The rise of big data testing

According to IDC, the big data market is expected to be a $50 billion industry by 2019. With the ever-increasing number of big data applications in the world, the demand for big data testers is now more critical than ever.

Hence, we have asked experts in the industry to share their knowledge on big data testing and its rising importance.

 

What is big data testing?

Big data testing is the process of testing data for data and processing integrity; and to validate the quality of big data and ensure data is flawless to help business derive the right insights, according to Hitesh Khodani, Test Transformation Manager.

Marco Scalzo, Big Data Engineer at AgileLab, adds that when we talk about big data, we refer to a large collection of data, which is potentially unbounded if we are in a real-time streaming model, on which we execute a pipeline of operations in order to process data and store them in an external sink such as a database.

For this reason, the traditional software testing techniques are not suitable for big data testing. Indeed, the real-time data processing tests need the big-data application to be running in real-time mode to execute the test processes. The application is run using real-time processing tools like Spark and Spark Structured Streaming, which allows you to manage the stream of data in real-time. Hence, different testing types like functional and nonfunctional testing are required to ensure that the data coming from different sources are processed without errors. This way, it helps to achieve a good quality of the data on which to perform analysis in the next steps of the data processing pipeline.

For example, the first functional test is data ingestion testing. The input data are collected from various sources such as CSV files, sensors logs, social media and then stored into HDFS. In this kind of test, the primary goal is to check if the data are correctly extracted and accurately loaded into HDFS or not. A relevant requirement of this test is that data need to be properly ingested according to the defined schema and also that there is no data inconsistency or corruption. A common approach to validate the correctness of data is to compare the source data with the ingested data.

Another important test is the data processing test, which is very important as its primary focus is moved on the aggregated data. As the ingested data are processed, the check on the correctness of the business logic is performed comparing the aggregated data, produced as an output by the data processing pipeline, with the expected data. In the end, if the pipeline ends with the storage of these aggregated data into an external sink, it’s a good practice to first retrieve the data from the chosen sink and then compare them with the expected data.

One of Big Data’s main characteristics is speed, Marco continues, it is then also necessary to do some performance tests. For instance, to determine the performance metrics and to detect errors, the Hadoop performance monitoring tool can be used. There are fixed parameters like capacity, operating time, and other metrics like memory usage within performance tests. So, the purposes of performance testing are not only intended to acknowledge the application performance scores but to improve its performance. Through the test execution and the analysis of the results, the performance bottlenecks can be found and analyzed further.

The performance testing allows verifying how fast the system can consume data from different sources. It inspects how quickly data can be inserted into the underlying data store, for instance, by calculating the insertion rate for databases like MongoDB or Cassandra. Thus, in order to understand and validate the data processing pipeline, performance tests are needed. They are indeed helpful in verifying the speed with which the spark jobs are executed inside a spark application.

Moreover, there is the failover test. It is useful to replicate the case in which a single node in a cluster, called “worker” in a Spark application, fails. Then, being the failover test a performance testing technique, it comes in handy to inspect whether and how the system is able to re-allocate and re-distribute the resources of the failed worker across the cluster. This makes the execution of the pipeline resilient and fault-tolerant. Besides, performance testing ensures that the system can handle unexpected peak demand dynamically adding a new node in a cluster and maintaining a high level of performance.

With performance testing, we can obtain the actual performance of big data applications such as the response time or the maximum processing capacity. Besides, we can get both the performance and resource status of the cluster which can lead to an optimization of the performance parameters of big data applications. Finally, it is possible to discover performance limits and identify the conditions that may cause performance issues.

 

Why do we need big data testing?

Hitesh points out that big data is integral to a company’s decision-making strategy, hence inappropriate testing of big data systems will adversely impact business.

There has been an exponential increase in data generated with the increased usage of mobile devices, social media, digital repositories, IoT equipment, and devices. Big Data helps in analyzing consumer behavior and enables organizations to understand the market trends and demand so that they can modify their products, services, and offerings to meet expectations.

Therefore, he underlines, Big Data is very important for businesses and organizations; the only clever way to ensure the correct processing and assessment of data is through the implementation of Big Data testing.

