Deliver reliable, high quality and low risk product launch through continuous testing
QA Spider automates all aspects of product testing allows organizations to quickly pivot based on product feedback, product strategy and positioning.
All levels of verification and validation are done within the scope of Continuous integration. If there are defects, it goes back into the feedback loop for development teams to provide a fix.
As soon as the automation test scripts are committed to the version control system, the primary build is triggered on the ‘Development’ environment. Static code analysis scans the code for quality, safety and security
Once the availability of test scripts are a success, the test infrastructure is created with Device farms and virtual instances followed by a trigger of the secondary build of the ‘Test’ environment. At this stage all integrated microservices are validated for correctness and followed by the Build verification tests (BVT) and non-functional test execution.
Once build verification tests are complete, the ternary build is triggered on the ‘Pre-production’ environment followed by the full end-to-end regression test execution.
Transformation of Data, big or small, structured or unstructured into visually rich information that enable decisions and precise actions with few clicks. Data Visio helps clients Visualize on Demand, and helps Data Dashboarding of enterprise data for real time decisions.
A self-service distributed applications platform for enterprise engineering units to build, ship and run any application anywhere, on any infrastructure, from laptop to public cloud instances
Orchestration of processes related to product delivery has various technical challenges. Enterprises face additional challenges with integrating their existing tools and technologies in such orchestrations.
Cloud Sparrow helps customers automate "orchestration of processes related to product delivery" by integrating their existing tools and technologies. This enables organizations to gain competency in niche technology space and leverage it to make smart technical decisions.
Built a big data analytics platform to provide performance metrics, monitoring and predictive analysis of ‘Class-A’ applications for a fortune 500 enterprise customer. The platform uses Apache Hadoop ecosystem with Apache Spark for analysis of data from production environment. Performance metrics and reports are generated. Also provided is an executive dashboard to view key metrics to aid the management in their decision making and planning.
Built and deployed an API driven centralized env i ronmen t ma n a gemen t too l for SOA middleware. The tool had capability of pro-active monitoring of SOA systems in real time, generate usage reports, scale up and down on cloud infrastructure for a fortune 500 enterprise customer in a duration of 4 months for their ‘Class A’ application.
Set up a continuous delivery practice (Devops) to introduce agility in product delivery. We devised a solution with an integration of existing tools and technologies. The solution provided an integration with a version control, release management, infrastructure management and application management systems. Automating the entire process of delivery helped reduce operational expenses for a fortune 500 enterprise customer for their ‘Class A’ application.