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    Automate Big Data Testing with DataQ

    Automate Big Data Testing with DataQ

    Your business collects a massive amount of data every second. It enables you to make effective decisions. However, managing the enormous amount of data can be very challenging. It is challenging to maintain accuracy and reliability. By using DataQ, you can effectively overcome all the challenges. In this post, you will find all the details.

    What is big data testing?

    What is big data testing?

    Big data testing refers to examining and validating the functionality of big data applications. It enables you to identify and fix issues quickly. As a result, your big data application will run flawlessly. Also, it helps you to ensure the accuracy and integrity of the complex datasets effectively. So, you can derive the right insights.

    Why is big data testing necessary?

    Why is big data testing necessary?

    Big data is a collection of massive datasets. You cannot handle it with traditional databases like Oracle, MySQL, and SQL. They are not designed to handle the complexity of enormous datasets. Also, it will be inefficient and expensive to use traditional databases. They can’t handle the huge velocity of big data.

    The datasets are too complex. As a result, traditional sampling and manual testing methods will not work in big data. You need to find an alternate solution for testing the application. This is where big data testing comes into play. It allows you to eliminate data complexity. As a result, you can efficiently validate data integrity lying at different sources and channels.

    But most importantly, big data testing helps you to identify quality data. It can significantly improve your decision-making capability. As a result, you can generate more revenue from your business.

    How does big data testing work?

    How does big data testing work?

    Big data testing works by performing a variety of analyses and examinations. The whole process can be divided into two stages:

    • Functional testing
    • Non-functional testing

    Functional Testing

    Functional testing is performed by examining and validating the application's front end. It involves testing the complete workflow from Big Data Ingestion to Data Visualization.
    Functional testing is performed in three stages:

    Testing of Loading Data into HDFS

    Big data systems have structured, unstructured and semi-structured data collected from different sources. They are stored in HDFS. By performing the test, you can effectively check the accuracy and completeness of the data. Also, you can check if the data files are stored in the correct location.

    MapReduce Operations

    MapReduce tool is used to analyze the data stored in the HDFS. It performs two essential tasks - Map and Reduce. In Map, the mapper processes an individual set of data. It produces smaller multiple chunks of data in (key, value) pairs. In Reduce, the reducer processes the data produced by the mapper. It generates new aggregated sets of output in (key, value) pairs.
    In the stage of MapReduce Operations, you can effectively check the business logic on the nodes. Also, you can verify if the MapReduce process is generating the correct (key, value) pairs.

    ETL Process Validation and Report Testing

    This stage involves storing data of MapReduce operations in the Enterprise Data Warehouse (EDW). Here, you can generate the report. Also, you can analyze the data further to gain additional insights.
    In ETL Process Validation, you can compare the data stored in the EDW with the HDFS. It helps you to identify the corrupted data quickly. Also, you can check the accuracy of reports generated by the system. It ensures the inclusion of the right data and layout.

    Non-Functional Testing

    Non-functional testing enables you to analyze the performance of big data systems. Also, it helps you to identify the cause of failures in the application.
    Non-functional testing is performed in two different stages:

    Performance Testing

    Performance testing enables you to derive various performance metrics, including maximum processing capacity, and response time. It allows you to check the data processing speed of the MapReduce tasks. It helps you to identify the best configuration for optimizing the performance.

    Failover Testing

    Failover testing involves detecting failures in the Hadoop nodes. It ensures quick recovery of the big data application. The system can switch to other nodes to continue the task of processing the data.

    What is the best tool for big data testing?

    What is the best tool for big data testing?

    The best tool for big data testing is DataQ. It enables you to monitor the data quality effectively. It helps you to reduce release cycles and minimize losses. As a result, you can boost productivity and enhance revenue.

    Why should you use DataQ for big data testing?

    Why should you use DataQ for big data testing?

    • Efficiently monitor the data quality by analyzing factors like accuracy, distribution, schema, etc.
    • Reduce the release cycles and boost productivity.
    • Reduce downtime by improving data quality.
    • Perform cross-reference data validation across multiple data sources.
    • Minimize losses and increase ROI.
    • Easily define data quality rules by utilizing auto-suggestions.
    What tools does DataQ utilize for big data testing?

    What tools does DataQ utilize for big data testing?

    DataQ utilizes world-class testing tools to analyze big data applications efficiently. Let’s take a look at them.

    Hadoop Distribution File System (HDFS)

    Hadoop is an open-source processing framework that handles pools of big data. Hadoop Distribution File System (HDFS) is the primary data storage system used by the Hadoop applications. It can accommodate apps that have gigabytes or terabytes of datasets. It has been designed to detect the fault and make an auto-recovery quickly.


    Hortonworks is an open-source framework for distributed storage. Cloudera developed this technology, and it can efficiently process large and multi-source datasets. It enables you to conveniently gain insights from structured and unstructured data of the big data system.


    Cassandra is an open-source distributed database management system. It can efficiently handle large amounts of data across many commodity servers. It offers high availability with no single point of failure. As a result, Cassandra has become one of the most reliable platforms for handling critical data.

    Google BigQuery

    Google BigQuery is a server-less data warehouse. It allows you to make rapid SQL queries. Also, it enables you to perform scalable analysis of massive datasets. Google BigQuery offers a flexible, scalable, and multi-cloud analytics solution. It allows you to derive valuable insights from large datasets easily. As a result, you can quickly make effective business decisions.


    Snowflake is a cloud-based data warehouse. It is used for efficiently processing large-scale data. It enables you to easily discover and securely share live governed data across your business. As a result, users at all levels can make data-driven decisions.

    How does DataQ perform big data testing?

    How does DataQ perform big data testing?

    DataQ performs big data testing through a series of analyses and examinations. Let’s take a look at them.

    End-to-End Data Testing

    End-to-End testing validates the data from end to end of the big data applications. It helps you to prevent data corruption effectively.

    Data Quality Monitoring

    DataQ closely monitors the data to ensure quality. It enables you to derive insights accurately.

    Reports and Visualization Testing

    Reports and visualization testing ensures that the data is valid and error-free. It enables you to create highly accurate business reports.

    Data Extraction Testing

    Data extraction testing helps you validate data integrity. As a result, you can effectively analyze the reliability of datasets.

    Data Migration Testing

    DataQ tests and verifies migrated data in just a few minutes. You no longer have to worry about the data accuracy after migrating from Oracle to Hadoop.

    Data Transformation Testing

    Data transformation testing runs SQL queries for each row. It helps you to verify whether the data is correctly transformed or not according to business rules.

    Bad Data Extraction Testing

    Bad data extraction testing allows you to keep your data pipelines free from errors.

    Should I use DataQ for big data testing?

    Should I use DataQ for big data testing?

    DataQ's big data testing service offers end-to-end testing from data acquisition to analysis. It supports world-class tools to validate structured and unstructured data efficiently. It ensures superior data quality and accuracy. As a result, you can derive the right insights for making effective business decisions. So, you should consider using DataQ for big data testing. Request a demo to see how it works.