Cloud Data Integration – A Modern Approach for Handling Test Data

With the adoption of new technologies, like IoT, AI, etc. and to keep up with the ongoing market traction, enterprises often have the urge to fasten the testing step in the software development lifecycle. Testing is a way to determine if the application performs according to the set configurations. Thus, the importance of software testing cannot be overstated throughout the cycle, but testing completeness and coverage depend mainly on test data quality.

Data Integration is the process of managing the data necessary for fulfilling the needs of automated tests with little human intervention. This process ensures the availability of test data reducing slippage in testing deadlines, and timely access to realistic and compliant data for test cases. According to a MarketsandMarkets report, the Test Data Management market has grown from USD 524.0 Million in 2016 to USD 1,060.9 Million in 2022 at a 12.7% Compound Annual Growth Rate (CAGR).

Importance of Data Integration in Test Data

Enterprises require fast, reliable test data for their projects across the software development lifecycle to bring quality applications to market. Incorporating data integration helps create better-quality products/software by making the test data available and ensuring its desired qualities.

Offers Realism in Data

The realism factor is important in the test data because the test environment is replicated to the end-user environment as close as possible to identify any bug and possible rollbacks at an early stage. Thus, the test process is justified only when data mimics the production data to understand how the system under test behaves in real-world scenarios. If the system is tested against generic data, many problems can arise in production.

Provides Continuous Data Delivery

Continuous testing requires accurate, relevant, high-quality data delivery. The quality data helps to discover defects earlier in the development life cycle and reduces the cost of production fixes. TDM also sorts out the data and archives it in a central repository, making it reusable. Reusability of data in the TDM process is the most valuable feature of the TDM as it reduces the cost.

Ensures the Security of Sensitive Information

With the intent to mitigate any defects in the system, the enterprises clone the production data for testing. This creates the risk of exposing sensitive data and can result in dire financial and legal consequences per industry standards and compliance. Test Data Management process helps to identify sensitive data and ensures data security by masking the read data and creating dummy data for testing.

Best Practices for Handling Data Integration

  • Setting requirements: Enterprises should adopt this practise to identify the test data requirements and optimize the efforts necessary to create test data.
  • Subsetting Data: This approach should be used to create realistic test databases wherein different subsets of the production database are copied and not the whole database. These subsets are small enough to support rapid test runs and reflect production data accurately.
  • Refreshing test data: This practise should be followed to reflect the latest and most-relevant test data. Updated test data helps to streamline the testing process and to keep data consistent, correct and available over time. This practice improves testing efficiencies.
  • Picking a Tool: Enterprises after understanding the importance of the TDM process should pick a TDM tool depending on their requirement. There are many TDM tools that differ from each other in many aspects, including price and the resources they offer.

Let’s look at some of the Data Integration and management tools available in the market:-

Skyvia

Skyvia is a cloud data platform that provides data integration, backup, real-time data access and management tools. The platform has a no-coding wizard that allows technically savvy professionals and business users with no technical skills to use it. Skyvia connects and collects data from various data sources, including cloud apps like Salesforce, Shopify, Mailchimp, and 131 more apps; databases like Amazon RDS, Oracle, PostgreSQL, SQL Server and nine other connectors; storage systems like Google Drive, OneDrive, Dropbox, Amazon S3, FTP and more; data warehouses like Google BigQuery, Amazon Redshift, Snowflake and Azure Synapse Analytics.

K2view

K2View’s Data integration tool is a Self-service portal to provision test data in minutes and on-demand. Getting the top score in the Gartner “Voice of the Customer” report, published in June 2021, reflects the high-end security the tool promises for sensitive information. This tool helps in reducing the time required in retrieving and preparing test data by quickly provisioning test data (associated with the particular business entities) from any number of production sources, based on user-defined rules. K2View’s key features include automating the data provisioning process on a single platform respective of the technologies and the number of testing environments. It delivers complete test data you can trust and is in compliance with privacy regulations.

Informatica

Informatica’s Data Management tool is designed to focus on data quality and privacy for development teams. This tool provides compliance at scale with data masking and subsetting capabilities for testing. It also offers synthetic data generation (non-production dataset) capabilities for testing needs. Informatica’s key features include its automatic provisioning capabilities, powerful monitoring and reporting features, and intensive masking techniques.

IBM

IBM InfoSphere Optim tool provides on-demand service facilities and creates and manages non-production data. The tool supports continuous testing, analyzes, and provides refreshed test data on-demand to improve operational efficiency. This feature helps in continuous testing and agile software development. IBM InfoSphere Optim’s key features include real-time data testing, data analysis capabilities, and complying with test data management software policies.

Conclusion

Testing is a non-negotiable step in the development cycle of a product, but the quality, reliability and consistency of the data come as a big challenge. Test data management is thus useful to fulfil the data needs. Thus, data integration tools help enterprises improve the quality of their software by generating high-quality test data in a reliable, consistent, and automatic way. Implementing data integration for testing projects helps generate a large volume of similar data swiftly and efficiently.

Source: datafloq.com

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