Big Data Testing: How to Overcome Quality Challenges

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Big Data Testing: How to Overcome Quality Challenges

The adoption of big data technologies isn't going to slow down. It's important to note how we can overcome the challenges that come with this technology.

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Big data has gone beyond the realms of merely being a buzzword. It is now vastly adopted by both small companies and corporations. Even so, many companies are still grappling with the huge plethora of information that’s coming their way.

Forrester researchers have come up with the eye-opening statistic that big data technology was adopted by 40% of firms in 2016, and more companies (prediction says 30%) will hop on the bandwagon in the next 12 months.

New data technologies are entering the market, but the old ones are also going strong. However, it is important to note here that the adoption of big data technologies will never slow down, at least not anytime in the near future. So, it is important to note how we can overcome the challenges that come with this technology. This article sheds more light on this aspect.

With the huge onslaught of data that’s coming in, businesses have tried different methods to handle them all. Conventional database systems have been replaced with a horizontal database, columnar designs, and cloud-enabled schemas. Even so, the role of quality analysis is still teetering on toddler's legs because in order to big data applications, you need a specific mindset, skills, and knowledge, followed by a knowledge of data science.

What Is Big Data Testing and Why Do We Need it?

As per Gartner:

“Big data is high-volume, high-velocity, and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation."

In simple words, big data refers to huge quantities of data. There is no particular size parameter to define the size of this technology. It is safe to assume that the standard way to measure it comes in terabytes or petabytes. Data comes in from all directions, and the volume and velocity would be monstrous.

Data gets replaced at a rapid pace and hence, the need for processing becomes higher, especially when it is related to social media feeds. But that is not the only medium through which information comes in. It could come from a variety of sources and formats. If you check a data repository, you can see text files, images, audio files, presentations, video files, spreadsheets, databases, and email messages. Depending on the requirement, the format and structure of the data could vary.

The data collected from mobile devices, digital repositories, and social media can be unstructured or structured. Structured data, of course, is very easy to analyze. Unstructured data like voice, emails, video, and documents are difficult to analyze and take up a lot of resources.

One of the most reliable solutions for a database management system is a relational database management system (RDBMS), the leading players in the solution being Microsoft, IBM, and Oracle. An RDBMS uses structured query language (SQL) to manage the data, define, query, and update it.

Still, when the data size is tremendous, RDBMS finds it difficult to handle — and even if it does handle it, the process will is expensive. This proves that relational databases do not have the ability to handle big data, and new technologies are required. Traditional databases are perfect for structured data but not for unstructured data.

Big Data Characteristics and Data Formats

The wave of information that keeps coming in is characterized by the 3 Vs:

  1. Volume
  2. Variety
  3. Velocity


Volume is perhaps the most relevant V with regards to big data. Here, you deal with incomprehensible options for data. An example would be Facebook, with its immense capacity to store photos. It is believed that Facebook is storing more than 250 billion images (as of 2015 records), and the numbers will only keep growing. 250 billion images a day is just a drop in the bucket. Considering the fact that people upload more than 900 million photos a day, the numbers are staggering.

And on YouTube, about 300 hours worth of videos are uploaded since almost everybody has a smartphone these days. Vendors have begun to manage their app data in the cloud, and SaaS app vendors have huge amounts of data to store.


Velocity is all about the measure of data that’s coming in. Take the example of Facebook. Look at the number of photos it has to process, store, and then retrieve. In the initial stages of data acquiring, companies used to do batch processing of the information that’s coming in.

The data is fed into the server and then it waits for the result. However, this can work only when the incoming data is slow — and with the speed at which data can be acquired now, it is possible to have a waiting period, however short that may be. Information keeps coming in from all directions in real-time, and you need to make them coherent enough to analyze and draw conclusions.


