What tools can you use to validate data completeness and correctness after migration?

As the article is talking about tools, technologies like SODA, GreatExpectations, DataBrew, etc... can helps implementing the points listed in this chapter, especialy in the context of modern data stacks. In addition it's very important to have observability and monitoring tools to achive that goal especialy to profil and understand what happen during the migration phase.

1. Data Profiling Tools: Talend, Informatica, or IBM InfoSphere analyze data for issues. 2. SQL Queries: Compare data between source and destination databases. 3. Data Quality Monitoring Tools: Apache Griffin or Talend monitor data quality. 4. Checksums and Hashing: Check for changes using MD5 or SHA hashes. 5. Custom Scripts: Create tailored validation checks. 6. Data Comparison Tools: Beyond Compare or Redgate SQL Data Compare highlight differences. 7. Manual Inspection: Review sample data for discrepancies. 8. Error Logging: Capture migration errors for analysis. 9. Regression Testing: Ensure applications work with new data. 10. Validation Frameworks: Use predefined rules for automated checks.

I’m my experience there are two different types of data quality frameworks that are used . One for business rules and the other for technical rules. . Tools such as great expectation and dbt do a good job is building tests around technical , but not business rules. business rules are actually built into the custom rules defined in data catalogues or worse elt pipelines . Observability tooling helps , such as Monte Carlo and custom rules engine like drools .

1. Data Profiling Tools: Talend, Informatica analyze data for issues. 2. SQL Queries: Compare data between source and destination databases. 3. Custom Script: Validation is essential for ensuring the integrity and trustworthiness of data throughout its lifecycle. As new data sources emerge, it is crucial to apply business-specific data validation rules and conditions to ensure that it is in the desired format. Carefully analyzing the factors discussed above will help you choose the right data validation that meets your data requirements.

Many articles have been written and generated about this. However, at its core - a data quality framework is a set of tools and methodologies that an organization uses to achieve its data goals. In a 5 min nutshell: 1. Start with the requirements, and the end outcome in mind. 2. Know critical (sensitive inc), and important data elements, and define/align key metrics with key stakeholders. 3. Agree on roles and responsibilities for data. Who are the data stewards? 4. Assess data against metrics. 5. Improve data with right tools, at right moment. 6. Celebrate key wins and milestones. 7. Monitor the execution, and rinse and repeat process 4-6 until results meet data outcome defined.

Data Profiling can identify noncompliant data (data type, length, domain, value, etc.), recommend key structures (primary & foreign), recognize data entry patterns to determine requirements for additional edits, identify data errors and anomalies based on business rules. Results can be used to suggest business rules that must be validated by stewards. Discover the actual range of values for a specific field. Data steward must validate the range and add values that are possible, but do not occur in the source. Results can also suggest anomalies in the data requiring steward review. Identify a range of values which cover a high percent of the total occurrences. Values with a very small occurrence (< .1%) are suspect of being invalid.

- Data Validation jobs to confirm that the attended results is satisfied they can be technical like the number of lines, the number of specific values...or functional like the total amount of stock of products, etc... - Such validation can be done using either profilings solutions like colibra, DataBrew, or using scripting technologies like greatexceptations, soda, etc..

Some useful tools for validating data completeness and correctness post-migration include SQL queries to check for null values and invalid formats. Data validation scripts to verify values against business rules. Comparing row counts and unique key counts pre- and post-migration also helps validate full data migration. Testing a statistically significant sample of records manually as an extra validation check has worked well on projects I've been involved in. Approaching validation from multiple angles using both automated and manual techniques helps ensure high data quality after any data migration effort.

Microsoft Purview is the solution for unified data governance to help manage and govern your on-premises, multi-cloud, and software as a service (SaaS) data. Data profiling feature in Purview helps data source discovery and collect statistics for the registered data sources, these statistics can help you determine the suitability of the data to solve business problem. It helps answer tough questions like below. Can it be used to solve my business problem? Does the data conform to particular standards or patterns? What are some of the anomalies of the data source? What are possible challenges of integrating this data into my application? Hope this helps! Thank you BR Sheik

Data profiling is a critical step in the data preparation and data quality assurance process.If you want to reduce your SAAS footprint in your org ,you can chose to create your own basic profiler using many built in functions in pandas or use inbuilt profilers of open source projects like great expectations,Amazon Deequ.

Data validation is a crucial step both before and after the migration process. It involves ensuring that key aspects of data quality, such as integrity and completeness, are maintained as data is transferred to the target database. For instance, if the source system transmits 1000 records but only 900 are received at the destination, it's imperative that the ETL process is designed to either fail or delete the discrepant records before they become accessible to end users. This proactive approach to data validation is essential. Thus, incorporating robust quality validation tests within your ETL process is vital. These tests should be capable of triggering failures and alerts in case of any data integrity issues during insertion.

good value additions here, in my experience I have found good data validation framework as per underlying technology (schemaspy, few apache native frameworks,etc.) may be useful but custom validations segregated at each step of ETL based on source limitations and target expectation is necessary and if these are automated in pipeline will benefit more. Obviously, technology being used will need to be considered before deploying such framework.

Ferramentas como o Great Expectations e o Deequ, s?o exemplos de ótimas ferramentas para valida??o de dados como volumetria, campos nulos, range de valores, duplicidade de chaves, etc.

