ETL Startegy to store data validation rules Every time there is movement of data the results have to be tested against the expected results. For every ETL process, test conditions for testing data are defined before/during design and development phase itself. Some that are missed can be added later on. Various test conditions are used to validate data when the ETL process is migrated from DEV-to->QA-to->PRD. These test conditions are can exists in the developer’s/tester’s mind /documented in word or excel. With time the test conditions either lost ignored or scattered all around to be really useful. In production if the ETL process runs successfully without error is a good thing. But it does not really mean anything. You still need rules to validate data processed by ETL. At this point you need data validation rules again! A better ETL strategy is to store the ETL business rules in a RULES table by target table, source system. These rules can be in SQL text. This will create a repository of all the rules in a single location which can be called by any ETL process/ auditor at any phase of the project life cycle. There is also no need to re-write /rethink rules. Any or all of these rules can be made optional, tolerances can be defined, called immediately after the process is run or data can be audited at leisure. This Data validation /auditing system will basically contain A table that contains the rules, A process to call is dynamically and A table to store the results from the execution of the rules Benefits: Rules can be added dynamically with no cange to code. Rules are stored permanantly. Tolerance level can be changed with ever changing the code Biz rules can be added or validated by business experts without worring about the ETL code. Data Warehouse Testing Businesses are increasingly focusing on the collection and organization of data for strategic decision-making. The ability to review historical trends and monitor near real-time operational data has become a key competitive advantage. We provide practical recommendations for testing extract, transform and load (ETL) applications based on years of experience testing data warehouses in the financial services and consumer retailing areas. There is an exponentially increasing cost associated with finding software defects later in the development lifecycle. In data warehousing, this is compounded because of the additional business costs of using incorrect data to make critical business decisions. Given the importance of early detection of software defects, here are some general goals of testing an ETL application: Data completeness. Ensures that all expected data is loaded.
Data transformation. Ensures that all data is transformed correctly according to business rules and/or design specifications.
Data quality. Ensures that the ETL application correctly rejects, substitutes default values, corrects or ignores and reports invalid data.
Performance and scalability. Ensures that data loads and queries perform within expected time frames and that the technical architecture is scalable.
Integration testing. Ensures that the ETL process functions well with other upstream and downstream processes.
User-acceptance testing. Ensures the solution meets users’ current expectations and anticipates their future expectations
Regression testing. Ensures existing functionality remains intact each time a new release of code is completed
Data Completeness One of the most basic tests of data completeness is to verify that all expected data loads into the data warehouse. This includes validating that all records, all fields and the full contents of each field are loaded. Strategies to consider include: Comparing record counts between source data, data loaded to the warehouse and rejected records.
Comparing unique values of key fields between source data and data loaded to the warehouse. This is a valuable technique that points out a variety of possible data errors without doing a full validation on all fields.
Utilizing a data profiling tool that shows the range and value distributions of fields in a data set. This can be used during testing and in production to compare source and target data sets and point out any data anomalies from source systems that may be missed even when the data movement is correct.
Populating the full contents of each field to validate that no truncation occurs at any step in the process. For example, if the source data field is a string(30) make sure to test it with 30 characters.
Testing the boundaries of each field to find any database limitations. For example, for a decimal(3) field include values of -99 and 999, and for date fields include the entire range of dates expected. Depending on the type of database and how it is indexed, it is possible that the range of values the database accepts is too small. Data Transformation Validating that data is transformed correctly based on business rules can be the most complex part of testing an ETL application with significant transformation logic. One typical method is to pick some sample records and “stare and compare” to validate data transformations manually. This can be useful but requires manual testing steps and testers who understand the ETL logic. A combination of automated data profiling and automated data movement validations is a better long-term strategy. Here are some simple automated data movement techniques:
Create a spreadsheet of scenarios of input data and expected results and validate these with the business customer. This is a good requirements elicitation exercise during design and can also be used during testing.
Create test data that includes all scenarios. Elicit the help of an ETL developer to automate the process of populating data sets with the scenario spreadsheet to allow for flexibility because scenarios will change.
Utilize data profiling results to compare range and distribution of values in each field between source and target data.
Validate correct processing of ETL-generated fields such as surrogate keys.
Validate that data types in the warehouse are as specified in the design and/or the data model.
Set up data scenarios that test referential integrity between tables. For example, what happens when the data contains foreign key values not in the parent table?
Validate parent-to-child relationships in the data. Set up data scenarios that test how orphaned child records are handled.
Data Quality For the purposes of this discussion, data quality is defined as “how the ETL system handles data rejection, substitution, correction and notification without modifying data.” To ensure success in testing data quality, include as many data scenarios as possible. Typically, data quality rules are defined during design, for example: Reject the record if a certain decimal field has nonnumeric data.
Substitute null if a certain decimal field has nonnumeric data.
Validate and correct the state field if necessary based on the ZIP code.
