With the advent of Big Data, there is a large quantity of structured and unstructured data entering the organizational databases every day. While it is important to mine valuable insights from this data, it is also important to ensure it that does not adversely affect application performance. If effective streamlining of data does not happen, it can quickly become a bottleneck for companies and this in turn can lead to data related performance issues.

Data Performance Testing has become a prerequisite for organizations in this scenario to address these challenges and is increasingly becoming an important area of focus.

At Adaequare, we understand that data performance cannot be tested and optimized in isolation. We fix end-to-end data management issues in your organization. Data Performance also depends on the enterprise applications using the data, and hence, we adopt both micro and macro-focused approach to data testing.

With a wide array of testing best practices and approaches gained over several years, we ensure a high performance and efficiency. We provide access to robust enterprise cloud environments and proven experience with physical and virtual data centers to quickly generate and mimic production strength loads.

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Approach

We employ a logical phase-wise approach for Data Performance Testing to ensure the optimal performance for your applications, networks and devices.

In Phase 1, we test databases and Data Flows in isolation. This involves:

01

Review of database schemas and ensuring that it is normalized or de-normalized enough to ensure peak performance

02 Look at data volumes, large datasets such as blobs and images, complex or time consuming database queries
03 Verifying if throughput and response times are within SLAs or if they can be further optimized through data tuning

In Phase 2, we test the Data from an end-to-end perspective. Further data optimization may be necessary after end-to-end performance of applications is verified.

Review database schemas and ensure that it is normalized or de-normalized enough to ensure peak performance
Look at data volumes, large datasets such as blobs and images, complex or time consuming database queries
Verify if throughput and response times are within SLAs or if they can be further optimized through data tuning
For more details about our services and solutions,Contact us now.