Big Data: What Is It?

In simple terms, a large volume of data which is structured or unstructured is known as big data. It may exist in different formats like images, videos, files, etc. The characteristics of bog data are volume, velocity, and variety. Volume is the size of data collected from different sources like transactions, sensors, etc. Velocity is the speed in which the data is being handled and the processing rates involved in handling them. Variety is the different formats of data which is collected in different sources. Examples of big data are E-commerce sites, social media sites, healthcare, etc.

Why Big Data Testing?

Every new software or app needs to be tested whether it is a new technology or any other stuff to enhance the quality and to fix the issues in it. Similarly, big data testing is implemented to ensure the functionality and performance that will benefit the enterprise. Big data testing services includes different kinds of testing like database Testing, Functional testing, Performance Testing, and Infrastructure Testing.

Big Data Testing Services for Enterprises

1. Testing Strategy

It includes a series of different testing strategies like:

• Data Ingestion Testing

At first, data is collected from different sources like social media, sensors, etc. and then stored into HDFS. It is done to verify that the data is extracted and loaded correctly or not. The tools used are Zookeeper, Sqoop, etc.

• Data Processing Testing

Here the emphasis is given on aggregated data and to process it. It is done to validate business logic. The tools used are Hadoop, Hive, etc.

• Data Storage Testing

Here the output is stored and tested the output data to load it into the warehouse correctly. The tools used are HDFS, HBase, etc.

• Data Migration Testing

It is needed when the data is moved to a different server, or there’s is any change in technology to move the data from the old system to new.

2. Functional Testing

This type of testing checks the complete workflow from ingested data to its visualization. It is performed by testing the front end application as per requirement. It is needed to validate the application results and to compare it with the expected results.

3. Performance Testing

Performance Testing is useful to avoid discrepancies in the software as Big Data applications require regular processing. It leads to the requirement of large computing sources. This testing is focused on some features like:

• Data Loading and Throughput

It emphasizes on the rate at which data is consumed from various resources and also the rate at which it is created.

• Data Processing Speed

This type of performance testing is done to check the speed of each component to check for the issues.

Also Read: A Comprehensive Guide To Software Performance Testing

Benefits of Big Data Testing For Enterprises

1. Data Accuracy.
2. Improved Quality Standards.
3. Enhanced Market Strategies.
4. Cost Reduction.
5. Enhanced Business Decisions.
6. Increasing Productivity.

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Just imagine you need to test 500 TB of unindexed and unstructured data which has does not have any attached cache. Is it scary to think right? In case you think that the challenges that you might face during the testing include slow process, unknown errors, faults during transmission and uncleaned data, you are nowhere close to the actual challenges that might come into the picture during the big data testing process.

Top Big Data Testing Challenges

The main challenges that you are likely to encounter while carrying out the big data testing have been captured here. This knowledge will help you to design a robust testing process so that the quality will not get compromised and you will be well-equipped to overcome these challenges.

1. The Huge Volume of Data

The huge volume of data that exists in Big Date is an extremely serious matter. Testing bulky and voluminous data is a challenge in itself. But this is just the tip of the iceberg. In the 21st century, most of the business undertakings need to store Petabyte or even Exabyte data that they have extracted from various offline and online sources.

The testers have to make sure that the data that is being tested and examined is of value for the business entity. The key issues that arise due to the high volume of data include storing the data carefully and preparing the test cases for the specific data that are not consistent. In addition to these hurdles, the full-volume testing of the massive data is next to impossible due to the size factor.

Also Read: Big Data and Analytics Testing: Learning the Basics

2. High Degree of Technical Expertise Requirements

If you think you will require an excel spreadsheet to test Big Data, you are living in a fairytale. As a Big Data tester, you need to have a proper understating of the Big Data ecosystem. You need to think beyond the usual parameters that are related to manual testing process and automated testing procedure.

If you are thinking to go with the automation testing process to make sure that no data is lost or promised, you are smart. This is because this machine-driven process makes ensure that all the range of probabilities is covered. But if you use this process, you will have to manage a large number of software which will make the testing process more complicated and complex.

3. High Costing and Stretched Deadlines

In case the Big Data testing procedure has not been properly standardized and strengthened for optimization and the re-utilization of the test case sets, there is a possibility that the test cycle will exceed the intended time-frame. This challenge could take a serious turn as the overall costs relating to testing will increase, and maintenance issues could arise as well.

