DWBI Testing and Analytics Testing Services

Codetru excels in providing comprehensive DWBI (Data Warehousing and Business Intelligence) testing services. Their expert team ensures the accuracy, reliability, and performance of data warehouses and BI systems through meticulous testing processes. By leveraging advanced tools and techniques, Codetru validates data integration, ETL processes, and reporting functionalities, ensuring high-quality, actionable insights for informed decision-making. Codetru's commitment to excellence and precision makes them a trusted partner for DWBI testing needs.

1. Empowering your journey towards digital excellence codetru.com Visit Our Website [email protected] | [phone] - Ext: 339

2. 350+ Head Count 50+ Active Clients 300+ Total Clients 4 Global Offices in US & UK 1 Delivery Center in India 2012 Codetru established with exceptional passion & broader vision 2019 •Introduced Mobile and Web Development offering.​ •Opened a new Mobile Application Testing Lab 2021 •Built Solutions for Large firms (Asian Paints, Trustiphi & Innophos ) •Supported numerous Digital Transformation Journeys success fully. 2024 On track to achieve 6x growth rate 2018 •Increased team to 50+. •Setup a new office location 2020 •Increased workforce by 60% •Withstood the chaos of Covid 2022 •Achieved 3x revenue generation •Added more digital offerings

3. Insurance E-Commerce & Retail Banking & Finance Energy, Travel & Logistics Healthcare Telecom, Hi Tech, ISV Education Entertainment & Media Clientele

4. Service Offerings Mobile & Web Development UI / UX Development Application Maintenance & Support Application Modernization & Migration Custom Application Development Application Integration Cross-platform Support Application Development QA & Software Testing DevOps & Automation Data Analytics AI-ML Managed IT Services Functional Testing Non-functional Testing Specialized Testing Testing Advisory CI / CD Delivery Configuration Management Release Management Monitoring & Logging Process Automation Cloud Migration Analytics & BI Data Modernization Cloud Services BI Strategic Services Machine Learning and Model Development Natural Language Processing [NLP] AI Chatbots Data Preparation & Management Model Deployment & Integration AI Consulting Data Management Big Data Social BI Data Warehousing BI Strategic Services BI Application Services Server Automation Managed Hostings Virtualization Services End-user Computing

5. Quality Assurance & Software Testing ERP Testing Web & Mobile Testing IoT Testing Blockchain Testing AI-ML Testing Cloud Testing DWBI Testing Manual Testing Test Automation Performance Testing Security Testing Functional Testing Non-functional Testing Spcialized Testing

6. Core Testing Core Testing Core Testing Functional Regression System Integration Test Management Consulting Automation Testing Automation Plan Framework Design Development Execution Acceptance Maintenance Automation Testing Tools & Technologies: Tools & Technologies: QA Competency

7. Corporate growth Mergers, acquisitions, and restructuring of disparate systems Compliance Validations against regulations and standards i.e. HIPAA, PCI, SOX. Data volume Escalating amounts of data Data diversity New data formats like RFID, SMS, and e-mail- increases complexity. Data decay Data deterioration at a rate of 10 percent to 25 percent per year Data denial Organizations unaware of their data quality issues Technical advances Proliferation of new data devices ( IoT ), platforms, and operating models Economic factors Pressure to use data for competitive advantage Drivers of Data Complexity

8. Following is the Architecture of a typical data warehouse specifying the source data, the target data warehouse and various data marts. Legacy DB CRM/ERP DB Finance DB Source Data ETL Process Target DW ETL Process Data Mart BI The architecture below depicts the various types of testing that can be performed for any data warehouse testing engagement Functional Testing Non – Functional Testing Data Validation Testing Report Testing Integration Testing Backup & Recovery Testing Performance Testing Security Testing Regression Testing User Acceptance Testing DWBI Testing

9. The complexity and criticality of data warehouse testing projects is growing rapidly each day.  Data warehouses need to be validated for functionality, quality, integrity, availability, scalability and security based on the defined business requirements by an organization. Testing the Data warehouse basically includes testing of the following: Conceptual Schema Logical Schema ETL Procedures Database Front-end The following is the mapping of testing types and the areas of to be tested: Conceptual Schema Logical Schema ETL Procedures Database Front-end Functional Testing     Usability    Performance     Back-up & Recovery   Security    Regression      Data Warehouse Testing

10. Key Points for BI Testing: Complexity of the data is the major challenge as large number of data sources are involved. BI reports use flash and other technologies that create problems for traditional test automation.  It is difficult to use traditional testing tools to automate the testing of BI applications built using tools such as OBIEE, Cognos and Business Objects BI testing can be done using tools like Motio CI, BI Validator, etc. These tools helps to diagnose problems faster by eliminating manual tests and also reducing development costs BI Application Testing

