After the deregulation of professional and amateur sports betting, media and entertainment (M&E) companies wasted no time creating consumer-oriented services through feature-rich applications and supporting platforms. As a result, digital technologies in sports betting provided avid online gamers enjoyable and secure entertainment—with mixed results.

The market Opportunity

As of October 2021, statistics show that over 30,800 businesses worldwide have entered the sports betting market, which sits at 58.9bn USD and growing. However, growth for sports betting companies may be compromised if they don’t address the expectations of their customers. When their experience with sports betting applications and platforms is abysmal, customers move on to the next one. And they have plenty of other options to choose from. 

Online Gamer Expectations

Sports betting companies must assess how their consumer-oriented service may be failing the user. You may currently host an online sports betting application or platform or plan to build one shortly. Either way, you should have insights into current or potential online gamer struggles and identify opportunities to elevate user experience.

Here are five things online gamers and betters are struggling with right now:

  1. Seamless navigation: Poor navigation may be because of a poor UI/UX design interface, introducing friction between the user and the application. Designing an intuitive, user-friendly product using buttons, images, colors, and data directs users where to go and what to do. Customer journey mapping, ethnographic research, design strategy, and superior UI/UX design will significantly eliminate friction and ensure a seamless user experience.
  2. Flexible Options: For instance, laws and regulations may vary from state to state, and sports betting companies must have the technology and platforms to meet the differing requirements. In addition, users must have the option to pay via non-traditional methods like cryptocurrency.
  3. Engaging Content: A betting platform or app should provide timely, relevant, and valuable information about upcoming games, highlights, and statistics. It should outline the features like live streaming, early cash-out, taking control of bets, or betting from the comfort of home. The real value for customers lies in easy access to all kinds of relevant content and connections with the greater sports betting community. They often discuss or share tips on betting, statistics and analysis, and ways to earn freebies.
  4. Anytime. Anywhere Access: Users expect a secure access and seamless experience across games, devices and locations, and throughout the gaming season. Robust platform architecture and rigorous testing mythology will help avoid or minimize the friction due to technical issues like log-in problems, poor latency, crashing apps, or incompatibility across devices.
  5. Broadly focused features: Betting apps designed to engage a wide range of customers may miss the mark due to a lack of personalization. According to independent design publication and blog UX Collective, companies must know their customer: Some numerous personas and journeys exist within the betting world, so make sure to define the journey and the optimal experience for every persona. Too often, we are told that the products and features are for everyone—that’s too broad to create a meaningful and measurable experience.


Overcoming the Challenges with Technology and Innovation

Overcoming industry-specific challenges requires a deep understanding of the application of digital technologies in sports betting and a trusted partner with deep expertise in strategic design, complex engineering, and the sports betting industry. Media & Entertainment companies can bring their vision to life by leveraging the power of ML and AI, Big Data and Analytics, Cloud, DevOps, Mobile/Web technologies to innovate and build next-gen products and platforms, to deliver engaging, data-driven user experiences.

GlobalLogic, a pioneer in digital product engineering, has proven expertise in online gaming/betting and industry-specific platforms. Its Betting Platform Reference Architecture helps clients quickly deploy a platform that can grow with the organization while providing users with an immersive and personalized experience by adding social interaction and gamification to the betting front end. To learn more, click here.

The sports entertainment industry is growing. Live and on-demand sports streaming platforms are being welcomed by sports enthusiasts worldwide.

However, designing and launching a best-in-class streaming product requires access to niche digital skills, which take significant investment, time, and effort. For one of our customers who owns a top-grossing on-demand sports platform, the time and effort were well worth it, but the team didn’t do it alone. The customer and the platform creator (a leading digital sports content and media group) joined forces with Method, a GlobalLogic company, to find worldwide success.

Success: Co-location and Co-creation

A driving force for the customer is to connect the world of sport by producing the most detailed and engaging content. Part of this focus included building a digital product that would disrupt the sports broadcasting industry, making streaming content available to anyone who wanted it. A key member of the client team said, “We have seen a revolution in entertainment with the introduction of disruptive brands like Netflix; now it is sport’s turn to be more consumer-friendly.”

Though the client’s team had a specific vision for its digital product, they still needed help from experts to bring the vision to life. Method – a GlobalLogic company that provides global strategy, UX/UI design, and software engineering was their pick.

Method closely worked with the client’s product development team to build a platform that would reach consumers worldwide. The client worked side-by-side with Method’s product team at their London studio. Co-creation and co-location are two facets of success. Every team member was able to ask questions, make suggestions, and contribute to the overall design in person. This hands-on approach helped teams develop core systems and capabilities and evolve the product in the future.

