The 5 Most Popular Big Data Testing Tools [+FREE Bonus tool]

Most Popular Big Data Testing Tools
The global big data analytics market is forecasted to reach a whopping 68.09 billion U.S. dollars in annual revenue by 2025.

With the adoption of AI, IoT, and other futuristic technologies to constantly innovate, and the pandemic-fueled digital transformation of organizations worldwide, the demand for reliable big data applications is steadily on the rise.

However, quality assurance of these big data apps remains a constant bottleneck that delays app releases. For businesses to make the right decisions swiftly, it is important that they rely on precise insights. This can only be made possible by regularly validating the quality of big data and ensuring that the data is flawless.

But here’s the catch- traditional software testing techniques are NOT cut out for big data testing. With the colossal amount of data that these applications handle, it is necessary to carry out software testing using precise tools, well-thought-out strategies, and robust testing frameworks.

In short, big data apps need stringent quality checks using the proper testing tools to check for data integrity, app functionality, performance, and security.

Big Data Testing Tools and Their Features

There are a lot of big data testing tools in the market to help testers in their efforts. With a few differences in their features and capabilities, these tools are usually categorized depending upon the phase in which they are used. These include:
  • Data Ingestion tools: Sqoop, Zookeeper, Kafka
  • Data Processing tools: Pig, MapR, Hive
  • Data storage tools: Amazon S3, HDFS
  • Data Migration tools: CloverDX, Talend, Kettle

In this detailed post, we’ll discuss the tools mentioned above and highlight their most important features so that you can get a better idea.

Let’s dive right in:

1. SQOOP

Sqoop is a utility tool to move data back and forth between an RDBMS to HDFS. Sqoop stands for SQL to Hadoop.

Features and uses:

Scoop can both import and export data from structured data sources like relational databases, NoSQL systems, and enterprise data warehouses.

Using Scoop, you can also move data from an external system onto an HDFS and populate tables in Hive and Hbase.

It also integrates with data orchestration platforms like Oozie, allowing you to carry out scheduled automated imports and exports.

Scoop employs a connector-based architecture that can support connectivity plugins.

2. ZOOKEEPER:

Zookeeper is a pillar for many distributed applications. It is used by different nodes in a cluster for coordination and maintaining shared data in synchronization.

Features and uses:

  • Distributed configuration management
  • Self-selection/ consensus building
  • Coordination and locks
  • Key-value store

Zookeeper is used in many distributed systems such as Hadoop, Kafka, etc. It manages brokers for a Kafka cluster. Kafka 2. x is entirely dependent on Zookeeper to work. It helps in performing leader elections for partitions.

Zookeeper is known for its stability as it hasn’t had a good release in years after its 3.4 version.

3. KAFKA:

Kafka is a high throughput distributed messaging system for decoupling data streams and systems.

For simpler understanding, let us put it this way-

When there are too many source systems and target systems exchanging data with one another, things can become really complicated. The biggest problem with this type of architecture, is the high number of integrations you’d have to write, keeping in mind the protocols, data formats, and the data schema and evolution.
Most Popular Big Data Testing Tools
This puts a lot of load on source systems. Apache Kafka decouples your data streams so now your source systems will have their data in Apache Kafka, for the target systems to retrieve.

Features and uses:

  • Activity tracking
  • Ability to gather metrics from various locations
  • Ability to gather application logs
  • Decoupling system dependencies
  • Integration with other big data technologies

4. PIG

Apache offers the scripting platform Pig, which helps in processing and analyzing large data sets. Pig runs on Hadoop clusters.

The best part about Pig is that it is extensible, self-optimizing and easily programmable. Programmers can use it to write data transformations, without even knowing Java. It uses both structured and unstructured data for input, to perform analytics and uses the HDFS to store the results.

Features and uses:

  • Step by step procedural control
  • Ability to operate directly over files
  • Schemas can be assigned dynamically
  • Support to user defined functions and data types
  • Fully nestable data model

5. HIVE

Hive is essentially a data warehouse infrastructure software. It provides SQL intellect, allowing users to write SQL like queries to extract the data from Hadoop. The hive component interacts with the Hadoop ecosystem and the HDFS file system by converting HQL queries into MapReduce jobs.

Features and uses:

  • Can be used for online analytic processing.
  • Fast, scalable, and flexible.
  • Supports various file formats
  • Metadata is stored in RDBMS
  • Provides a lot of compression techniques
  • Specialized joins, UDF and HDF support

Bonus tool: QUERY SURGE

Query surge is a smart ETL data testing solution. If you are looking for a validation and testing tool for Big Data and data warehouses, Query surge is the tool to go for. It can also analyze and validate data intelligence reports.

QuerySurge automates the following for a data warehouse:
  1. Test design
  2. Management
  3. Scheduling
  4. Execution
  5. Analysis
  6. Reporting

Query surge supports various data stores both as source and target and provides complete DevOps functionality for continuous testing.

Features and uses:

  • Ability to test across platforms
  • Integrates with other ETL solutions
  • Validate data quality faster
  • Test any big data implementation
  • Increased data coverage

Conclusion

Inappropriate testing of big data applications is detrimental to businesses. Big data is instrumental in driving revenues by helping organizations understand and analyze market trends, consumer behavior, and overall demand. As a result, big data testing has a vital role to play in creating customer-centric products and services.

However, big data testing does not come without its fair share of challenges. For starters, it is highly complicated because of its enormous volumes, and no single tool can perform end-to-end testing. Big data testing deals with scripting and validating a high degree of test cases.

The process requires highly skilled testing professionals to customize solutions for critical testing areas. A software quality assurance services company such as Qualitest can help you ease into the deeper aspects of big data testing.
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