11. Data Validation Testing Data Completeness Data Quality Data Cleanness Field to Field Testing Constraint Testing Source to Target Testing Business Rules Checks Granularity Aggregation Data Refinement Report Testing Standards, Prompts, Drill Down, Summary, Look & Feel, Graphs / Charts, Measures / Dimensions Verify report data with the data source Create SQL queries to verify source/target data Backup & Recovery Data Recovery Data Backup E-mail functionality Audit Logs Standards User Acceptance Testing End User Representative Validate Usability Integration Testing ETL Modules Integration DB-Report Integration Verify the initial load of records and the incremental loading of records on data warehouse Test error log generation Data Warehouse / BI Functional Testing Functional Testing of Data Warehouse / BI includes

12. The objective of this testing to ensure that System meets the security and performance expectations of the business users. It aims to prove that the entire system operates effectively in a production environment and that the system successfully supports the business processes from a user's perspective. Performance Testing Security Testing 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 &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 ETL Security Checks The front-end applications must not be accessible from outside company network Data entry points of the warehouse needs to be secure Database Security Checks Sensitive data is accessible only by certain people Data movement needs to be monitored Reports Security Checks User must access Reporting and OLAP with only specific credentials Data Warehouse / BI Functional Testing

13. Level 1 Sampling Sampling a % of data by visually comparing data sets. Not repeatable. Excel, Ad Hoc Reporting Level 2 Using Excel or other homegrown method. Ad hoc reporting. Level 3 Utilizing SQL editor & minus queries to test data. More detailed reporting.  Data Test Automation Level 4 Queries Fully repeatable test automation, centralized reporting. Data Quality Optimizing Level 5 Full automation, tracking of ROI, predictive data issues, auditable history & results. Business value is fully understood/supported by management. Data Testing Maturity

14. Consulting & Strategy Automation Feasibility Strategy ROI Assessment Tool Evaluation POC Test Lifecycle Initiate Requirement Review GAP Analysis Test Strategy Creation Traceability Matrix High-level Test Plan Plan Test Case Writing Set-up Test Environment Set-up / Plan Test Tools Set-up Defects Management Process Detail Test Plan Execute Execute Test Cases Report Defects & Re-testing Automation Designing & Scripting Automation Script Execution Report Defect Logs Test Summary Reports Defect Trends QA Metrics Improvement Process Initiation Approach

15. Dynamic & Flexible Staffing Dynamic Flexible Staffing Access to Field Experts Ramping Up and Down of Resources Access to Missing Skills Consultancy Services On-going Professional Support Access to talent pool QA Efficiency and & Effectiveness Improvement End-to-end Needs Management Dedicated Testing Team Continuous Asset Building Managed Testing Services Fixed Term Services Set-term Outsourcing Project-based Agreement Resource Availability at Key Milestones 1 2 3 4 QA Engagement Model

16. Mobile Web J2EE QA and Testing Ecommerce Cloud & Data Skillset

17. Why Codetru Experience & Expertise Quality of Service Innovative Approach Strong Market Sense Competitive Pricing Faster Resource Turnaround 60+ Successful Projects 30% Less Testing Cost 9+ Industries Served 50+ Automation Testers 30+ Key Clients 100+ Total Testers Codetru Highlights

18. Success Stories codetru.com Visit Our Website

19. Problem Statement The client, a leading technology company, was developing an innovative solution to automate their existing manual process. The solution was intended to simplify the process and improve productivity. They wanted to ensure that the product was thoroughly tested and bug-free before launching it in the market. Our team was responsible for providing QA and testing services to the client, including test planning, test execution, and test reporting.​ Our team faced several challenges during the project. Firstly, the client's team was working on an Agile development process, which meant that the requirements were continuously evolving, and we had to be flexible in our testing approach. Secondly, the client had a tight timeline, and we had to ensure that we delivered the project within the given timeframe. Thirdly, the application was complex, and there were several integration points that required thorough testing.​ Solution : To address the challenges, we adopted a comprehensive testing approach that involved the following:​ Test Planning: Our team worked closely with the client to understand the requirements and developed a test plan that aligned with their needs.​ Test Design : Based on the requirements, our team developed test cases, test scenarios, and test scripts that covered all aspects of the application.​ Test Execution : We conducted functional, integration, and regression testing to ensure that the application worked as expected.​ Test Reporting : We provided regular test reports that highlighted the progress of testing, identified defects, and suggested corrective actions.​ Deploying the Python Utility ​: Once the utility was tested and verified, the team deployed it on a Windows server and scheduled it to run at regular intervals using the Windows Scheduler.​ Result : Our team successfully delivered the project within the given timeline, and the client was satisfied with the quality of our work. We identified several defects during the testing phase, which we resolved promptly. Our approach helped the client improve the overall quality of their product, and they were able to launch it in the market successfully.​ Technologies Used: Tools: Selenium, JMeter, Appium, TestComplete ​ Coding Language: Python Success Story #1

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