Varying traditions and cultures affect customer expectations. Before embarking on product design, there was a need to understand the target market. A senior client executive further relayed that “Each of our local products is distinct and specifically intended to drive our business in Japan or our business in Italy.” The client further believed that to get people to subscribe, they must have the content that matters, whether Serie A in Italy, MotoGP in Spain, Bundesliga in Germany, or J-League in Japan, fights in the US, NFL in Canada. You have to build the product around must-have, must-see content.

Method conducted first-hand research, rapid prototyping, and interface concept testing for the initial launch slated for Japan and Germany. It then designed an interface that accommodates a range of formats and interests. Ultimately it’s the content that matters!

The sports app built by Method enabled video-on-demand service accessible via web-enabled devices, including tablets, computers, consoles, or TVs, which viewers in over 200 countries and territories are enjoying today.

Success: Strategic Partnerships

The case study is a resounding example of collaboration and co-creation with clients. Together, we constructed a complex yet adaptive design system with everything the client needed, from interface components and UX guidelines to experience principles. As a result, the app became one of the most profitable sports apps in 2019, and GlobalLogic is proud to have played a considerable part in its global success. 

At Method and GlobalLogic, we serve customers across the Media and Entertainment (M&E) segment—especially sports, online gaming, and betting segment—is expanding. GlobalLogic provides consumer-oriented services by developing feature-rich apps and supporting platforms to succeed in a competitive market.

If you’re looking for a strategic partner, choose GlobalLogic. Contact our team today, and let’s work together to build the exceptional.

Operationalization is one of the buzzwords in the technology industry. Even so, it’s still surprising to see operationalization associated with almost all areas of technology such as AnalyticsOps, AppOps, CloudOps, DevOps, DevSecOps, and DataOps.

Although companies rely on their people and data, creating meaningful data is still a challenge for many companies. Obtaining the right data at the right time can bring tremendous value to any company. Today, most organizations focus on collecting insightful data and consolidating their data infrastructure and operations to create a unified structure and data platformization.

This consolidation is the answer to data centralization. All forms of data have a lifecycle and flow through certain steps before the information becomes usable. As a result, we end up designing a highly scalable data platform with the latest and greatest technology, and company operations run smoothly.

Now, we must consider whether using scalable technological processes and implementing an end-to-end data pipeline is the best possible solution out there. There are specific challenges, apart from functional data pipeline development, which can create customer dissatisfaction and a loss in revenue.

These challenges include:

Growing demand for data.

Today, companies rely heavily on data to generate insights to help make decisions. Companies collect various forms of data from numerous different sources. This data impacts business growth and revenue. However, about 80% of that data is unstructured. Companies can use this unstructured or dark data with technology, artificial intelligence, and machine learning methodologies.

The complexity of data pipelines and scarcity of skilled people.

Data comes from multiple sources. The nature of that data is diverse and complex because there are numerous rules and ways to transform that data throughout the pipeline. To address these complexities, companies are looking for skilled data engineers, data architects, and data scientists who can help build these scalable and efficient pipelines. Finding these qualified individuals is a challenge every company faces to backfill demands and create automated processes.

Too many defects.

Even after rigorous quality checks, the complexity of these data pipelines creates a system full of defects that are then released to production. Once production reports the defects, it takes time to analyze and fix the issue, leading to SLA misses and customer dissatisfaction.

Speed and accuracy of data analytics.

Every company wants efficient and accurate analytics. However, when teams work in silos, it becomes challenging to create effective data pipelines. This is because quality collaboration among operations and data teams must take place to help identify requirements accurately before implementation.

Companies pretty much universally aim to deliver fast, reliable, and cost-effective products to customers while generating revenue. Accurate and reliable data is the force behind this goal, and DataOps is the methodology to build a data ecosystem to help industries capitalize on revenue streams.

What is DataOps?

Gartner’s Definition

DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization”.

In other words, the goal of DataOps is to optimize the development and execution of the data pipeline. Therefore, DataOps focuses on continuous improvement.

Dimensions of DataOps

DataOps is not an exact science as it works against different dimensions to overcome developmental challenges. However, when DataOps operates on a high level, it can be factored into the following dimensions:

  • Agile:
    • Short sprints
    • Self-organized teams
    • Regular retrospection
    • Keep customer engaged
  • Total Quality Management:
    • Continuous monitoring
    • Continuous improvement
  • DevOps:
    • TDD approach
    • CI/CD implementation
    • Version Control
    • Maximize automation

In looking at the different components, it’s clear we need competent teams to implement these dimensions. To create the DataOps processes, we require technical teams of data engineers, data scientists, and data analysts. These teams must collaborate and inegrate their plans with business teams of data stewards, CDOs, product owners, and admins who help define, operate, monitor, and deploy the components that keep business processes running.

