How do you validate a data warehouse?

Honestly, not every company needs a data warehouse. If you are a small startup *unless your product is data-related* - you can survive without a data warehouse until a certain point. don't adopt new technologies just because of the hype. start with your business needs first. what is an absolute must for the business today?

Data with poor quality & without validation is as good as noise. Most important part of DWH validation is to identify & setup rules at the first layer - validating the source data before processing into DWH. Validate and fix data issues before loading it. Data validation is done at multiple levels - before processing source data, at the intermediate level(stage layer), Transformed level (Dimension and Fact layer). Validation should be done for - NULL, Unique, date format, number format, consistency across different sources for same data metric, consistency compared to previous data feed & finally the business rules along with threshold limit. ETL process should generate Quality reports and fix data issues based on learning over the months.

An important thing to consider is making sure whatever data is ingested into the data warehouse is quality from the start. Enforcing guidelines such as enforcing data type adherence and not allowing null values to be processed on ingestion is key. It shifts responsibility to the producer by making sure they cannot just dump bad data into your DW.

Validating a data warehouse is a critical step to ensure that the data it contains is accurate, complete, and consistent. Here are some common steps and techniques for validating a data warehouse. Data warehouse validation involves: 1. Data profiling to understand data. 2. Data quality checks for accuracy. 3. Schema validation for correctness. 4. ETL process verification. 5. Data reconciliation for consistency. 6. Error handling and logging. 7. Regression testing for quality. 8. Data sampling for ad-hoc checks. 9. Data auditing and tracking. 10. Performance testing. 11. Security and access control. 12. Documentation and metadata. 13. User acceptance testing. 14. Automated testing for continuous monitoring.

I would start with validating it against the business questions it is trying to to answer and can you answer that from the data available in the data warehouse. If not what why? Is something not clear, not detailed enough or simply missing. If you can do you trust the answer to the question? If not why? Lineage, data quality, doesn’t fall within expected business ranges.

Datawarehouse profiling is very important task. At minimum it must include, Structure discovery 1. Performing data quality assessment, risk of performing joins on the data. 2. Discovering metadata and assessing its accuracy. 3. Distribution identification Content discovery 1. Collecting descriptive statistics like min, max, count and sum. 2. Collecting data types, length, and recurring patterns. 3. Tagging data with keywords, descriptions, or categories. Relationship discovery 1. Key candidates 2. Foreign-key candidates 3. Functional dependencies 4. Embedded value dependencies 5. Inter-table analysis.

i believe data profiling is the key to ensure data quality and keeping anomalies in check, as wells as compliance like HIPAA and GDPR. the challenge is however, how to do you keep it updated and alive.

To validate a data warehouse, follow these key steps: 1) Define requirements, 2) Profile source data, 3) Validate ETL processes, 4) Check schema alignment, 5) Implement data quality checks, 6) Enforce referential integrity, 7) Verify business rules, 8) Ensure data consistency, 9) Develop test cases, 10) Reconcile data with source systems, 11) Test performance, 12) Secure data and access, 13) Enable logging and auditing, 14) Maintain documentation, 15) Conduct user acceptance testing, 16) Implement ongoing monitoring, and 17) Continuously improve validation processes.

Significant challenges in data profiling include: - Dealing with extensive real-world data sources containing structured, semi-structured, and unstructured data is resource-intensive. - As data grows, profiling becomes time-consuming, requiring scalable solutions. Identifying missing values, duplicates, outliers, and inconsistencies is just the start. Devising strategies for data cleaning, transformation, and integration is complex and requires collaboration with data stewards and domain experts. - Data profiling must ensure sensitive data remains secure, complying with regulations like GDPR and HIPAA. This includes appropriate anonymization or pseudonymization, especially in multinational or multi-industry contexts.

Utilize data profiling, as a means to acquire an understanding. Evaluate the level of completeness, accuracy, and consistency within and across sources. Identify any gaps, anomalies, or outliers that may arise. Automate the process of profiling during data ingestion to facilitate feedback on quality. Visualize the connections between sources for an overview. Collaborate with experts, in the domain to interpret and resolve any discovered insights. Consistently conducting profiling activities contributes significantly to the establishment of data confidence.

Having a separation of concern helps here. Dev, staging and prod environments are good to have for any data warehouse pipeline. Test scenarios must be documented and run regularly even without code changes being introduced. Treat incoming data as a change which mandates testing at each row of entities. Adopt integrity by design model when building your data warehouse. Have it part of the pipelines.

Data testing faces challenges due to disparities between QA and Production environments, leading to incoherent data sets. These differences, encompassing data volume, structure, and quality, affect testing accuracy. Maintaining legacy systems within QA is resource-intensive and poses management challenges. Additionally, implementing rollback procedures, while necessary for issue resolution, can disrupt testing. Effectively addressing these challenges requires a customized strategy that balances data coherence, legacy system support, and efficient rollback procedures, ensuring data reliability and integrity in the testing process.

Testing testing testing is a nightmare for many. in many testing (UT or SIT) there tend to be biases. 3rd party independant testing agency should be deploy on accuracy, functionalities and reliability. also to keep the developer in check.

