecting the appropriate tools is vital: ETL Tools: Use Informatica, Talend, or Apache NiFi for legacy systems. They excel at connecting to diverse data sources, transforming data, and loading it into target systems, ideal for structured data and legacy infrastructure. SQL: For relational databases like Oracle or MySQL, SQL remains a powerful choice. It's simple, widely adopted, and efficient for querying and manipulating data. Scripting Languages (Python/R): Python and R are invaluable for parsing flat files and handling unstructured data. Libraries like Pandas in Python and tidyverse in R offer flexibility and customization for diverse file formats and complex transformations.

Si es posible, usa el hardware y el software originales para acceder a los datos. Este método es el más sencillo y menos propenso a provocar corrupción o pérdida de datos. En caso de que no estén disponibles o fallen, considere utilizar técnicas de emulación. Los emuladores imitan la funcionalidad del hardware o software obsoleto, lo que le permite acceder a los datos utilizando sistemas modernos.

A seleção de ferramentas adequadas é crítica, especialmente quando se lida com tecnologias antigas. Pode ser necessário utilizar ferramentas específicas de legado ou desenvolver adaptadores customizados para garantir a comunicação eficaz com os sistemas desatualizados. Além disso, escolher estruturas flexíveis que possam ser ajustadas ou expandidas para lidar com diferentes formatos de dados e peculiaridades das tecnologias antigas é essencial para uma extração bem-sucedida.

Pay attention to data Drift and Concept Drift. Pay attention to the drift of data and concepts. These problems are clearly manifested over a long period of time and are especially critical in artificial intelligence tasks. When working with outdated resources, data is often outdated as well. If you add such data to an existing artificial intelligence system, its accuracy may decrease dramatically.

I follow a step-by-step approach after extracting data from outdated technologies: - Perform Data Quality Checks: Verify data types, formats, values, and ranges to ensure data integrity. - Use Profiling Tools: Employ tools like pandas-profiling for in-depth data analysis and summary reports. - Document Thoroughly: Record the extraction process details, including source information, methods, and outcomes, enhancing traceability and process improvement. This validation and documentation phase is crucial for maintaining data accuracy and facilitating future extractions' efficiency and transparency.

Here is where Data Quality takes center stage enormously and likewise a layer under the Data Contracts Framework finishes closing the complete validation cycle since the union of these two allows us to verify that and how we expect to receive the data, but at the same time the second section offers us how to do said security/quality implementation where obviously there must be a component of time validation and version depreciation in the systems that are sharing said information.

𝐕𝐚𝐥𝐢𝐝𝐚𝐭𝐞 𝐚𝐧𝐝 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚: Data validation is essential to ensure the accuracy, completeness, and consistency of the extracted data. Develop validation procedures to verify the integrity of the data during the extraction process. Perform data profiling, data quality checks, and reconciliation against source systems to identify discrepancies or anomalies. Document the data extraction procedures, including data lineage, transformation rules, and any data cleansing or normalization steps. Maintaining comprehensive documentation helps facilitate troubleshooting, auditing, and future data integration efforts.

Após a extração, os dados devem ser cuidadosamente validados para garantir sua precisão e completude. Isso pode incluir a verificação de consistência, a limpeza de dados e a correção de quaisquer erros identificados. A documentação detalhada do processo de validação, bem como das estruturas de dados extraídos e de quaisquer transformações aplicadas, é vital para garantir que os dados sejam compreensíveis e utilizáveis no futuro.

Data Profiling: Profile the data to understand its quality, completeness, and consistency. For example, when migrating data from an old Lotus Notes database, validate email addresses, phone numbers, and other fields. Data Lineage and Metadata: Document the data lineage—how data flows from source to destination. Tools like Apache Atlas or Collibra can help. Suppose you’re extracting data from legacy spreadsheets; documenting the column meanings and transformations is crucial.