The stretching of test cycles is not very uncommon when a tester has to handle and carefully check massive sets of data. As a Big Data tester, you need to make sure that the test cycles are accelerated. This is possible by focusing on robust infrastructure and using proper validation tools and techniques.

4. Future Developments

Big Data testing is quite different from the usual software evaluation process that one conducts from time to time. The Big Data testing is carried out so that new ways can be found to make some kind of meaning of the huge data volumes. The processes that are involved in testing big data must be carefully selected so that the ultimate data must make sense for the tester and the organization. The future development challenges arise in the case of big data testing as it focuses on the functionality aspect.

Also Read: Big Data Testing To Eliminate Data Complexities

5. Understanding the Data

To introduce and implement an effective big data testing model, it is necessary for the tester to have proper knowledge relating to volume, variety, velocity, and a value relating to the data. Such an understanding is of vital importance which acts as the real challenge for the tester who is carrying out the test process.

Understanding the data that has been captured in a huge quantity is something that is not easy. The tester might find it daunting to measure the testing efforts and strategic elements without having a proper idea about the data that has been captured. Similarly, it is paramount importance for a Big Data tester to get an idea about the business rules and the association that exists between the varying subsets of the data.

These are some of the common challenges that one might face while carrying out Big Data testing. It is necessary for a tester to carefully carry out the testing process so that the quality of the big data and the test results will not be compromised in any manner.

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Businesses today need to understand complex data in-depth to unlock its potential and stand out. Complex data can be a curse for all organizations working in the Information Technology industry. It can lead to potential problems in managing data effectively and can also hinder system performance.

So what can be an effective solution to manage complex data? Well, these data complexities can be reduced to a larger extent by adopting Big Data Testing. Big Data consists of a huge amount of both structured as well as unstructured data collected from some sources including mobile devices and social media.

What Is Big Data Testing? Why Is It Needed?

In layman terms, Big Data indicates huge data quantities. The data comes from different directions, with the velocity and volume being monstrous. The data is replaced at high speeds, and so the requirement of processing also tends to increase, especially if in case it comes from social media. However, data can come from some sources and in many different formats.

In a data repository, one can find images, text files, presentations, audio files, emails, databases, video files, and spreadsheets. The structure and format of the data can vary depending upon the necessities. The data that come from digital repositories, social media, and mobile devices are both structured and unstructured.

Also Read: Big Data and Analytics Testing: Learning the Basics

The structured data are easier to analyze, while the unstructured data such as emails, video, documents, and voice are hard to analyze. Moreover, the unstructured data consume more resources. Relational Database Management System (RDBMS) can be an effective solution for managing data. It can define and manage data using structured query language (SQL).

However, RDMS fails to handle things, when data volume is tremendous. And if at all an organization would try to manage such huge data volume using RDMS, the process involved in doing so, will indeed be very costly. Hence, RDMS is not much efficient in handling complex data, and new technologies such as big data testing are needed to serve the purpose.

Advantages of Big Data Testing

Big data testing helps a lot in handling and managing complex data. Some of the advantages of Big Data testing are enlisted below:

• It helps in making sure that the huge sets of data across various sources are accurately integrated for providing real-time information.
• It facilitates quality validation of the data deployments for preventing wrong decisions and corresponding actions.
• It aids in data alignment concerning the changing dynamics for taking predictive actions.
• It helps to leverage the correct insight from even the smallest data sources.
• It ensures data processing and data scalability across the various touch-points and layers.

Also Read: Big Data Testing – What benefits It can Offer To Your Enterprise?

Major Areas of Big Data Testing

There are three major characteristics of Big Data testing. These are often referred to as the 3V’s.

1. Volume

Volume is one of the most critical characteristics of Big Data testing. Here, the testers deal with the incomprehensive data options. An example, in this case, can be Facebook, which has a huge capacity for storing photos. Facebook is believed to be storing around 250 billion pictures, and this number will keep on growing in the days to come as people on this social media platform upload around 900 million pictures a day.

2. Velocity

Velocity is nothing but the measurement of the incoming data. Let us take the example of Facebook here once again. Think about the huge quantity of pictures it requires processing, storing, and retrieving.

3. Variety

There are different types of data including unstructured, semi-structured, and structured. You can have encrypted packages, images, sensor data, tweets, and many more. When the data is structured things are easy, but when the data is overflowing and unstructured, things need a lot more attention.