There are all kinds of data: structured, unstructured, and semi-structured. You will have photographs, encrypted packets, tweets, sensor data, and plenty more of this kind. Data is not something given to you in a spreadsheet anymore. When data comes in a structured format, things become easier, but when they come in the form of photos, videos, audio recordings, books, geospatial data, presentations, email messages, posts, comments, documents, tweets, and ECG strips... they're unstructured and overflowing.

Finding a pattern from this insane flow of data is not easy, and the process to make them coherent is known as data analytics. All of the data collected requires particular technologies and analytic methods to get a clear picture of what they indicate, making the information valuable.

As explained earlier, data is something that provides information and this is used for analysis and drawing conclusions. Data comes in different sizes, formats, etc. — hence, the three different categories:

  1. Structured
  2. Unstructured
  3. Semi-structured

Structured Data

Structured data can be easily utilized because they have a definite structure and are well organized. An example would be a spreadsheet where information is available in a tabulated form. Identifying patterns and extracting useful and actionable insights would be easier. The data is also stored in a relational DB and pulled easily.

Understandably, structured data is the most processed of all information and can be managed easily. They have a relational key and can be easily mapped into predesigned fields.

Unstructured Data

This refers to huge amounts of data stored in no particular pattern. But to gauge an understandable pattern from it, you need the help of sophisticated tools and technologies. Examples would be videos, web pages, emails, PowerPoint presentations, location coordinates, or streaming data. This lack of structure makes it difficult to manage the data from a relational DB.

80% of the data that’s found online is unstructured. However, these kinds of data do have some sort of internal structure, but they don’t neatly fit in any database. Exploiting unstructured data to its full advantage would help you make critical business decisions.

Semi-Structured Data

This kind of data is not rigidly organized and can be utilized after a little bit of sifting, processing, and conversion. Softwares like Apache Hadoop are used for this. However, these are not stored in the relational DB. In fact, semi-structured data can be called structured data that is available in an unorganized manner.

Examples of such kinds of information come in the form of web data such JSON (JavaScript Object Notation) files, tab-delimited text files, CSV files, BibTex files, XML, and other markup languages. Having semi-structured data makes it easier to ease up space, clarify, and compute data. They come with organizational properties that make analysis easy.

Big Data Application Testing: Key Components

As big data is described through the above-mentioned three Vs, you need to know how to process all this data through its various formats at high speed. This processing can be split into three basic components. To be successful, testers will have to be aware of these components.

  1. Data validation: Understandably, this is one of the most important components of data collection. To ensure that the data is accurate and not corrupted, it is important that it is validated. For this purpose, the sources will be checked. The information procured is validated against actual business requirements. The initial data will be fed into Hadoop Distributed File System (HDFS), and this will also be validated. The file partition will be checked thoroughly, followed by copying them into different data units. Tools like Datameer, Talend, and Informatica are used for step-by-step validation. Data validation is also known as pre-Hadoop testing and makes it sure that the collected data is from the right resources. Once that step is completed, it is then pushed into the Hadoop testing system for tallying with the source data.

  2. Process validation: Once the data and source are matched, they will be pushed to the right location. This would be the business logic validation or process validation, where the tester will verify the business logic, node by node, and then verify it against different nodes. Business logic validation is the validation of MapReduce, the heart of Hadoop. The tester will validate the MapReduce process and check if the key-value pair is generated correctly. Through the “reduce” operation, the aggregation and consolidation of data are checked out.

  3. Output validation: Output validation is the next important component. Here, the generated data is loaded into the downstream system (this could be a data repository) and the data goes through analysis and further processing. This is then further checked to make sure the data is not distorted by comparing the HDFS file system with target data. Architecture testing is another crucial part of big data testing, as having poor architecture will make the whole effort go to waste. Luckily, Hadoop is highly resource-intensive and is capable of processing huge amounts of data, and for this, architectural testing becomes mandatory. It is also important to ensure that there is no data corruption and compare the HDFS file system data with target UI or business intelligence system.

