What is Big Data Testing?

Big data testing is a process of testing data for quality and integrity so it can be processed to generate accurate insights. As digital transformation in capital markets increasingly leverage big data applications to improve revenue, reduce costs and address regulatory reforms, the ability to validate varied sources of data can be a game-changer.

Traditional tools cannot efficiently process large data sets.

Capital markets are data-intensive. Huge amounts of structured (market data, transactional data, reference data) and unstructured (news and social media feeds, corporate filings, and economic indicators) data are available to you.

Traditional data processing tools weren’t designed to improve the quality or make sense of large data sets. Big data tools can analyze large amounts of structured and unstructured data to generate logical patterns for interpretation and insights. As far as big data testing is concerned, there are different types of tests and tools to verify the accuracy of all the streams of data coming into your business.

What Does Big Data Testing Look Like?

The ultimate goal of big data testing is to ensure that data arriving from different sources is processed without errors. Data ingestion testing, for example, checks if the data in files, databases and near real-time records are correctly extracted and accurately loaded into a file system. The correctness of the data is validated by comparing the ingested data with the source data.

The big data processing test checks the correctness of business logic on the data aggregated when it is gathered from various sources. It focuses on how ingested data is processed and ascertains the correct implementation of business logic by comparing output (expected) files with input files.

The terabytes and petabytes of data generated must be processed accurately or they will go to waste. Big data is generated at a high velocity, and you have the opportunity to utilize real-time data for rapid near real-time insights. As Big Data processing must support a large variety of data, the quality and integrity of all the data has a direct impact on the quality of insights and business outcomes thereof.

Big Data Testing Strategies are based on three different scenarios :-

• Batch data processing testing
• Real-time data processing testing
• Interactive data processing testing

Batch data processing test
In Batch data processing tests the applications are run the data in Batch Processing mode, using Batch Processing Storage units. The test is usually done by running the application against faulty inputs and continuously varying the amount of data being tested.

Real-time data processing test
This test is done in the Real-time Processing mode and uses certain Real-time Processing tools like Apache Spark, Storm, Kafka and others. It involves testing the application for stability in the real-time environment.

Interactive data processing test
In this test the application interacts with real-life test protocols like a real-world user would interact with the data. This data processing test uses Interactive Data Processing tools like HiveSQL, BigSQL and others.

Steps Involved in Big Data Testing

The various steps involved in a Big Data testing strategy include:

Step 1 – Data Ingestion Testing

Verifying if the data is correctly extracted and loaded into HDFS (Hadoop Distributed File System).

Step 2 – Data Processing Testing

Verifying that business logic is implemented correctly whenever the ingested data is processed.

Step 3 – Data Storage Testing

Verifying if the output data is correctly loaded into the warehouse by comparing output data to warehouse data.

Step 4 – Data Migration Testing

Ensuring data is migrated from an old system to a new system without any errors or data loss and with minimum downtime.

Step 5 – Performance Testing

Verifying that the data application architecture allows for the processing of large amounts of data in short time intervals effectively.

Step 6 – Speed Testing

Testing of data processing speeds for the complete workflow from data ingestion to data visualization.

Advantages of Big Data Applications Testing

The benefits of Big Data testing to organizations are many. From improving decision-making to ensuring seamless data integration, Big Data testing plays a crucial role in improving the business processes. Quality testing of Big Data ensures that only accurate and useful information makes its way into the decision-making process. Apart from that, Big Data testing is found to minimize downtime, improve data security and prevent data inconsistencies, which also add up to build an organization’s reputation.

In capital markets too, real-time data feeds are critical to financial services companies requiring the ability to correlate, analyze and act on data such as market prices, trading data, company updates, geopolitical news and other information. If this data is being fed into tools for predicting future values, price movements and creating reports for clients, then it needs to be validated quickly, on a continuous basis.

Trading has become algorithm-driven and time sensitivity matters a great deal. Savvy traders are leveraging big data to gain a complete overview of their trading patterns and generate in-depth reports on profits and losses. Indian stock broking firms already offer platforms through which investors can access real-time predictions on all asset classes. Many are using analytics to predict margin-limit multipliers, and analyze customer sentiments and queries. As big data becomes integral and indispensable for businesses, the need for data quality will only increase.

Applications of Big Data Testing

Optimum data quality achieved through big data testing can create value in certain areas:

Trading strategy
Rich and accurate data are useful for performing accurate investor sentiment analysis. Traders working on commodity desks can draw judicious inferences from mining industry news and weather data.

Compliance
Market abuse monitoring is a real risk in the age of mobile devices and hybrid work. Validating the relevance of unstructured data for monitoring is crucial to correctly identify potential financial misconduct and effectively meet applicable compliance regulations.

Automated reporting
If you need to prepare ad-hoc market reports, you’re obligated to cross-reference multiple sources of data and gain an accurate, single picture view of the status quo. While automated reporting helps, the quality of data matters even more to provide reliable reports and earn customer trust.

Tools Supporting the Big Data Testing in the Capital Markets

Software testers can use one of many big data testing tools for storing and validating massive amounts of data.

Hadoop, for example, is a good addition to your tech stack for its ability to store huge amounts of data and considerable processing power for complete data validation.

Apache Cassandra is another open-source distributed NoSQL DBMS that can manage huge amounts of data across numerous servers, very quickly.

High-Performance Computing Cluster (HPCC) can also be reviewed as it allows data to be distributed across nodes and processed more efficiently and accurately.

Cloudera is a testing tool for enterprise-level technology deployment. This open-source tool lets organizations gather, manage and distribute huge amounts of data seamlessly. It also offers a free platform distribution including Apache Hadoop, Impala, and Spark.

Other useful tools include Lumify (data integration, analytics, and visualization), Apache Samoa (big data stream mining and machine learning), Talend (Open studio and real-time Big Data platform), to name a few.

After the tech stack is chosen, the testing automation framework and all its layers can be developed. A big data automation testing framework reduces testing time and allows you to generate insights from financial data faster. It helps in bringing about improvements in data quality and eliminates costs and man-hours you might have spent on data quality management.

Trends in Big Data Testing

The big data testing market was valued at $20.56 billion in 2019, and estimated to grow at a CAGR of 8.35% between 2020 and 2025. The increasing integration of artificial intelligence and analytics is expected to drive the capital market growth.

In-house big data engineers and data scientists can be tasked with big data application testing. External big data testing services can also step in for companies that don’t have the resources or time to develop an in-house team. Typically, these providers have a vast skill set across big data testing frameworks and technologies, and help you maximize benefits from good data. In this era of big data and bigger applications of this data – NSEIT is there to help you devise your strategy and testing around the big data. Get in touch with us to know more about this.