The definition of Big data as per Gartner is, “Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation”. In current scenario, Big data is in use in almost everywhere. As per a survey, 62% of companies use this Big data to assists business. Below is a pie chart that gives the reasons why companies use big data for analysis.

Related post – 8 Characteristics of Big data-features of Big data

What is Big Data Testing?

Big data must be used strategically. It should be collected, analysed, and retrieved with proper plan. For this Big data needs various testing procedures to reveal their usage and characteristics.  As we know the primary characteristics of Big Data are:

1. Volume, 
2. Velocity, 
3. Variety
4. Veracity
5. Value

Why Big Data Testing needs strategy?

Big data testing strategy is required as there are several areas in Big Data. Big data testing comes in different flavors like

• Database testing
• Performance Testing
• Infrastructure testing
• Functional testing

Besides it may be

• Structured
• Unstructured
• Semi-structured

It may exist in many formats like flat files, videos,images, etc.

Also there may be different parameters of Big data based on its V’s or characteristics.

Types of Big Data Testing

Big data testing can vary based on the each characteristic of data. The different types of Big data testing depend on several requirements such as

• Testing automation tools
• Testing experts and their skills
• Ready to use processes that can validate data movement.

These testing methods validate the Vs of Big Data such as– Volume, Velocity, Variety  and Veracity. 

Here are examples of some Big data testing that helps to analyze different characteristics of Big data.

What are Big data Testing Strategies Key Components

As we have discussed about the Vs and the Big data types, let see the strategies involved in data testing. Here are the key components in testing data :

1. Data validation
2. Process validation 
3. Outcome validation

Data Validation

This is the very first important stage where we need to ensure that the collected  is not corrupted and accurate. Validation takes places in the Hadoop Distributed File System (HDFS). Here the data gets partitioned and checked thoroughly in a step by step validation process. Different tools like Datameer, Talend and Informatica are used for this purpose.

In a nutshell this step ensures whether right data is entered in the Hadoop Distributed File System (HDFS). Once this testing stage is passed it enters into the next testing system.

Process validation

This is the Business Logic validation or Process Validation step. In this step, the business logic is tested at every node point. Map Reduce is used for this purpose. The tester tests for the process as well the key-value pair generation. Once this step is executed successfully the data validation is considered finished.

Output Validation

This check is performed in the downstream to validate if any distortion is present in the processed data. The output files created in the process are moved to the Enterprise Data Warehouse (EDW) to check for data corruption.

Big Data Automation Testing Tools

Big Data uses different automation testing tools that can integrate with platforms like Hadoop, MongoDB, Teradata, AWS, other NoSQL products etc. The automation testing tools must be able to integrate with devops for continuous delivery. These tools must match below criteria –

• must have a good reporting feature
• flexible to constant changes
• must be scalable
• economical
• reliable.
• They must be able to automate repetitive tasks

Here are some of the tools –

• HDFS (Hadoop Distributed File System)
• MapReduce
• Hive
• HiveQL
• HBase
• Pig Latin

 Benefits of Big Data Testing

Companies can achieve various benefits with proper data testing strategies. Few of them are listed below:

Decision making – It prevents from bad decision making. Furthermore, it helps to achieve data-driven decision-making. Eventually, it drives decision making easy as we have data and analytics in hand with correct measure.

Data Accuracy – Since Big data testing assures defect free data and analysis on them that helps businesses to identify the weak spots. As a result business can deliver better than their competitors.

To implement a better strategy to enhance market goals – As with tested Big data we can optimize the business data this helps in creating better results as per the current situations. 

Helps to minimize losses and increases revenues – Even if we face a loss, we can minimize that with proper analytics of the data. It isolates different data types to enhance CRM.

Quality Cost – The big data testing methods are very less expensive so that business can gain more revenue generation. It has a high ROI.

Along with the above points business can perform seamless integration, reduce time to market and reduce total cost of quality.

Big Data Testing Best Practices

• Testing based on requirements
• Stay connected with the context
• Prioritize the fixing of bugs
• To save time, automate it
• Communication
• Test objective should be clear
• Technical skills