Your team values speed in ETL processes. How do you ensure data quality doesn't take a backseat?

- 🤖 Implement continuous automated testing to ensure data quality in ETL processes. - 🚦 Set up checkpoints throughout the data pipeline to catch issues early and often. - 📊 Use a range of tests from simple schema validations to complex data quality checks, such as verifying data consistency and completeness. - 🛡️ Automated tests act as guardrails, preventing data quality from veering off course as speed increases. - 🔄 Automating these tests ensures they can run consistently and efficiently, maintaining high data quality without compromising on speed.

There are usually 3 kind of tests for data engineering: - unit testing for each task - end-to-end testing with testing environment databases - type / schema / format validation testing, which is usually overlooked. If using python, something like Pydantic can be really useful. Think about CI / CD to make those tests as useful as possible. Also logs, monitoring and alerts relative to data size and quality for production environment are crucial to test continuously the data quality.

Implement the well written down test cases using automated testing tools. Ensure all test cases are covered. Log the errors so that any missed test cases can be covered upfront.

1. Unit testing should be done for each task. 2. When code is merged, end to end testing needs to be done to validate the system or pipeline is working fine. 3. Data, schema, Nulls and other validations needs to be completed. 4. Finally QA testing can be done, After which code is ready for production release. CI/CD is really important and we can setup checkpoints and test cases in them to validate everything is going well.

Just a brief comment, the speed, even if slow, does not guarantee that the product is error-free. It is the periodic control during the development of the project, the assessment of its deliverables (dones) and methodical monitoring of critical processes as well.

Durante a construção do processo o engenheiro de dados deverá garantir a qualidade do processo de etl. A monitorização em produção do processo permite o controlo da chegada do dado ao usuário. A qualidade do dado que chega ao usuario poderá ser feita por monitorização de indicadores de qualidade em tempo real com alertas aos responsáveis pelo processo no caso de existirem desvios

Real Time Monitoring required to confirm the data accuracy. Data engineer should validate the data. Without validation business user will suffer to get the actual data.

Real-time monitoring indeed plays a critical role in maintaining data quality within ETL processes. It's akin to having a vigilant sentry on watch, ensuring your data pipelines run smoothly without compromising on data integrity. Leveraging tools that offer robust dashboards and alert systems is essential for tracking data flows and system health. These tools enable you to detect and resolve issues promptly, maintaining a fine balance between operational speed and data quality. By continuously monitoring your data pipelines, you can preemptively address anomalies before they escalate into significant problems, ensuring a seamless and reliable data processing environment.

Real Time Monitoring involves continuously tracking and analyzing data as it's extracted, transformed, and loaded. This ensures immediate detection of issues, system performance, and data quality, enabling timely interventions and optimizations. It helps maintain the pipeline's efficiency, reliability, and accuracy, facilitating proactive decision-making and seamless data processing.

En mi experiencia, trabajando con productos de datos que entregan información RT/NRT es crucial, ya que le agrega mucho valor identificar y rectificar procesos a tiempo, y que los clientes no sufran las perdidas de datos generando confianza en los productos

Use incremental data loading to minimize processing time and reduce the risk of data errors. This ensures that only new or updated data is processed which reduces the data volume and thus increases speed also incremental load can even be done in such a way that you can keep your modified records as a backup for your future reference if required.

With more adoption of Data Lakes on clouds data storage, CDC is one of the best option to keep all the changes in raw layer and built a incremental deduped transform layer on top of raw layer, by applying business relevant logic.

Incremental processing with checkpoints, Change Data Capture (CDC), and Slowly Changing Dimension (SCD) tables is a great way to make ETL processes faster. Medallion architecture is one method to apply these techniques without sacrificing data quality, by maturing the data quality constraints in layers from raw to "gold." Also, just because a data pipeline is derived from CDC events doesn't mean you should impose the same CDC metadata and parsing difficulties on your data consumer.

I recommend incremental loading, a method that focuses on updating only the changed data since the last run. This translates to quicker ETL cycles and less strain on your systems. Speed shouldn't come at the cost of data accuracy. To ensure no updates fall through the cracks, implement Change Data Capture (CDC). This robust mechanism tracks data modifications meticulously, guaranteeing the integrity of your data even as your ETL processes accelerate. Think of CDC as a safety net for your data quality. It captures every change, from inserts and updates to deletes, ensuring your data warehouse always reflects the latest and most accurate information.