ETL Testing

ETL is an acronym for extract, transform and load, and has been around for a very long time because it is associated with traditional batch processing in the data environment. The function of data warehouses is to provide businesses with data that they can consolidate, analyze, and make coherent ideas out of that is relevant to their focus/goals. There are ETL tools through which the raw data is converted into a meaningful format. The tool also helps them convert data into a format that can be used by businesses. Software vendors like IBM, Pervasive, and Pentaho provide ETL software tools.

  • Extract: Once the data is collected, it will be extracted/read from the source database. This is done to all the databases.

  • Transform: Transformation of the data is done next. The data format is changed into usable chunks and must conform to the requirements of the target database.

  • Load: This is the final stage where you write data to the target database.

To ensure that the data procured in this manner is trustworthy, tools for data integration processes like data profiling, cleansing, and auditing are all integrated with data quality tools. This entire process will ensure that you have extracted actual data. ETL tools are also important for loading and converting both structured and unstructured data into Hadoop. It also depends on the kind of ETL tools that you use. Highly advanced ones let you convert multiple simultaneously.

The data processing segment in a data warehouse follows a three-layer architecture during the ETL process.

1. Data Warehouse Staging Layer

The staging area is a temporary location or a landing zone where data from all the resources are stored. This zone ensures that all the data is available before it is integrated into a data warehouse. It is imperative for the data to be placed somewhere because of varying business cycles, hardware limitations, network resource limitations, and data processing cycles. You cannot extract all the data from all the databases at the same time. Hence, data in the data warehouse is transient.

2. Data Integration Layer

This is the foundation of next-generation analytics and it contributes to business intelligence. The data integration layer is a combination of semantic, reporting, and analytical technologies based on the semantic knowledge framework. Data is arranged in hierarchical groups known as facts and converted into aggregated facts. This layer is the link between the staging layer and the database.

3. Access Layer

Using common business terms, users will be able to access the data from the warehouse. The access layer is what the users can access, and the users themselves know what to make of the data. It is almost like a virtual layer because it doesn’t store information. The layer contains data targeted to a specific population, making access and usage easier.

Benefits of Using Big Data Testing

Through big data testing, you can ensure the data in hand is qualitative, accurate, and healthy. The data you collected from different sources and channels are validated, aiding in better decision-making. There are several benefits to big data testing.

  • Better decision-making: When data gets into the hands of the right people, it becomes an asset. When you have the right kind of data at hand, it helps you make sound decisions, analyze risks, and make use of only the data that will contribute to the decision-making process.

  • Data accuracy: Gartner says that data volume is likely to expand by 800% in the next five years, and 80% of this data will be unstructured. Imagine the volume of data that you have to analyze. You need to convert all this data into a structured format before it can be mined. Armed with the right kind of data, businesses can focus on their weak areas and be better prepared to beat the competition.

  • Better strategy and enhanced market goals: You can chart a better decision-making strategy or automate the decision-making process with the help of big data. Collect all the validated data, analyze it, understand user behavior, and ensure all of them are realized in the software testing process so you can deal out something they need. Big data testing helps you optimize business strategies by looking at this information.

  • Increased profit and reduced loss: Loss of business will be minimal or even a thing of past if data is correctly analyzed. If the accumulated data is of poor quality, the business suffers terrible losses. Isolate valuable data from structured and semi-structured information so no mistakes are made when dealing with customers.


Transforming data with intelligence is a huge concern. As big data is integral to a company’s decision-making strategy, it is not even possible to begin asserting the importance of arming yourself with reliable information.

Big data processing is a very promising field in today’s complex business environment. Applying the right dose of test strategies and following best practices will ensure qualitative software testing. The idea is to recognize and identify the defects in the early stages of testing and rectify them. This helps in cost reduction and better realization of company goals. Through this process, the problems that testers face during software testing are solved because the testing approaches are driven by data.

big data, big data testing, data analytics, data integration, data quality

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