The never-ending surge for the Creation, Storage, Retrieval and Analysis of the colossal volumes of data, triggered the motivation behind the origin of Big Data Testing. Testing such a gigantic amount of data requires precision tools, remarkable frameworks and brilliant strategies.
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In this article, we will learn the Techniques, Tools, and Terminologies behind Big Data Testing.
- Need for Testing Big Data
- What exactly is Big Data Testing
- Strategies behind Testing Big Data
- Big Data Forms
- Big Data Testing Environment
- Big Data Testing
- Big Data Testing Tools
- Challenges faced in Testing Big Data
- Traditional Testing and Big Data Testing
Need for Testing Big Data
Most of the users might end up with one question that asks “Why exactly we need Big Data Testing?” you might have written the queries correct and your Architecture might just be fine. Yet, there might be many possibilities for failure.
Let us assume a classic case of a drastic failure that occurred in a bank. The designers of the bank database name the Customer Bank location pin code column as CL, Customer ID column as CI and Customer Phone number column as CP.
Then, the CP(Customer Phone Number) is replaced within the key-value pairs CL(Customer bank location Pin). Now the Customers wouldn’t get OTP and Phone Banking Facilities.
Just imagine this in a Real-Time situation. To avoid such mistakes, we prefer to test the Big-Data. Learn more about Big Data concepts from the Azure Data Engineer Certification Course.
What exactly is Big Data Testing
Big Data Testing can be defined as the procedure that involves examining and validating the functionality of the Big Data Applications. Big Data is a collection of a huge amount of data that traditional storage systems cannot handle.
Testing such a huge amount of data would take some special tools, techniques, and terminologies which will be discussed in the later sections of this article.
Strategies behind Testing Big Data
Testing an Application that handles terabytes of data would take the skill from a whole new level and out of the box thinking. The core and important tests that the Quality Assurance Team concentrates is based on three Scenarios. Namely,
- Batch Data Processing Test
- Real-Time Data Processing Test
- Interactive Data Processing Test
Batch Data Processing Test
The Batch Data Processing Test involves test procedures that run the data when the applications in Batch Processing mode where the application is processed using Batch Processing Storage units like HDFS. The Batch Process Testing mainly involves
- running the application against faulty inputs
- varying the volume of the data
Real-Time Data Processing Test
The Real-Time Data Processing Test deals with the data when the application is in Real-Time Data Processing mode. The application is run using Real-Time Processing tools like Spark.
Real-Time testing involves the application to be tested in the real-time environment and it is checked for its stability.
Interactive Data Processing Test
The Interactive Data Processing Test integrates the real-life test protocols that interact with the application as in the view of the real-life user. Interactive Data Processing mode uses Interactive Processing tools like HiveSQL. Learn more about Big Data concepts from the Azure Data Engineering Training in Mumbai.
Big Data Forms
Big-Data gains its fame through its superiority in handling multiple formats of data which the traditional data processing units fail to handle. The data formats which the big data can handle are as follows.
- Structured Data
- Semi-Structured Data
- Unstructured Data
Structured Data
Any tabular data which is meaningfully organised under rows and columns with easy accessibility is known as Structured Data. It can be organised under named columns in different storage units such as an RDBMS.
Example: Tabular Data
Semi-Structured Data
Semi-Structured Data lies perfectly in between the Structured and Unstructured Data. It cannot be directly ingested into an RDBMS as it includes metadata, tags, and sometimes duplicate values. Data needs some operations to be applied to it before the data is ready to be ingested.
Example: .CSV, .JSON
Unstructured Data
Data that does not obey any kind structure is known as Unstructured data. Unlike the Structured Data, The unstructured Data is difficult to store and retrieve. Most of the data generated by the organisations are Unstructured type of data.
Example: Images, Videos, Audio
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Big Data Testing Environment
Owning the perfect Environment for testing a Big Data Application is very crucial. The basic requirements that makeup Data Testing are as follows.
- Space for Storing, Processing and Validating Terra bytes of data should be available.