Incremental loading is a strategic approach that focuses on updating only the data that has changed since the last ETL process. This method significantly reduces the volume of data being processed at any given time, leading to quicker ETL cycles and more efficient use of system resources. However, to prevent data corruption, it's crucial to accurately track these changes. Implementing a robust Change Data Capture (CDC) mechanism ensures that no updates are missed, preserving the integrity of your data while enhancing ETL speed. With CDC in place, you can confidently manage incremental loading, ensuring both efficiency and data reliability.

Profiling indeed helps reduce any errorneous data, in terms of type, name or value, proactively rather than keeping the ETL process at a risk to fail. Transformations and modelling the data act as the safeguards and help data profiling before initiating the pipeline.

Data profiling helps in data quality by identifying issues such as missing values, duplicates, and inconsistencies. It provides insights into the data’s structure, content, and relationships, ensuring accuracy and reliability. I built a data quality rules recommender using profiling as the base, innovatively suggesting specific checks and corrections tailored to each dataset. This system not only enhances data integrity but also automates the identification and resolution of potential data issues.

Also I would suggest that to check possible insights. The quality the data the earlier spotting of the insights. With good data you have good information then the insights

Think of data profiling as recon for your ETL warriors. Before data enters the pipeline, profiling analyzes its structure, patterns, and inconsistencies. This intel allows you to tailor your ETL processes for optimal efficiency. By understanding your data upfront, you can proactively address potential issues. Say goodbye to mid-pipeline surprises! Profiling lets you tailor transformations and validations to the specific nature of your data, preventing quality bottlenecks. Imagine fine-tuning your car for the terrain. Profiling helps you avoid roadblocks by identifying missing values, outliers, or unexpected data formats. You can then adjust your ETL processes to handle these situations smoothly.

Data profiling is essential for preparing your ETL processes by examining data for patterns, anomalies, or inconsistencies before it enters the pipeline. By understanding your data's characteristics upfront, you can tailor your ETL processes for better handling. Profiling enables preemptive adjustments in transformations and validations to address potential quality issues. Tools like Talend and Informatica offer robust data profiling features, allowing you to optimize your ETL workflows and ensure high-quality data from the outset.

As a data engineer, I’ve seen firsthand how a collaborative approach enhances data quality. For example, when data engineers and analysts work together, engineers can better understand the data's business context, leading to more accurate validation rules. Business users can provide real-world scenarios that highlight critical data quality aspects. This teamwork allows us to identify potential issues early and refine our checks. For instance, while engineers might focus on schema validation, analysts might emphasize data accuracy, ensuring comprehensive coverage and alignment with business needs.

1. Hold regular meetings and code reviews to discuss data quality issues and solutions. This promotes knowledge sharing and ensures alignment on quality goals. 2. Maintain comprehensive and accessible documentation on data sources, transformations, quality rules, and ETL processes. This helps teams stay informed and aligned.

One thing I have found very useful is to keep in continuous touch with Data Analyst/Scientist team to have a understanding of business Also, this might help in identifying the data related issues at earliest

A collaborative approach leverages the collective expertise of your team to maintain data quality in fast-paced ETL operations. By encouraging data engineers, analysts, and business users to work together and share insights about the data and its use cases, you can develop more refined and context-aware data quality checks. This collaboration ensures that quality checks are comprehensive and aligned with business needs. For instance, regular cross-functional meetings and shared documentation platforms can facilitate this teamwork, leading to more meaningful and effective data validation processes.

Collaboration and Documentation • Cross-functional Teams: Foster collaboration between data engineers, analysts, and business stakeholders to ensure data quality requirements are well-understood and incorporated. • Documentation: Maintain thorough documentation of ETL processes, data quality rules, and validation procedures to ensure consistency and clarity.