Big Data testing validates all the areas mentioned above and makes sure that data has been processed and is free of errors. It helps the testers to be confident about the health of the data. Big Data testing has indeed proved to be a boon to the businesses who earlier suffered from major data challenges, where the traditional data management systems never held good. Big Data testing has eliminated all these data challenges and made it easier for businesses to work with overflowing data. This new and advanced technology is evolving rapidly and will dominate the industry soon.

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Big Data and Analytics testing involve unstructured input data, data formats and types, processing and storage mechanisms as well as software and products that are completely new. This requires new abilities and skills, the building of new toolsets and instrumentations which are beyond the scope of traditional testing. Thus, confirming that highly concurrent systems do not have deadlock or race conditions, measuring the quality of data which is unstructured or generated through statistical processes or identifying tools that can be used pose great difficulties to testers. We discuss some of these points in this article.

What do you mean by Big Data and Analytics?

Big data Analytics can be understood through three ‘V’s- Variety, Velocity and Volume.

Variety-One of the common characteristics of big data systems is the diversity of source data, which typically involves types of data and structures which have a higher complexity and are less structured than traditional data sets. This data includes texts from social networks, videos, voice recordings, images, spreadsheets and other raw feeds.

Velocity-Huge inflows of data are brought into the system on daily basis. This extremely fast streaming of data could lead to few challenges as well- online frauds, hacking attempts etc. which makes the testing processes extremely important.

Volume-It is estimated that approximately 2.3 trillion gigabytes of data are generated on daily basis. The exponential rise in data is coupled with the Internet of Things (IoS) which allows the recording of raw, detailed data streaming through these devices. Under such circumstances, sequential processing becomes extremely tedious leading the organization vulnerable to threats.

Download : Manual & exploratory testing on 500+ real browsers and mobile devices.

Understanding the Basics of Big Data and Analytics Testing

With the ever-increasing nature of data- its analysis, storage, transfer, security, visualization and testing have always been an issue. In dealing with this huge amount of data and executing it on multiple nodes there is a higher risk of having bad data and even data quality issues may exist at every stage of the processing. Some of the issues faced are:

Increasing need for integration of huge amount of data available: With multiple sources of data available, it has become important to facilitate the integration of all data.

Instant Data Collection and Deployment issues: Data collection and live deployment are very important to meet the business needs of an organization. But challenges like proper data collection etc. can be overcome only by testing the application before live deployment.

Real-time scalability issues: Enormous Data Applications are assembled to coordinate the level of versatility included in a given situation. Basic mistakes in the structural components representing the configuration of Big Data Applications can prompt most exceedingly terrible circumstances.

There are also multiple challenges associated with testing of big data, including performance and scalability issues, meeting speed of data, understanding it and addressing data quality, node failure as well as problems related to continuous availability and security of data.

Tips and Techniques for Testing Big Data and Analytics

Tools and Softwares

Hadoop (a testing framework software allowing for the processing of large datasets) can be used to handle all issues related to bad data or data quality issues. Hadoop usually overcomes all traditional limitations of storage and computation of the huge volume of data as it supports any type of data and is open source as well. Being very cheap, simple and easy to use, it allows working on multiple cluster nodes simultaneously while offering linear scalability and thereby reducing scalability related issues.

NoSQL is another next-generation database management system that differs from relational databases in some significant ways and attempts to solve all Big Data challenges while supporting a less rigid and more anywhere and write-anywhere functionality. This allows data to be replicated to all other nodes and readily and quickly available to all accounts independent of their locations thereby increasing the performance of the data available. Cassandra is a NoSQL solution which can be suggested by a tester to accommodate large and complex requirements of Big Data workloads. It enables sub-second response time with linear scalability so as to increase throughput linearly with the increased number of nodes and to deliver very high speed to customers.

Techniques for big data and analytics Testing:-

Some testing approaches to solve performance issues are given below-

Parallelism: Without parallelism, the execution of a product framework is constrained to the velocity of a solitary CPU. An approach to accomplish parallelism is to intelligently segment application information.

Indexing: Indexing is another solution, which a Performance Engineer in Big Data Domain can consider to deal with performance related issues. Indexing refers to sorting a number of records on multiple fields so that an index is created on a field that creates another data structure which holds the field value and pointer to the record.