Addressing Challenges and Data Monetization Using DataOps

Figure One


In the data platform, the data lifecycle goes through multiple steps. Figure One shows that data comes from different data sources: structured and unstructured data, video, and text. After processing data through the batch or streaming engine, it transforms into meaningful information stored in the data lake or polyglot storage. Then, stored significant data publishes through the consumer layer for the downstream or consuming system.

In this data flow, we usually focus on collecting data while keeping business objectives in mind and we create clean, structured data from transactional systems or warehouses. Generally, companies require this data from the consumer. Still, several questions remain:

  • Are we reaping any actual benefits from the required data?
  • Are data quality issues reported at the right time?
  • Do we have an efficient system that points out problematic areas?
  • Are we using the right technology to help monitor and report our system issues effectively?

So how do we address the challenges mentioned above to use the correct information at the right time through DataOps?

To explain this, we’ll use the following example.

Example: Identifying defects in earlier phases of development helps companies monetize their data.

We develop data pipelines focused on business functionality by utilizing the latest cutting-edge technology. When the teams deliver the final product to production, multiple defects arise. When the system reports defects, it takes a significant amount of time to analyze and fix the problems. By the time the teams resolve the problems, the SLAs are over. In many cases, these pipelines are critical and have specific SLAs and constraints.

Figure Two


I’ve worked with multiple companies to design their data pipelines. When I distribute data pointers on five maturity models, it appears as pictured in Figure Two.

However, most companies are only at maturity level one or two, where issues are either detected from production or it takes a lot of time to fix those identified issues. In addition, very few companies have matured processes in which they proactively determine issues or create automated RCAs with a self-healing mechanism. Through DataOps processes and methodology, companies can achieve higher maturity levels.

We must add a highly collaborative data and operations team that works in tandem to set the goals and optimize proper processes, technologies, and methodologies to mature our data pipeline. This collaborative process helps to proactively identify slow or problematic data, automatically reports root cause analysis, and operates with self-healing systems. Today, companies rely heavily on artificial intelligence and machine learning technology to automate their bug reporting and for a self-healing system. These systems expedite the overall process to achieve defined SLAs and gain customer satisfaction.


DataOps is a combination of processes and technologies that automates quality data delivery to improve data value according to business objectives and requirements. It can fasten the data cycle timeline, generate fewer data defects, greater code reuse, and accelerate business operations by creating more efficient and agile processes with timely data insights.

DataOps can increase overall performance through high output, quality, and productivity in SLAs. Proper DataOps processes, governance teams, and technology can help industries capitalize on revenue streams, as well. Today, data is one of the most valuable resources there is, and it is the force for any company’s growth potential.




Data is the key to understanding behavior, patterns, and insights. Without data, it is incredibly complicated to gain the knowledge to decide the right actions to meet objectives. Therefore, collecting the right data is a crucial aspect of a data and analytics platform. But recent events show that the way organizations collect customer data and customer usage data for web applications will change.

Google announced that it will block cross-site tracking through third-party cookies by the end of 2023. This change means that using third parties to collect data will no longer be possible. With other browsers such as Safari and Firefox also working towards phasing out third-party cookies on their browser, the end of third-party cookies is here.

With privacy laws like GDPR coming into effect in recent years, how organizations can collect and use data is subject to many regulations. The privacy laws have also ensured that data privacy is at the forefront of the users’ thoughts, with notices for data usage requiring user consent. However, obtaining third-party data usage consent has become problematic because users are reluctant to share data when presented with information on its use.

Now that third-party data is more difficult to acquire, first-party data has become essential and needs to be a priority in an organization’s data strategy. But, first, let’s define the difference between first-party and third-party data before further discussing the situation.

The organization itself collects first-party data, and it has exclusive ownership of the data. However, external entities typically collect third-party data and then aggregate it for sale to different parties. Utilizing first-party data means more than just collecting data directly from consumers and customers. It also means first-party data needs to be secured and managed correctly with appropriate governance to ensure transparency and privacy across the whole data lifecycle.

Now, we’ll discuss the main pillars of an effective first-party data strategy to harness first-party data.

First-Party Data Strategy Pillars



First, organizations must decide what data to collect based on business objectives and user experience goals. The next step is to collect this data from users. Since there is friction getting consent for user’s data, earning the user’s trust through appropriate data collection channels is crucial.

For example, utilizing loyalty benefits or offers can help gain the user’s trust. It is also essential to provide full transparency on how the organization will use the data since users don’t want to receive irrelevant advertising.

Organizations also need to invest in new technology, applications, and websites to collect first-party data with user consent and move away from third-party mechanisms. However, organizations can retain the ownership of data and analysis, and strategic partnerships can develop technological modules. Additionally, customer data platforms can  help solve the technical puzzle of collecting first-party data.


It is essential to get consent from users to secure the use of the data. Organizations need to ensure transparency on how the data will be used and obtain an agreement from users or customers. Additionally, organizations need to adhere to the customer agreement to process and use the data and comply with laws and regulations.