- The Cluster and its respective nodes should be responsive
- Data Processing resources like the powerful CPU should be available
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Big Data Testing
The General approach to test a Big Data Application involves the following stages.
- Data Ingestion
- Data Processing
- Validation of the Output
Data Ingestion
Data is first loaded from source to Big Data System using extracting tools. The Storage might be HDFS, MongoDB or any similar storage. Then, the loaded data is cross-checked for errors and missing values.
Example: Talend
Data Processing
In this stage, the key-value pairs for the data get generated. Later, the MapReduce logic is applied to all the nodes and checked if the algorithm works fine or not. A data validation process takes place here to make sure the output is generated as expected.
Validation of the Output
At this stage, the output generated is ready to be migrated to the data warehouse. Here, The transformation logic is checked, the data integrity is verified and the key-value pairs at the location are validated for accuracy.
There are many categories involved in which a Big Data Application can be tested. Few of the major categories are enlisted below.
Unit Testing
Unit Testing in Big Data is similar to any other unit testing in simpler applications. The complete Big Data Application is divided into segments and each segment is rigorously tested with multiple possibilities for an expected outcome. If the segment fails, then it is sent back to the developments and improvements.
Functional Testing
Functional Testing can be otherwise called as the different phases in testing the big data application. The Big Data Application is designed to deal with huge blocks of data. Such a huge volume and variety of data is often prone to bring data issues, such as bad data, duplicate values, metadata, missing values and whatnot.
This is exactly why the pioneers in testing the big data, designed the procedure for functional testing of big data. The different phases in which the big data is tested are as follows.
- Data Validation Phase
- Data Integrity Phase
- Data Ingestion Phase
- Data Processing Phase
- Data Storage Phase
- Report Generation Phase
Now, let us discuss each one of these phases in an elaborate way.
Data Validation Phase
- Data validation phase deals with the Business logic and the layers in the Big Data Application
- The Data is collected from the source and it is run against the business use case
- Data collected is checked for accuracy and movement through the Layers of the application
- At this stage, the big data is tested with aggregation and filtering mechanisms
- The data undergoes End-to-End Validation and Transformation logic, based on Business rules
Data Integrity Phase
- Data is checked for completeness with referential integrity validation
- Data constraints and duplication is verified against error conditions
- Boundary testing which recognizes schema limits of each layer
Data Ingestion Phase
- The ability of the application to connect with different data modules is checked
- The data is replayed with messaging systems and any loss of data is monitored
- The main motto of this phase is to achieve the following qualities
- Fault Tolerance
- Continuous Data Availability
- Stable connection with a variety of Data Streams
Data Processing Phase
- Data processing Phase carefully examines and executes the business logic
- The business rules are cross-validated
- The Map-Reduce logic is validated at every stage
- Data is processed from End-to-End
- The application is checked for exceptions and they get perfectly handled
Data Storage Phase
- Data Storage Phase concentrates on the following parameters
- Read and Write Timeouts
- Continuous Availability
- Load Balancing
- Query Performance Analysis
Report Generation Phase
- It is the final stage in Functional testing. it deals with the following.
Non-Functional Testing
The Non-Functional Testing phase takes care of the three major dimensions and characteristics of Big Data. The Volume, Velocity, and finally the Variety of the Big Data. There are five stages involved in Non-Functional Testing.
- Data Quality Monitoring
- Infrastructure
- Data Security
- Data Performance
- Fail-over Test Mechanism
Data Quality Monitoring
- Data Quality Monitoring checks for erroneous data, records, and messages.
- Data Quality Monitoring makes sure the following parameters about data.
- Data Accuracy
- Data Precision
- Data Timeliness
- Data Consistency
- Data Profiling
Infrastructure
- Infrastructure makes sure there is a continuous service availability in both
- Internal Big Data Application Systems
- External Big Data Application Systems
- Infrastructure also takes care of the following
- Data Replication
- Data Backup
- Data Restore
Data Security
- Data Security is the most important aspect of any Big Data Application.