Moreover, this pandemic has forced all the governments to make decisions in a timely manner, which has led to the need to define a set of metrics useful for monitoring and tracking, in a real-time way, the spread of the virus. Due to the necessity to define those metrics, Marco emphasizes, the big data testing approaches have proven to be very useful for the implementations of tools and applications whose purpose is to offer sufficient knowledge in order to drive governments in the decision-making process.

He notes that the only way to achieve valuable insight is to the first store, then clean, and finally analyze the huge volume of data that are produced every day by ourselves. In most cases, data are fed into multiple artificial intelligence systems in order to train their model improving predictions and, for instance, helping to track and predict the behavior and the mutations of the virus. It’s easy to notice that the use of appropriate strategies of big data testing will reduce the time to market because it leads to testing the entire data processing pipeline of complex big data applications, monitor each step, easily scale and go into production in a fast way.

Another important thing is that AI needs data to learn and evolve, and data requires AI-driven analytics to have the possibility to offer real value. Because of that, it is safe to say that AI and big data are co-dependent.

Marco continues by stating that every day a lot of data are created, and it is estimated that by the end of this year, nearly two megabytes of data will be created every second per person. So, in order to improve and change the world with powerful decisions, it is crucial to have continuous and consistent availability of clean and reliable data, which can be used for making advanced analyses to reveal critical and valuable insights. As data without the insights are more or less invaluable to organizations, enterprises need to define processes for continuous, reliable, and secure data collection, analysis, and deployment.

By performing rigorous pipelines of big data testing, enterprises can ensure the accuracy and reliability of the data gathered and processed, and it can be integrated into their business decision-making processes.

 

Implementing a big data strategy

“Data is the new oil”! The concept behind this is that like oil, raw data are not valuable by themselves, but the value is created when they are processed completely and accurately and this is done in a timely manner.

Indeed, as Marco points out, big data analysis can improve the decision-making process or, even better, automate the whole decision-making process. This is the reason why using Big Data has been crucial for many leading companies to outperform the competition. In fact, many industries use data-driven strategies to compete with other companies and to innovate themselves.

According to the International Institute for Analytics, companies that are using data will see a lot of productivity benefits over competitors who are not using data to improve or automate the decision-making process. The adoption of a big data strategy brings various benefits. An example of this is that it allows a business organization to profile customers in a more accurate manner, allowing a business to understand the customers’ needs and to engage, in a real-time scenario, a one-to-one conversation with them.

Furthermore, the customers will always be treated how they want to. Big Data is the best way to collect feedback that helps companies to understand how customers perceive their services and products. In such a way it is possible to make the necessary changes and re-develop the products if needed.

Regarding the strategy, in this period we are attesting a shift of the big data strategy, from a monolith architecture, also known as a Data Lake architecture, towards a Data Mesh architecture. A Data Lake is a storage repository that can store a large amount of structured, semi-structured, and unstructured data. It is a place where it’s possible to store every type of data in its native format, collecting a huge quantity of data and making it accessible for everyone who wants to perform analysis on them. It democratizes data and it is a cost-effective way to store all data of an organization and analyze them later.

This current approach with a centrally managed data lake integrates all data sources, transforms data, and exposes them to all the consumers. But this does not scale well. As the companies grow and become more data-driven, the possibility that the data team will end up in a bottleneck is really high. This can happen because the traditional separation between the IT department and the rest of the business could be the biggest problem.

In fact, the successful deployment of a Data Lake depends on the solid collaboration between data scientists and data engineers. The data scientists must use the tools provided by the data engineers that have to understand and use what is implemented by the data scientists. This often leads to difficulties in aligning priorities, heavy upfront design, communication problems, and longer time-to-value.

Data Mesh is a new concept that has become one of the fastest-growing trends during 2020. It represents an alternative to the centralized architectural pattern of the data lake. It proposes a new distributed and decentralized architecture designed to help enterprises to achieve agility and business scalability, reducing the time-to-market of the business initiatives. This paradigm draws on the paradigm shift that was introduced by the microservices architectures and applies it to data architectures. It offers the possibility to empower agile and scalable analytics.