Whereas, scalability issues can be resolved using the following techniques :

Clustering techniques: In clustering data is distributed to all nodes of a cluster as it is loaded in. Large data files are split into chunks which are handled by different nodes in a cluster. Each part of the data is duplicated across several machines so that single machine failure may not lead to data unavailability. One example of a Clustered database used to handle Big Data is Hadoop.

Data Partitioning: In Data apportioning, the System regards CPU centres as individual individuals from a group and spots legitimate allotments of information at each CPU. Informing between allotments is preoccupied so it lives up to expectations the same path over the system or over the neighbourhood transport.

Conclusion

While big data testing may be the industry’s next big thing, testing teams may yet not fully comprehend the implications of big data’s impact on the configuration, design and operation of systems and databases. Hence, testers require a well-defined strategy to execute their tests, though there are many new unknowns as big data systems are layered on top of enterprise systems struggling with data quality. The above techniques may assist software testers to cope up with new challenges.

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Since the last couple of years, everybody has seen a remarkable progression of software development.  The systems and techniques of software testing, as well as test management tools, are in a consistent move.

The system software applications are getting disturbed by the rising digital transformation as it is opening up the requirement for building better software testing abilities. The models, techniques, as well as tools for testing, have developed with the transforming software testing requirements. For example, according to industry analysis as well as reports, DevOps and Agile testing have been the leading drivers for development in the product testing market. There is a never-ending requirement to instigate applications at pace and maintain quality in the meantime.

Nowadays, business sector demands more proficient as well as powerful security program for their created IT frameworks than that of software companies. The existing circumstance of testing and software development is getting all the more difficult with consistently passing.

With the regularly expanding requirement for QA and without bug products, the software testing industry is prepared to think of more logical trends in the coming year. It is normal that any new trends in software and application security cycle are going to profoundly change the work process of testers and developers.

Prediction About 2018

In 2018, each enterprise owner or a business person will lean toward the products based on the lines of the most recent trends and approaches in software testing. Seeing this necessity, the software testing market is choosing the best most effective 5 techniques that will be on the run through in the coming year. Subsequent to these newest software trends in functionality, compatibility, performance, usability, and security testing will enable you to remain ahead in the game.

List of Top 5 Testing Trends that will Rule in 2018

1. The ascent of Agile Development and DevSecOps

   The progressing year will be all with reference to open source tools for Agile Development and DevOps security. The blend of DevSecOps and Agile methodologies will turn into a definitive source to accomplish flexible security forms at any point constructed. Additionally, these tools will advance more cross-sectional teamwork. Developers along with testers will get the greater part of their assistance from such tools that will help them in keeping up a smooth work process all through.Agile methodologies will keep on helping IT teams build some strong values for software development as well as testing processes, whilst DevSecOps will act as a watchdog over all security concerns.In the realm of increasing shopping, digital banking, and on the spot communication, one can’t just disregard the significance of security from the eternity happening cyber violations. You are sure that no gigantic level partnership is sufficiently arranged to endure any sort of cyber-attack. So, be prepared to profit by the energizing merger of QA featuring Agile and DevSecOps.

2. Internet of Things (IoT) Testing Will Nurture

   In 2018, the requirement for IoT Testing will rise amazingly. The universal use of smartphones, tablets, smartwatches,  and other related items is showing that the requirement for IoT Testing will come like never before some time recently. There is presumably that IoT has turned out to be a standout amongst the most effective software monitoring processes and later on, it will gain momentous advancement in increasing the security and execution of each tech device released in 2018.

3. Performance Testing to Performance Engineering

   Genuinely, each IT organization concurs with the way that an excellent user encounter helps in making an effective end-product. The manner companies carry out tests and monitors test cases and other documentation indicates the scope of a screens test cases and other documentation shows the extent of a particular product in the market. For the most part, the IT specialists roll out improvements in their product testing procedures to bring the best comprehensible experiences. 2018 will spot an awesome move in these abilities. QA testers will relocate from the old techniques and move from Performance Testing to Performance Engineering.

4. Big Data will Develop More
   

   There is no halting the regularly developing advancements like Big Data. The tech experts ought to prepare themselves as indicated by the upcoming developments in Big Data progressions to adjust their testing abilities as needs are. With the extending datasets and component information, the requirement for ideal analytical evaluation is getting to be plainly exponential for QA experts.In 2018, testers should upgrade their techniques for checking the progressing Big Datasets on the grounds that without figuring out how to adapt to these usual trends, a Testing Engineer will lapse in the cut-throat year to come.