Here is a structured approach to validate the dataset with: - Assess Data Quality - Evaluate the quality and consistency of the data stored in the legacy system - Identify data anomalies / inconsistencies / redundancies (if any) - Start using the data at new destination and keep adding incremental data as needed

After extraction, it's crucial to verify the accuracy and integrity of the data. This step ensures that the data matches its original state and meets the requirements for its intended use. Documenting the data, including its source, structure, and any transformations applied, is essential for future reference and compliance purposes. Some useful tools: For ETL and data documentation, WhereScape is one of the best data automation tools. WhereScape RED is one of the tools for ingesting, transforming, building, deploying, and managing your data solutions. Furthermore, for Data Documentation and Cataloging Alation is one of the best solution.

It's important to establish a clear process for handling and documenting data anomalies or exceptions. This includes setting up a data anomaly reporting system and a process for investigating and resolving discrepancies. This contributes to the overall integrity and reliability of the data, making it more trustworthy for decision-making.

Dazu kann ich noch ergänzen: - Genauigkeit und Vollständigkeit der extrahierten Daten überprüfen. -Dokumentation des Struktur- und Formatierungsprozesses.

Scheduled and well-managed data extraction tasks reduce the risk of data loss or corruption by ensuring that data is extracted accurately and consistently. Also, monitoring the extraction process helps in early detection of errors or inconsistencies, allowing for prompt correction and ensuring high data quality.

1.we should review and refine the data extraction queries and scripts to ensure they are well-written and optimized. 2.We can use query tuning techniques, such as indexing, partitioning, filtering, and caching, to improve the speed and efficiency of the queries. 3.We can also use best practices for writing and debugging scripts, such as modularization, documentation, and testing

𝐎𝐩𝐭𝐢𝐦𝐢𝐬𝐞 𝐚𝐧𝐝 𝐦𝐨𝐧𝐢𝐭𝐨𝐫 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐞𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐩𝐫𝐨𝐜𝐞𝐬𝐬: Optimise the data extraction process to minimise downtime, reduce resource consumption, and maximise efficiency. Implement incremental extraction techniques to extract only the delta changes since the last extraction, reducing processing time and resource utilisation. Schedule extraction jobs during off-peak hours to minimise impact on system performance and user activity. Monitor the extraction process in real-time to detect and address any errors, failures, or performance bottlenecks promptly. Implement logging, alerting, and error handling mechanisms to ensure timely resolution of issues and maintain data availability.

Fine-tune the extraction process for efficiency, reducing the time and resources required. Continuous monitoring is necessary to address any issues promptly and ensure the ongoing reliability of the extraction process.

In most organizations building monitoring and reconciliation systems is still a gap or an afterthought while building data pipelines from outdated technologies. Reasons include focusing on short-term deliverables primarily on development only, neglecting the long-term maintenance and operational aspects of the data pipeline, and assumptions made about the data accuracy or the performance of the data pipeline components. All this can lead to issues going unnoticed until they have a significant impact. A good migration project allocates sufficient resources for building the monitoring and reconciliation systems while also fostering awareness of these among business and operational teams.

To optimize and monitor the data extraction process, as a data engineer, I focus on implementing efficient incremental extraction methods, leveraging parallel processing techniques to enhance speed, and employing ETL tools for streamlined operations.

Make sure to back up all data before attempting to extract it from outdated technology. Document each step of the data extraction process to ensure that it can be replicated if needed. This documentation will also help in troubleshooting any issues that may arise during the extraction. Utilize tools that are compatible with the outdated technology for data extraction.Understand the data structure of the outdated technology and map it to the format required by modern systems. This will help in ensuring that the extracted data is usable and meaningful in the new environment.validate it to ensure its accuracy and completeness. This will help in identifying any issues early on and prevent data loss or corruption.

Otimizar o processo de extração para eficiência pode ser particularmente desafiador com tecnologias desatualizadas devido a limitações de desempenho e compatibilidade. No entanto, ajustar o processo para minimizar o impacto no sistema de origem e garantir a transferência de dados eficiente é crucial. Além disso, o monitoramento contínuo do processo de extração permite identificar e resolver rapidamente quaisquer problemas que possam surgir, garantindo a continuidade e a qualidade da extração de dados.