- Data Securit stage protects the sensitive data.
- It manages User Authentication checks and User Role-Based Authorization.
- Data Encryption and Masking of Personal Information
Data Performance
- Data Performance evaluates every single component.
- Evaluates maximum Data processing speed
- Evaluates maximum data capacity size
- Checks the Message transfer speed and response time
- Calculates the number of operations performed per unit time
- Engages parallel job monitoring
- Performs Read, Write and Update operations on Real-time Databases
Fail-over Test Mechanism
- Ensure seamless data processing while switching to neighbouring data nodes
- Create data recovery points parallelly and be ready for calamities.
- Be ready to replay the data using multiple offsets
- Enable Dynamic Clustering
Performance Testing
Performance testing highly concentrates on the performance delivered by all the components of the big data system. Performance testing includes the following Categories.
- Data Collecting Phase
- Data Ingesting Phase
- Data Processing
- Component Peripheral testing
Data Collecting Phase
In this Stage, Big Data System is validated based on its speed and capacity to grasp the data within a given timeframe from the different sources like RDBMS, Databases, Data-ware houses and many more.
Data Ingesting Phase
The next phase after Data Collection is the Data Ingestion. Here the application is tested and validated based on its pace and capacity to load the collected data from the source to the destination which might be HDFS, MongoDB, Cassandra or any similar Data Storage unit.
Data Processing
Here, the Application is tested based on the Map-Reduce logic written. The logic is run against every single node in the cluster and the processing speeds are validated. The Queries to be executed are expected to perform with high speeds with low latency.
Component Peripheral testing
This stage is related to component performance. Each component in the system should be highly available and connected. The component backup should be online when any node faces failure. High Capacity Data Exchange should smoothly be supported.
Performance Testing Approach
- The procedure begins by establishing the Big-Data Cluster and Running the Application
- Design the workload required to run the test
- Involve the clients in the test and take their feedback
- Execute the application with the data and analyse the results.
- If the Application is performing with optimum stability, then the process is finished
- Else, apply the required modifications and re-test the application
Parameters involved in Performance testing
- Data Storage: Take note of the orientation in which the data gets stored in the system
- Commit Logs: Mark the limits for committing logs
- Concurrency: Check the number of threads allocated for the read-write process
- Cashing: Dedicate the row cache and key cache
- Timeout: Set timeouts for application, connection and queries etc.
Architecture Testing
Architecture testing concentrates on establishing a stable Hadoop Architecture. The architecture of Big Data Processing Application plays a key role in achieving smooth operations. Poorly designed architecture leads to chaos like,
Big Data Testing Tools
Various tools used in testing the Big Data are mentioned as shown below:
| Process | Tools Description |
| Data Ingestion | Zookeeper, Kafka, Sqoop |
| Data Processing | MapR, Hive, Pig |
| Data Storage | Amazon S3, HDFS |
| Data Migration | Talend, Kettle, CloverDX |
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Challenges faced in Testing Big Data
- Big Data Testing is highly complicated and the process requires a highly skilled official.
- Automated Big Data Testing procedures are predefined and not suited for unexpected errors.
- Virtual Machine latency creates latency in tests and managing multimedia is a hazzle
- The volume of the data is one major challenge for testing
- Test environment and automation should be developed for different platforms
- Each component is from a different technology hence, requires isolated testing
- No single tool can perform end to end testing
- High Degree of scripting is required for designing test cases
- Customised Solutions are required to increase performance and test critical areas
Traditional Testing and Big Data Testing
| Traditional Testing | Big Data Testing |
| Supports Structured Data | Supports all types of Data |
| Testing Does not R&D | R&D is Testing needed |
| limited Data size does not require special Environment | Special Environment is needed because of the huge data |
| Uses EXCEL based Macros or UI based automation tools | Has a vast range of programmable tools for testing |
| Basic Operations knowledge is enough to run tests | The highly qualified skill set is necessary |
With this, we come to an end of this article. I hope I have thrown some light on to your knowledge on Big Data and its Technologies.
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