The Data Mesh idea also increases the data scalability by moving towards a distributed-domain-oriented data platform and start to consider data as a product. The Data Mesh enables autonomy and abstract technical complexity by a self-serve data infrastructure. In this distributed setup the data team will transfer ownership of the data to the domain teams. Both the teams cooperate together becoming a platform team that focuses on enabling data producers and consumers to work efficiently with data.

According to these assumptions, the data is seen as a shared, discoverable and self-describing product. It should be considered as a building block of the data mesh architecture. In this way, on one hand, the domain teams can create and consume data products autonomously using the platform abstractions, hiding the complexity of the building, executing, and maintaining data products.

On the other hand, the data users can easily discover, understand, and use high-quality data with a beautiful experience. It is also important to note that the data are distributed across many domains. This leads to the creation of federated computational governance, in which the users can get value from aggregation and correlation of independent data products to obtain the value at the right granularity. The mesh is behaving as an ecosystem following global interoperability standards, that are baked computationally into the platform.

Hitesh adds that businesses should implement big data strategy as well-defined and detailed Big Data strategy sets the foundation for the organization to deploy data-related or data-dependent capabilities. Indeed, it helps to set out the path for an organization to follow in order to become a “Data-Driven Enterprise”.

An effective data strategy should link to an organization’s strategic goal, things to be considered while developing a solid big data strategy:

  • Data Requirements: types of data required, source and target, etc.
  • Data Governance: data quality, ownership, access, and security.
  • Technology stack: the right level of infrastructure to collect, store, process data, and generating insights.
  • People Skills: Right skilled resources, building teams with diverse and complementary skill sets and multiple levels of experience.
  • Collaboration: Business and IT teams working towards the same goal.

 

The impact of the pandemic on big data testing

According to Hitesh, the pandemic increased the need for big data testing due to the following reasons:

  • Due to lockdown across the world, people’s buying patterns changed and there was a huge surge in online shopping on e-commerce sites and other online platforms. This led to an increase in the amount of data generated.
  • Increase in Social Media usage: Sites like Twitter, FB, Instagram, TikTok, Snap, etc. saw increased traffic and led to a huge upsurge in data generation.
  • Many governments rolled out Covid alert applications with the aim to provide insights.
  • Other applications rolled out to generate insights to manage Covid: Hotspot neighborhoods, Vaccinations centers, # of vaccines administered, # of active cases, etc.

Big data testing thus plays a critical role in all the above scenarios to ensure the right level of insights are generated to enable organizations, governments, and people to make informed decisions.

Furthermore, Marco continues, many countries are dealing with big data, machine learning, and other digital tools to track, control and forecast the future of this pandemic.

Indeed, many governments have been forced to make a strong relationship with science and lean towards data‐driven decisions to fight against the challenges caused by the coronavirus. The data are being used to track the outbreak of the virus in the world and to create innovation in the medical field helping the research and the development of new treatment procedures. The testing of a large amount of data can diversify manufacturing, enhance vaccine development on more profound means, and can lead to building knowledge that will be useful for other similar cases.

Big data management helps to forecast the impact of the virus in a particular area and how it can be blocked promptly. Similarly, Big data can also provide possible sources and opportunities for people to help them in handling stressful situations. China defeated the coronavirus with the help of data and AI, leading towards a low rate of spread.

Since Big Data is an asset that helps to better forecast and understand the reach and impact of coronavirus, Marco states, medical professionals and researchers are now heavily relying on data to have accessibility to accurate and precise real-time data that allows making a set of actionable insights. This is coming at the right time since the Big Data tools were not available during previous pandemics. Certainly, following the approach of Big Data testing, a lot of advantages can be obtained in this field. For instance, it allows faster development of medical treatments, it is useful to assist in the development of new medicines and equipment needed for current and future medicinal needs.

