Data testing entails thoroughly verifying every component of the offered software, from the user interface to the most complex and minute operations. It is critical that the testing team considers every potential situation when building the test cases and comprehensively evaluates the offered software for each of them. The type of test data utilized by the test team has a significant influence on the whole test process. Test data is then used to test a specific piece of software. While some data is utilized to acquire confirmation findings, other data may be used to test the software’s capability. There are several methods for obtaining acceptable test data for evaluating a system. A tester or software can generate test data for a specific procedure.
For example, the testing team may wish to see if the program produces the expected output. The data would be sent into the system and executed. It would examine the findings to determine whether or not the intended outcomes were reached. The program should, at the very least, produce the desired outcomes without error. After all, it was created for this purpose and must fulfill it.
In contrast, it should not provide unexpected, strange, or severe outputs when given non-standard input. There must be enough test data to examine positive and negative scenarios. This guarantees that the program continues to run smoothly even if the end user enters incorrect information when using it or decides to do so on purpose to play with the system.
Experts disagree on whether actual production data or fake data should be used for testing. There are certain instances in which each of them is appropriate. Synthetic data, for example, works better in narrowly targeted experiments. Torana Inc. offers various services, including ETL testing, data warehouse and migration testing, data monitoring and governance, data architecture service, data testing as a service (DTaaS), and data migration services.
The Idea to tackle problems related to data-centric projects
A team of architects founded the firm in 2005 to tackle issues linked to data-centric projects and systems and to provide a platform for auditing large-scale enterprises’ data operations. The firm opened a software R&D unit in Nagpur, India, in 2008. Torana Inc employs 120 developers, architects, analysts, and consultants in the United States and India.
In 2010, Torana Inc released iCEDQ, a Data Migration, and ETL/Data Warehouse testing automation platform. It is a separate rules engine that is specifically built for data validation, auditing, and reconciliation between the source and destination systems. It is intended to provide users complete flexibility over how they verify and compare data sets and to allow them to create various sorts of tests or rules for data set validation and comparison. It also acts as a platform for enterprise data quality governance. iCEDQ performs large-scale data auditing and testing and offers a fully automated solution.
For almost 30 years, data transformation has been a critical component in how firms offer analytics-ready data. For many years, the data transformation process concentrated on the “T” in ETL (extract, transform, and load). Data transformation in an ETL pipeline primarily involves purifying data and mapping it from a source schema to a destination model. These mapping methods got more complicated as businesses’ destination schemas (star- and snowflake schemas) became more advanced. In the last half-decade of the 2010s, analysts saw data preparation as an essential component of self-service data transformation. Individual, less technical analysts may now undertake a wide range of data transformations without depending on and waiting for IT and data teams to construct data transformation pipelines for them.
Industry Verticals Covered by iCEDQ
For many businesses providing for their data testing and migration needs, iCEDQ is a priceless asset. High-volume data testing calls for precision and accuracy. As a result, iCEDQ has been continuously used by industry verticals like finance, banking, insurance, healthcare, hospitality, and retail to maintain their data quality. Maintaining data correctness ensures total data quality, assisting these businesses in making precise and fast judgments.
The Big Data Edition of the iCEDQ platform was introduced by Torana Inc in May 2018. This program version was released to help businesses test their migration to Hadoop or Big Data environments. In January of the following year, Torana Inc released the BI Testing and Report Testing module for iCEDQ.
For two years in a row, in 2015 and 2016, Torana Inc was included in the Inc. 5000. The company’s flagship software, iCEDQ, won the Finance Online awards for best-emerging star and premium usability. iCEDQ also won the 2019 Software Suggest Best Value Software Award.
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