Data validation serves as the safeguarding backbone during critical phases like data migration within the banking sector. Beyond ensuring adherence to predefined data quality frameworks, it serves as a gatekeeper against potential risks like financial errors or regulatory non-compliance. Count checks validate the integrity of transferred data, verifying accurate migration, while structure checks ensure the compatibility and consistency of the data schema between systems

Data validation is the process of verifying and validating data that is collected before it is used. Any type of data handling task, whether it is gathering data, analyzing it, or structuring it for presentation, must include data validation to ensure accurate results. The data validation process has gained significant importance within organizations involved with data and its collection, processing, and analysis. It is considered to be the foundation for efficient data management since it facilitates analytics based on meaningful and valid datasets.

Data reconciliation and correctness are pivotal for reliable data analysis. Utilizing sophisticated tools and techniques ensures the integrity of data throughout its lifecycle. Validation rules are crucial for accuracy. Statistical analysis using R or Python aids in identifying outliers, contributing to a more robust dataset. Visualization plays a key role; create data quality dashboards with tools like Tableau or Power BI to monitor and communicate data correctness metrics effectively. Comprehensive documentation acts as a knowledge repository, aiding in understanding data sources, transformations, and business rules. Collaboration with stakeholders provides domain-specific insights, enriching the analysis.

Useful data reconciliation tools after a migration include Excel or Google Sheets for manually reviewing and editing small data sets to fix errors and inconsistencies. For larger datasets, open source options like Talend also help merge duplicate records and fill gaps in incomplete data. Establishing the single source of truth and then updating downstream systems with the cleaned, reconciled data is crucial. I’ve found that both software and manual examination are needed to truly reconcile data and achieve consistency after a migration, especially when multiple legacy systems are consolidated into one target database or application.

Data reconciliation ensures data accuracy and consistency, and it comes in three main flavors: 1. Manual Reconciliation: Hands-on comparison of datasets using spreadsheets or data preparation tools. Typically performed by data stewards or business stakeholders with deep knowledge of the data. 2. Automated Reconciliation: Leverages software tools to streamline comparison, error detection, and correction. Employs data quality techniques to address inconsistencies and anomalies. 3. Statistical Reconciliation: Particularly useful during Machine Learning process to cook the data that becomes compatible to feed it to perform feature selection while building ML model and also to feed the data to train the model.

Data reconciliation is the process of comparing and verifying data in two systems or datasets to ensure consistency, completeness, and correctness after migration. It involves identifying and resolving discrepancies between the source and target data. For example, in a data migration scenario, data reconciliation may reveal discrepancies in record counts, missing values, or inconsistencies in data attributes. By reconciling data, organizations can validate the success of the migration process, identify any discrepancies or errors, and take corrective actions to ensure that the data in the target system aligns accurately with the source data. This process is crucial for maintaining data integrity and reliability post-migration.

Below are some great tools for the purpose: 1. Talend Data Integration: Offers comprehensive data reconciliation features. 2. Informatica PowerCenter: Provides robust data integration and reconciliation capabilities. 3. Microsoft SQL Server Integration Services (SSIS): Offers ETL and data integration with reconciliation features. 4. Apache Nifi: A powerful open-source tool for data integration, including reconciliation. 5. Trifacta: Focuses on data wrangling and reconciliation for diverse datasets. 6. Panoply: Cloud-based data platform with built-in reconciliation capabilities. Choose a tool based on your specific requirements, infrastructure, and preferences.

Post-data migration, harnessing the Power Platform is essential for thorough validation. Power BI facilitates visual comparisons via dashboards, metrics, and alerts for a holistic view. Power Automate automates validation WFs, ensuring efficiency and timely notifications. Power Apps empowers user-driven validation with custom applications and forms featuring validation rules. The Common Data Service guarantees unified data storage and facilitates automated data transformations. Integrating Power BI, Power Automate, Power Apps, and the Common Data Service streamlines workflows, boosts collaboration, and equips users to adeptly tackle completeness and correctness challenges, establishing a robust strategy for validating data after migration.

Data Quality should be approached by addressing that data quality is not an isolated activity, but involves every phase from data capture through processing, to usage; data quality may not need to be absolute, but rather sufficient to support planned usage, comply with applicable regulations, and minimize unnecessary costs; issues can have a varying level of criticality based on the impact to the business; identify candidate attributes from sources based on business criticality and suspected low quality based on feedback; Data Governance should be used to appropriately address data quality for the enterprise.

One thing that I always insist on is that we need to keep customer at the centre of things we do, Customer-Centric Innovation: Highlight the role of data in fostering customer-centric innovation. Involve customers and Discuss leveraging customer data insights ethically to personalize services, enhance user experiences, and build stronger relationships while respecting privacy concerns. Always be ready to listen and this will give you the best solution.

Plus the technical validations, parallel processing in as is and to be environments and validate. Functional Testing & validation with positive and Negative scenarios. Errors handling, implementing mechanism to identify errors and provide clear error messages aiding trouble shooting.

The recommendation is mature and rollout Data Observability as a Enterprise Capability which will encompass: - Foundational 1. Data Quality Monitoring - Automated tracking of data quality metrics like accuracy, completeness, uniqueness, timeliness to identify issues and trends across critical data sets and pipelines. 2. Metadata Management - Centralized visibility into key metadata like data schemas, lineages, dependencies, tags and glossary terms associated with data assets to enable discovery, trust and impact analysis. 3. Pipeline Observability - End-to-end visibility into health, throughput and performance of data integration processes and jobs enabling alerting and diagnostics. 4. Usage Analytics 5. Root Cause Analysis