Since the pandemic started, expressions like “social distancing”, “lockdowns” and “flattening the curve” became common and used every day in our lives. But how can Big Data testing help in this situation? It is resourceful to analyze and then discover the metrics such as population movements across regions, checking public compliance in following the health protocols, setting the limits beyond which a country should follow the lockdown protocols as well as identify which activities could be done safely.

All these metrics help in predicting how the curve will grow or flatten in the next weeks. Hence, it assists governments in planning immediate actions to take swift action to curb the pandemic spread.

 

The benefits of big data testing…

When data is in the hands of the right people in an organization, it can become an asset. Marco thus underlines the importance of organizing them and extracting the implicit value that may be hiding within data. The importance of Big Data testing is primarily to help to drop data complexities through the validation of the quality and integrity of the data. Consequentially the first big advantage of big data testing is that it lessens the threat to data quality. However, more rigorous testing methods can save data from becoming degradable and redundant.

The big data testing techniques and quality analysis of data will offer valuable data insights that are usually difficult to obtain with data warehousing facilities and other traditional business intelligence tools. Having accurate data will help companies in order to analyze their business competition and pay attention to their weaknesses to strengthen their power.

For these reasons, it has become of vital importance to use big data by large organizations in order to acquire verified and valuable insights. Regarding the streaming side, big data testing is useful to validate real-time data. For these kinds of applications, which deal with live data, some sort of filtration and analysis is required to ensure the data obtained are valid and of good quality.

Big data testing also allows us to quickly scale data sets. It is known that every application starts with small data sets and gradually shifts to larger ones. Applications based on smaller data sets work great, but in most cases, the results are affected by the different sizes of data sets and the application will fail. To avoid these problems, it’s highly recommended to add a testing process as an integral part of their application lifecycle to ensure the performance does not get influenced by the dimension of datasets. Moreover, it’s easy to see that the use of these kinds of tests allows reducing the time to market, making the application ready for release.

Another relevant benefit, Marco adds, is related to the fact that the downtime is slashed. Big data applications are strictly depending on underlying data in order to obtain good quality results, then bad data could hinder the performance and capabilities of the applications. In some cases, organizations have no way to analyze the correctness and the health of data that results in downtime. The attitude of testing every step of a given data processing pipeline can improve business decisions, it can support a company in better decision making or automate the entire decision-making process. Big data testing can then drive businesses to build an optimized targeting system that will improve all business decisions. It is known that analysis of Big Data helps in better decision-making more than 50% of the time.

For Hitesh, Big Data testing ensures the data is qualitative, accurate, and reliable. There are numerous benefits to big data testing:

  • Improved decision-making: With the right kind of data at hand, it helps organizations make sound decisions, analyze risks, and make use of only the data that will contribute to the decision-making process.
  • Increased data accuracy: With the right kind of data, organizations can focus on their weak areas and be better prepared to beat the competition.
  • A better strategy: big data testing helps organizations optimize business strategies by looking at the information.
  • Increased revenue: If data is correctly analyzed, no mistakes are made when dealing with customers and help organizations increase their market share.

Enterprises need to be competitive in the Big Data strategy, Marco emphasizes. Testing should be a mandatory activity before any analysis and any kind of decision. The data processing will help the enterprises to access the correct data which aids in improving the return of investment and always be ahead of competitors.

Therefore, big data testing helps to minimize losses by differentiating valuable data from the heap of structured and unstructured data. They will surely help businesses to improve their customer service, make better business decisions, and increase their revenues.

 

… and the drawbacks

Big Data offers a lot of benefits, but they also come with their own set of issues.

Indeed, Marco shows that there are lots of new emerging sets of complex technologies, although they are still in the early stages of evolution. For these reasons, some of the common issues are related to poor knowledge about the technologies involved and inadequate analytical capabilities of companies. A lot of companies are dealing with a lack of skills for working with Big Data technologies. Since not many people are actually skilled enough to work with Big Data technologies, it becomes the bigger problem. Surely, companies are facing some major challenges when trying to implement their big data strategies.