Imagine your ETL process as a well-oiled machine. Continuous improvement is like regularly inspecting and fine-tuning that machine. You'll: Review & Update: Regularly assess your test suites, monitoring tools, and validation rules. Are they still catching errors effectively in today's data landscape? Adapt & Evolve: As data environments change, so should your approach. Embrace new technologies and adapt your strategies to tackle emerging challenges and opportunities. Quality at the Forefront: Don't let speed become the sole focus. Continuous improvement ensures data quality remains a top priority, even as you strive for efficiency. This dynamic process ensures both speed and quality flourish in your ETL efforts.

As a data engineer, I prioritize continuous improvement to keep our ETL processes and data quality checks current. For example, i have seen a company regularly update their test suites to accommodate new data sources and formats. Recently, they integrated Apache NiFi to streamline data flow management and added new validation rules to handle JSON data more effectively. They have also upgraded their monitoring tools, like Apache Airflow, to enhance performance tracking. This ongoing refinement ensures that as they speed up ETL operations, which they don't compromise on data quality. Embracing this dynamic approach allows them to adapt to new challenges and maintain high standards in their data workflows which is a game changer for everyone.

As Rome was not built in a day, similarly it takes some iterations to built a robust pipeline. Its very much important to keep an eye on following few points for improvements 1) Performance improvements either through query optimisation or reducing number of hops around data 2) Watermarking correct information( size of data, number of rows, last failure date, reason of failure etc) , to understand the improvement areas

Continuous improvement is vital for refining ETL processes and data quality checks. As data environments evolve, strategies must be regularly reviewed and updated, including test suites, monitoring tools, and validation rules. Embracing a culture of continuous improvement ensures that speed does not outpace data quality. This dynamic approach adapts to new challenges and opportunities, maintaining a balance between efficiency and accuracy in your ETL efforts. For example, conducting periodic audits and incorporating feedback loops can help identify areas for enhancement, ensuring both speed and quality remain priorities.

As data environments evolve, so should your strategies for ensuring data quality. Regularly review and update test suites, monitoring tools, and validation rules. For instance, if you notice an increase in data volume, consider scaling your infrastructure to handle the load without sacrificing performance. Implementing automated data profiling can help identify anomalies early, and regularly updating your validation rules can ensure they remain relevant as new data sources are added. By fostering a culture of continuous improvement, you can balance speed with data quality, adapting to new challenges and opportunities and keeping both priorities at the forefront of your ETL efforts.

Sempre que estou criando um processo de ETL tento implementar o conceito de partições, dessa forma consigo monitorar de forma assertiva e bem visual todas as alterações que estou realizando nas entidades afetadas pelo processo. Para isso eu adoro utilizar o orquestrador Dagster, ele lida de forma muito fácil e prática com o conceito de Jobs (processos) particionados.

Error Handling mechanisms: To capture and manage data quality issues effectively and ensure they do not disrupt the ETL process and they are addressed promptly we need to implement error handling mechanisms. Recording the details of errors by implementing logging frameworks that captures error messages, timestamp, data records causing errors and tc using logging libraries (eg logging module in Python) can help us in identifying errors as soon as they occur. Example log errors can be in a central repository, for transient errors implement retrying and alerting mechanism for critical issues are some ways of handling errors promptly and effectively.

The first thing to consider for any data pipeline is the purpose, then writing validation and test cases for each step. Also have utility or adhoc jobs which can fix known issues, for example, in the cause of incremental transaction data if there was late arriving data, then reprocessing based on transaction date and not ingestion/load date to fix sequence of transaction data. Finally prepare weekly and monthly data quality reports which can identify problems in source, modules or logic so that we can rewrite portions of the code to improve quality.

Es importante sensibilizar el equipo de trabajo enfocándose en que la velocidad depende de la calidad de los datos. Cuando la calidad de los datos es mayor el proceso de ETL es mucho más rápido y el resultado final tiene mayor calidad, de hecho, es preferible tener más calidad que cantidad teniendo en cuenta limites razonables.

ETL Best Practices • Modular Design: Design ETL processes in modular components that can be independently tested and optimized. • Logging and Auditing: Implement comprehensive logging and auditing to track data lineage and transformations, making it easier to trace and fix data quality issues. • Version Control: Use version control for ETL scripts and configurations to manage changes systematically and ensure reproducibility.