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Making Better Sense of Big Data with Comprehensive Testing

  • Software testing
  • Abhay Das
  • June 12, 2017
  • Share ICon Share

Big Data is defined as large volumes of structured and unstructured data that can reveal patterns, trends, and associations.

According to an IDC study, the Big Data technology and services market is estimated to grow at a CAGR of 22.6 per cent from 2015 to 2020 and reach $58.9 billion in 2020; Big Data infrastructure to grow at a CAGR of 20.3 per cent to reach $27.7 billion;

software to grow at a CAGR of 25.7 per cent and reach $15.9 billion in 2020; services, including professional and support services, to grow at a CAGR of 23.9 per cent from 2015 to 2020 and reach $15.2 billion.

However, it is not without its challenges. A Gartner analysis shows that the average organization loses $14.2 million annually through Poor Data Quality.

For organizations to be able to draw value from Big Data, it needs to be tested for completeness, transformation and quality.

Testing Strategy

Data Validation

One of the key concerns of Big Data analytics is the sanity of the data itself. Therefore, functional testing services and data validation are critical in Big Data testing.

Since the data size runs in terabytes and the processing is very fast on Big Data infrastructure, testing requires a combination of skills involving standard testing techniques, Data Management, ETL, Cloud Infrastructure and Scriptwriting skills in Perl, Shell, Python etc.

The data needs to be checked for conformity, accuracy, duplication, consistency, validity and completeness. In addition, the following QA processes need to be implemented:

Step 1 – Data Testing:

  • To make sure all data is ingested
  • That proper data is ingested
  • Check that the data goes to the right database

Step 2 – Process Testing:

  • Map-Reduce process works correctly
  • Data segregation rules are applied appropriately
  • Key value pairs are generated
  • Validation of data post Map-Reduce process

Step 3 – Validating Output:

In the third stage, the output data files need to be validated for the following:

  • Whether the transformation rules have been applied correctly
  • For data integrity and correct loading of data into the target system
  • To ensure that the data is not corrupt

These three steps need the QA team to understand data, the purpose for which it is needed and the kind of output that will be required to ensure its relevance.

Architecture Testing

The second area requiring testing is the Big Data architecture to ensure that it is designed appropriately for optimum performance as well as meets business requirements.

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Performance and Failover test services are critical at this stage to ensure the robustness of the architecture.

Performance testing includes:

  • Time taken to complete the task
  • Memory being utilized
  • Data throughput and other related system metrics
  • Whether data processing occurs seamlessly in case of failed data nodes

Speed, a capability to process multiple data sources in parallel and the multiple components involved in data aggregation are some of the critical aspects tested at this stage.

QA Environment

The factors to be kept in mind while testing include:-

  • The availability of enough storage to process large amounts of data, to also include the replicated data
  • To reset test environment, through a data clean-up process for regression testing
  • Clustered approach with distributed nodes to ensure optimum performance

Test Automation Framework

While much of Big Data Testing may sound like course for the par, there are fundamental differences between database testing and big data testing – right from the volume of data to the architecture it needs and the environment.

Automation is one way to deal with the volume as well as reduce the testing time. It needs to be tested across different platforms, and performance challenges to be addressed.

The testing process also needs to be monitored constantly and diagnostic solution provided in case of any bugs.

An IP-driven test automation framework such as Indium’s iSAFE  is already equipped to handle the complexities posed by the large volumes of data.

It can be customized, it has an inbuilt monitoring and diagnostic tool that triggers alerts and communications to the developers with the detailed report, thus identifying and addressing the issues correctly.

Indium Software’s Big Data Testing Solutions harness strong capabilities in Hadoop, Spark, Cassandra, Python, MongoDB & Analytics Algorithms and combined with traditional strengths in testing techniques and frameworks, to meet our customers’ needs.

It helps organizations working with Big Data achieve their goals more effectively.

Abhay Das