Testing a huge volume of data is also very challenging. One of the possible solutions lies in the automation of big data tests because automation is essential to every big data testing strategy. In fact, data automation tools are designed to review the validity of this large volume of data. But making automation testing requires a team with strong technical expertise. The automated tools are not able to handle unexpected problems that can arise during the test’s execution. Since there is a lack of standard methodologies for Big Data testing so far, the time of the project is deeply tied to the expertise of the team. This might lead to missing a deadline which means an increase in costs.

A way to cut costs is to create sprints of testing. This approach is also related to architecture because storage and memory can significantly increase costs. When working with Big Data it is recommended to implement Agile methodology and to follow the Scrum framework. In this way costs are always kept under control and requirements can be scaled to fit the budget dynamically.

Another important challenge, Marco continues, is related to the high scalability the application will be capable to handle. In fact, a significant increase in workload volume can dramatically impact the data processing and networking for big data applications. Even though big data applications are designed to be able to handle large amounts of data, they may not handle immense workload demands. To work with this enormous volume of information the solution can be to make use of a cluster with different machines that guarantee parallelism of the operations and resilience to the failures. The distribution of data among the nodes of a cluster allows us to handle data in parallel, which gives us the ability to scale quickly and rapidly.

The data testing methods should equally distribute the large amounts of data in a cluster among all the nodes. This then allows the data replication within the cluster which reduces the machine dependency and ensures the resilience to the failures.

Moreover, Hitesh set out a list of challenges that can impede big data testing:

  • Data Completeness: Data pulled from various sources making it difficult to ensure complete data is sourced.
  • Data Quality: Ensuring data quality can be a task.
  • Test Environment: Creating an effective test environment is challenging.
  • Test Data Management: It’s not easy to manage test data when it’s not understood by the testing team.
  • Skillset: highly skilled and experienced resources are required, which can be difficult to find.

Hence, every company should meet the speed of data and understand it. It is known that figuring out what they mean and extracting the value which is hidden inside them will be a big challenge. But if the analyzed data quality is accurate the decision-making capabilities of an organization will be surely improved.

 

The future of big data testing

Hitesh believes that with the ever-increasing usage of mobile devices, social media, IoT adoption, the importance and need for big data testing is only going to increase many folds in coming years.

There will be more demand to test big data in a variety of industries and hence the demand for skilled big data testers will increase. All said and done, general testing and troubleshooting during the development of any product—including big data —follows a similar pattern and hence big data testing will then be a key focus area for all the quality-focused organizations.

Marco thinks that Big Data will continue to play an important role in many different industries around the world. It is now certain that with proper management and analysis of Big Data, every business will be more productive and efficient

Since data is becoming the most significant asset for any company, this pushes them to embrace this new approach, otherwise, it can be very difficult to survive without data and the right data analysis techniques. This is why every organization is willing to deploy the right techniques in order to collect, store, analyze, and test big data. They need to be able to juggle through large amounts of data, find patterns and draw the right conclusions in order to make better decisions, improve society and drive our economy forward.

When a business is ready to collect and store the correct information, in most cases it is useful for breaking down a wide range of dangers. Once that information is incorporated into the decision-making process, it turns into an incredible guide for making quality choices. But it is clear that the precision and the quality of the obtained data is the backbone for all critical business choices.

Consequently, high accuracy of data can lead organizations to be in the right place at the right time. The information must be as accurate as possible because, if not, there aren’t chances to extract knowledge and make it accessible at the proper time. Applications that experience load testing with various volumes and assortments of data can rapidly process a lot of information and make the data accessible when required.

Another significant challenge may be found in the automation of testing. It is clear that artificial intelligence brings in the processing power, in terms of speed and scale, in a way that the human is not able to do. As more and more organizations are embracing the Agile & DevOps development approach, intelligent automation of testing becomes the main factor to ensure high-quality products, reduce the time to market, and the cost overheads.

For all of this, we can definitely conclude that big data testing is extremely crucial for businesses to make strategic and accurate decisions based on the high quality of data available. For every company, what is necessary is to have the right and thoughtful testing strategy to get the maximum benefits of big data testing.

 

Special thanks to Hitesh Khodani and Marco Scalzo for their insights!

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