Data monitoring is very key in ETL processes. This ensures that several KPIs like, data ingest rate, ingest times, query response times, data volume, error rates, and so on are always observed. All of these KPIs can then later help in the optimizition of things like extraction queries, to ensure that queries that are run against external databases are optimized to respond in timely manner without causing so much load on the databases being extracted from. They can also help in detecting deviations in data consistency as soon as possible so as to avoid data corruption,...

Data Monitoring plays a vital role to find out the actual width and breadth of the data warehouse in terms of performance, availability, usability and once the data is in ready to flow state , we can find many perspectives which we haven't encountered in development area and user acceptance environments. this helps us to improve the visibility of the product.

Data monitoring is crucial in three key real-life scenarios: - E-commerce Operations: Online retailers rely on data monitoring to track customer behavior and swiftly detect sales anomalies. This allows them to adapt to changing customer trends and optimize the customer experience. - Financial Institutions: Banks use data monitoring to spot fraudulent transactions in real-time, ensuring the security of clients' assets. - Healthcare: Data monitoring is vital in healthcare to maintain patient records' integrity and regulatory compliance, enhancing patient care and system efficiency. In these scenarios, data monitoring is essential for quick issue detection, security, and maintaining data accuracy.

Data monitoring is the most important step to ensure business or end system and clients is getting the data 24*7 without any issues. In case of issues arise need to already categorize the impact and inform stakeholders. Once the issue is resolved there should be RCA performed to remediate the issue happening in future

Data Quality Monitoring can play a vital role here. It requires creating a baseline data and then comparing it with every fresh load. There are several parameters that can be analysed like number of NULL records, data type abnormalities, mean, median standard deviations in the values etc. and defining a threshold to get the expected violation report. AWS sagemaker etc. can be used for the same or core python frameworks can be developed for the same. Also, it is important to identify critical attributes so that attributes impacting business are properly monitored.

Some examples of three standout products for data validation are: - Informatica Data Quality: This tool excels in data profiling and quality assessment, making it a reliable choice for understanding data anomalies and ensuring accuracy. - QuerySurge: Specializing in data testing, QuerySurge is a top pick for organizations focused on data warehouse validation, offering a comprehensive suite of testing features. - Splunk: Renowned for data monitoring and real-time analysis, Splunk plays a pivotal role in proactively identifying data discrepancies, ensuring data quality, and maintaining the reliability of data warehouses. These tools collectively contribute to streamlined data validation and improved data quality.

Data validation tools can provide the information related to data completeness and ensuring the data flowing through pipelines adhere to business rules setup

I agree, best practice data warehouse validation involves confirming design meets requirements, implementing frameworks for verifying accurate source data, ensuring correct ETL processes, and checking data quality for accuracy and consistency. It also includes assessing performance to handle queries, verifying security measures, conducting user acceptance testing, and testing backup/recovery systems. Use tools like Informatica for ETL, JMeter for performance, and Tableau for BI report validation. Validating a data warehouse is a continuous process. Regular checks maintain data integrity.

Certain things to be followed as best practices Implement the re-usable code framework to reduce the development liefe cycle and make it a easier for maintenance. The code should be scalable to onboard similar system

Three essential data warehouse best practices with real-life tool examples are: - Utilize QuerySurge to validate ETL processes, data models, and queries with diverse tests and scenarios, ensuring data accuracy and performance. Prevents issues like inconsistent data. - Implement Informatica DQ to understand data patterns, identify anomalies, and assess quality. This practice detects issues early, such as inaccuracies or missing data. - Use Splunk to continuously monitor data flows and provide real-time alerts. This proactive approach maintains data quality and ensures reliability, preventing problems like delayed data delivery. These practices, combined with the respective tools, enhance data warehouse validation and data quality.

Follow established validation frameworks and methodologies to ensure the reliability of your work. It's crucial to validate frequently during the development process. Make sure to involve both IT professionals and business users in the testing phase. Whenever feasible automate tests using unit, integration, and regression testing. Document your objectives, criteria, test cases, and outcomes accurately. Keep track of any issues that arise and conduct a root cause analysis for each one. Continuously monitor the validation process to ensure its success. Remember that achieving validation requires well-organized and collaborative execution.

DWH testing often misinterpreted or interchangeably used with ETL testing...although they are interconnected, they serve different purposes and focus on distinct aspects of the data management process. Data warehouse testing and validation encompasses data quality checks, data validation, reconciliation. Most importantly, to ensure that the data stored (data at rest) in the data warehouse is accurate, consistent, and best available for reporting and analysis.

Establish and maintain business stakeholder confidence in the data warehouse through recurring reconciliations with known, trusted artifacts/reports of aggregates from the source systems where possible. Not only does this help guarantee, accuracy and completeness, it also ensures consistency in the business definitions associated with the data.

Data warehouse validation involves verifying data accuracy, completeness, and consistency. The key steps should include source validation, data ingestion and transformation checks, data quality assessments, schema validation, and reconciliation. Regular monitoring and user feedback ensure data remains reliable, aligning with business requirements.

In my experience, validating a data warehouse involves ensuring it meets business needs through requirement validation, verifying source data accuracy, checking ETL processes for correct transformations and error handling, and conducting data quality checks for accuracy, consistency, completeness, and uniqueness. Additionally, performance must be assessed to handle queries efficiently, security and access controls must be confirmed, and user acceptance testing conducted. Lastly, backup and recovery systems must be tested for effective data restoration.