5. Open Source Tools Will Take Place
   

   Prepare on the grounds that open source tools will assume control over the forthcoming year, 2018.  You will see highest IT firms getting the help of open source tools in the execution of DevOps, Agile Methodologies, Testing, and QA, Cloud Technologies, BigData, and Visualizations. As of now, Samba, Ubuntu, Linux Kernal, and so forth are the great open source tools in the market. In any case, in future, you will view a string of new open source tools to overwhelm the forthcoming year of developments.

Conclusion

Consistently, the software testing industry gets the chance to see some new changes and this year is no special case. In spite of the fact that the updates are great as far as execution and security, it tosses challenges in transit for testers, convincing them to continue learning. The main drawback is they may think that its harder to pick the best answers for their requirements.

Therefore, to all the valued testers, it about time to get ready and step ahead to learn most up to date happenings holding up to change the world of QA, development, as well as security testing.

Connect with KiwiQA to leverage focused capability for quality assurance testing services.

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Today, data has become the most significant asset for any organization and thus, it’s very difficult for the companies and firms to survive viably without the data and right data analysis techniques. Big Data, which is collected from multiple sources is capable of revealing valuable information, and that’s why, every organization is keen to deploy the right techniques to collect, store, analyse and test Big Data.

With the emergence of the cloud platforms, social media and mobile tools, the data that organizations can collect is too voluminous. Being a colossal in size and unstructured in nature, its difficult to carry out manual testing on Big Data.

The Necessity for Big Data Testing

High-quality analysis of data has become one of the top-notch priorities of business organizations, it’s important that the big data being used by them to derive resourceful insights are well tested and validated. If the big data is not tested for quality, it may not be able to deliver the information that can aid an organization and support decision making. That’s why proper testing of big data is mandatory.

Big Data Testing

Big Data testing is completely different from the other methodologies of testing, which are used for validating the software and applications. The primary goals of Big Data testing include:

• Verifying the completeness of data
• Ensuring the data quality
• Ensuring data transformation
• Automation of regression testing

In order for a tool to be efficient in Big Data testing, it must be able to determine which data is bad and must be able to provide a holistic view of the health of the entire data. It must also be able to ensure that the data which has been extracted from various sources remains intact in the target.

Benefits of Big Data Testing

Following are the four biggest benefits of leveraging the Big Data testing for your data:

Accuracy of Data

A large percentage of the data collected under the tag of “Big Data” is unstructured because it comes from machines and many other such sources. High-quality analysis of this data can facilitate intelligent insights which are usually very difficult to glean from the conventional BI tools and data warehousing facilities. Only, high-end analysis methodologies can be applied to such data for deriving resourceful insights, and if this data is not accurate. All the efforts would go in vain. The Big Data testing helps in validating the accuracy of data and aids to evaluate that whether it would be beneficial to go ahead with the data analysis or not.

Minimal Losses and Augmentation of Revenue

As per Gartner, every year businesses lose up to $100 million globally because of the poor quality of data. The high-end Big Data testing methodologies help in evaluating the quality of data and validate that whether it would be wise to go ahead with the analysis of the data. These testing tools help in differentiating the valuable data from the heap of the useless Big Data (structured, semi-structured and unstructured), thereby, minimizing the losses.

Insightful Business Decisions

With Big Data testing, one can differentiate between useful and useless Big Data. While useful Big Data can enhance the effectiveness of the decision-making process and help the managers make better decisions, the useless Big Data can impact the decisions and lead to losses. That’s why, implementation of the right, robust and immaculate Big Data testing strategies is very important.

Improved Business Strategizing

These days, every business organization, whether small or big is keen to gain the benefits which Big Data can offer, and with the advancement of the technologies, it has become much easier for businesses to collect huge amounts of data from multiple online and offline sources. This data can be leveraged to deliver a personalised experience to the customers, deploy predictive behavioural targeting and enhance their loyalty. But, to achieve this, it’s imperative that the Big Data which a business is planning to use is well tested.

These benefits certainly accentuate the fact that today, the reliable Big Data testing services are among the top requirements of every firm.

Connect with KiwiQA to leverage focussed capability for Big data testing services.

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