The most daunting, yet rewarding task in automation! Deploy - monitor - log - fix - adapt - document Airflow DAGs come in handy and wirh dockerization, you can rapidly identify and resolve critical perfoemance issues, without having to repeat processes and handle data duplication issues

Data extraction is an art. How much technology directly involved in its extraction is very subjective. The most important aspect of data extraction is the objective of the extraction and the outcome expected. In the present technology growth especially with AI assited tools as long as we have the access to the right data, we should be able to extract what we want. The capacity in designing the algorithm is the real art in data extraction.

In addition to the mentioned best practices some additional concerns may, • Data Security and Compliance • Legacy System Understanding • Data Transformation and Cleansing • Version Control and Documentation • Testing and Validation • Change Management

- Understand the technology and its limitations. - Before making any changes, ensure you have a backup of the data to avoid loss or corruption. - Find tools or software that are compatible with the outdated technology to extract data. - If automated tools are not available or viable, consider manual extraction methods. This could involve copying and pasting data or using scripts to extract data. - Clearly map out the data structure and format before extraction. This helps ensure that the extracted data remains usable. - Perform test extractions on a small scale to verify the process and check for any errors or data loss. - Document each step of the extraction process for future reference and troubleshooting.

Additionally, comprehensive logging and documentation contribute to transparency and troubleshooting, facilitating continuous improvement in the data extraction workflow.

𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭 𝐞𝐥𝐬𝐞 𝐭𝐨 𝐜𝐨𝐧𝐬𝐢𝐝𝐞𝐫: * 𝐃𝐚𝐭𝐚 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐜𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Ensure compliance with data privacy regulations and security standards when extracting sensitive or confidential data. Implement encryption, access controls, and data masking techniques to protect data privacy and mitigate security risks. * 𝐃𝐚𝐭𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐭𝐞𝐰𝐚𝐫𝐝𝐬𝐡𝐢𝐩: Establish data governance policies and practices to govern the extraction, usage, and management of data from outdated technologies. Assign data stewards or custodians responsible for overseeing the extraction process and ensuring adherence to data governance guidelines. * 𝐃𝐚𝐭𝐚 𝐫𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚𝐫𝐜𝐡𝐢𝐯𝐚𝐥:

Considere também aspectos legais e de segurança. Ao extrair dados de tecnologias desatualizadas, é importante considerar as implicações legais e de segurança, especialmente se os dados incluírem informações pessoais ou sensíveis. Isso pode envolver garantir a conformidade com regulamentos de proteção de dados, LGPD Brasil ou GDPR na Europa, e implementar medidas de segurança robustas para proteger os dados durante e após a extração.

Data Security: Ensure compliance with data privacy regulations. When extracting sensitive customer data from an old custom-built application, follow GDPR or CCPA guidelines. Data Transformation: Sometimes, data needs transformation during extraction. For instance, converting legacy date formats to ISO standards. Archiving and Retention: Decide how long to retain extracted data. Archive historical data from an outdated financial system for compliance purposes.

At some point, you will be faced with a seemingly-inaccessible data source — proprietary software with an encrypted data format and no tech support, for example. If you can still access the data somehow, even if only through a user interface … you can still extract it. On Windows (where this is most likely to occur), you can use a tool like AutoHotKey to script user interface operations (such as menu selection, keyboard input, and the like). There’s a good chance you can export an individual screen’s data to a file. Worst case, you can take a screenshot and use OCR on it. The whole thing can be automated and left to run at its own pace. It is truly miserable and likely very expensive … but, if it’s worth it, it can still be done.

Don't underestimate the power of "boring" tools. We always go into this hoping for a REST API, or a direct DB connection. But most of the time it's (S)FTP or an automated email that will save the day!

Es könnte auch erforderlich sein, mit Spezialisten für ältere Systeme oder veraltete Systemlandschaften zusammenzuarbeiten. Die extrahierten Daten lassen sich vielfältig nutzen, etwa für die Aktualisierung überholter Systeme, für datengestützte Analysen oder zur Erstellung von Berichten im Rahmen gesetzlicher Vorschriften.