Compare product code to values in a lookup table, and if there is no match load anyway but report to users. Depending on the data quality rules of the application being tested, scenarios to test might include null key values, duplicate records in source data and invalid data types in fields (e.g., alphabetic characters in a decimal field). Review the detailed test scenarios with business users and technical designers to ensure that all are on the same page. Data quality rules applied to the data will usually be invisible to the users once the application is in production; users will only see what’s loaded to the database. For this reason, it is important to ensure that what is done with invalid data is reported to the users. These data quality reports present valuable data that sometimes reveals systematic issues with source data. In some cases, it may be beneficial to populate the “before” data in the database for users to view. Performance and Scalability As the volume of data in a data warehouse grows, ETL load times can be expected to increase, and performance of queries can be expected to degrade. This can be mitigated by
having a solid technical architecture and good ETL design. The aim of the performance testing is to point out any potential weaknesses in the ETL design, such as reading a file multiple times or creating unnecessary intermediate files. The following strategies will help discover performance issues: Load the database with peak expected production volumes to ensure that this volume of data can be loaded by the ETL process within the agreed-upon window.
Compare these ETL loading times to loads performed with a smaller amount of data to anticipate scalability issues. Compare the ETL processing times component by component to point out any areas of weakness.
Monitor the timing of the reject process and consider how large volumes of rejected data will be handled.
Perform simple and multiple join queries to validate query performance on large database volumes. Work with business users to develop sample queries and acceptable performance criteria for each query. Integration Testing Typically, system testing only includes testing within the ETL application. The endpoints for system testing are the input and output of the ETL code being tested. Integration testing shows how the application fits into the overall flow of all upstream and downstream applications. When creating integration test scenarios, consider how the overall process can break and focus on touch points between applications rather than within one application. Consider how process failures at each step would be handled and how data would be recovered or deleted if necessary. Most issues found during integration testing are either data related to or resulting from false assumptions about the design of another application. Therefore, it is important to integration test with production-like data. Real production data is ideal, but depending on the contents of the data, there could be privacy or security concerns that require certain fields to be randomized before using it in a test environment. As always, don’t forget the importance of good communication between the testing and design teams of all systems involved. To help bridge this communication gap, gather team members from all systems together to formulate test scenarios and discuss what could go wrong in production. Run the overall process from end to end in the same order and with the same dependencies as in production. Integration testing should be a combined effort and not the responsibility solely of the team testing the ETL application greek143 11 Answers Member Since Aug-2010 Flatfiles Validations When we are extracting the flatfiles, What are the basic required validations? Ans. Flatfiles Validations
Folllowing are some common validations performed: a) Check for blank lines and remove them. b) Check the number of column in each row of the file. c) If there is a trailer line in the flat file containing additional information like total number of records,then a cross check is performed to check if the number of records specified in the trailer and the actual number of records are same. d) Check if a column contains balnk value (If it is expected to have values). Data Validations It depends upon the requirment you needed. Some bascic checks: 1. NULL validation 2. Data type validation if you consider Data quality below points may come across 1. Address fileld validations 2. Word validations 3. Character validations What is Requirements Traceability? Requirements traceability is defined as the ability to describe and follow the life of a requirement, in both a forward and backward direction (i.e., from its origins, through its development and specification, to its subsequent deployment and use, and through periods of ongoing refinement and iteration in any of these phases) Traceability ensures completeness, that all lower level requirements come from higher level requirements, and that all higher level requirements are allocated to lower level requirements. Traceability is also used in managing change and provides the basis for test planning. Benefits: To identify the extent to which the business requirements have been covered by functional and system requirements. To identify the ‘orphan’ functional and system requirements. This would indicate a missing trace between requirements To identify the extent to which system requirements are covered from a design perspective. To identify the functional coverage of the QA test scenarios.
To identify which design components implement a requirement. To identify the test scenarios that will be used to verify a requirement To analyze the impact of changing requirements on the software artifacts created in subsequent phases of the SDLC For Any given project, three important questions that need to be answered before embarking on any particular requirements traceability approach are : What needs to be traced ? What type of linkages need to be made? How and when and who should establish and maintain the links What needs to be traced : Application Components Business Requirements Functional Requirements System Requirements Design Artifacts Testing Artifacts Type of Links: Forward, Backward links between requirements Vertical links between requirements and other artifacts Internal /External Links Who, How and When? Project Manager, Business Analyst, Development Lead? Through Tools or through document linking and references Stages in SDLC with well defined entry and exit criteria for defining the links Link requirements to external documents/ URL’s to enhance requirement description. Link requirements across projects Get a full view of how requirements are related to each other through “Matrix” view or “Tree” view Get full description of the linked requirements through click of a button Prevent unpleasant surprise through real time alerts when requirements change. Traces are automatically marked suspect when requirements change Get full description of the change by comparing versions of the requirements
Generate functional coverage reports to reflect requirements which have not been addressed in the project Generate test coverage reports to identify requirements which have not been taken into account for testing purposes Features: Automatic conversion of links to suspect when requirements change Trigger alerts to concerned parties when requirements change Facility to identify the change in the request at click of a button Features: Identify Orphan requirements or hanging requirements (Dark Bands) Identify implied links (shown in red circle) Generate links on the fly (Not shown) Features: Trace to requirements in external projects Trace to artifacts in configuration management tools Trace to artifacts in design tools