Data governance means understanding the policies, processes, and structures applied to support data security, compliance, storage, management, data classification and data usage. Implementing the right data governance processes to ensure compliance with laws, regulations, and user consent is crucial to maintain the customers’ trust regarding their privacy and avoid potentially heavy fines.

Identity Resolution

Organizations must create customer profiles with appropriate data anonymization standards to protect the customer’s identity. Data stewardship and data governance practices can also help uphold the agreement with the customer. Additionally, organizations can tie customer profiles to channels and device-level identifiers to ensure there’s no violation of data collected from different channels. These processes are also crucial in case customers no longer want to share their data with the organization.

Data Platform

Organizations need a data platform to collect, store, analyze and process first-party data from different sources that can also provide analytical models. Additionally, the data platform should include modern data warehouses and custom data tools.

Data Use

The way organizations use first-party data is crucial. Obtaining user consent and adhering to the agreement builds trust not just with the user but also with customers. Additionally, the users are more likely to continue providing data as they see its value to the organization they built trust with over time.

First-Party Data Strategy with GlobalLogic

At GlobalLogic, we advise our partners on data strategy and implementation of data platforms, modern data warehouses, and data governance processes. These services can help lay the foundation for first-party data strategy and usage. If you’re ready for the transition from third-party to first-party data collection, please reach out to the Big Data & Analytics department at GlobalLogic to discuss data advisory and data platform implementation. In addition, we can help create the data governance processes and show you how to manage first-party data with relevant monetization applications.

There are numerous opportunities for CSPs to integrate 5G and its capabilities into a platform to capitalize on advantageous revenue streams. When creating an innovative and connected platform, there are many components to consider, such as a distributed cloud infrastructure, industry-specific services, and more. Additionally, there are unique challenges to overcome and plan for, such as high investment costs and security concerns.

Only through partnering with subject matter experts on a developer platform can CSPs maximize the investment they made in 5G to take full advantage of B2B and B2B2X opportunities. Learn about the ways to overcome potential risks through specific collaboration opportunities and an integrated developer platform to profit from potential revenue streams.

Children’s Aspirations and Education in the Pandemic World

One of the most profound implications of COVID-19 was the impact on 1.5 billion children worldwide — and the disruption to their traditional in-classroom education. For those who were lucky enough to have access to the bandwidth, computers and technology, this meant entirely different ways of learning and education, and for the many who didn’t have access, this meant a catastrophic interruption to their education. So what have children really missed out on?

Dr. Ger Graus will enlighten us about new ways of looking at educating children, beyond the traditional systems and structures of schooling. You will get an entirely new perspective of how to inspire children to think about who they want to be (vs. defining themselves by an occupation).

About Dr. Ger Graus

Professor Dr Ger Graus OBE is a renowned figure in the field of education. He was the first Global Director of Education at KidZania, and, prior to that, the founding CEO of the Children’s University. In 2019, Ger was invited to become a Visiting Professor at the National Research University in Moscow, Russia. In 1983 he moved to the United Kingdom where he began his teaching career and subsequently became an Education Adviser, a Senior Inspector, and Director of Education.

Ger is a member of the Bett Global Education Council, Junior Achievement’s Worldwide Global Council, chairs the Beaconhouse School System’s Advisory Board, Pakistan, Advises the Fondazione Reggio Children, Italy, and has been invited by His Highness Sheikh Hamadan Bin Mohammed Al Maktoum, Crown Price of Dubai, to help shape the future of education in Dubai as a member of the Dubai Future Councils. He also works with and advises organizations globally on the learning agenda in its widest sense, including the Organization for Economic Co-operation and Development (OECD), WISE as part of the Qatar Foundation, the UK Information Commissioner’s Office, as well as the business world.

In the 2014 Queen’s Birthday Honor’s List Ger was made an Honorary Officer of the Most Excellent Order of the British Empire (OBE) for services to children. In his book ‘Natural Born Learners’, author Alex Beard says: “In learning terms, Ger Graus is Jean-Jacques Rousseau meets Willy Wonka.”

Analytic Process Automation (APA) is the essential data analytics software to optimize insurance industry business processes. APA’s three main aspects are democratizing data and analytics, automating processes, and upskilling people. It also helps make your data work for you and alleviates employees’ focus from repetitive tasks to create time for upskilling. 

Additionally, APA can automate time-consuming processes like claim management and underwriting. Incorporating APA into your business operations can help your company overcome main challenges in insurance, such as mismanaged resources, operational blockades, and data crunches. Learn about the critical components of APA and how to incorporate them into your company effectively.

Industry 4.0 is streamlining the incorporation of automation and technology to improve smart machine capabilities. Artificial intelligence, machine learning, and data analysis are the foundation of smart machines, which help create smart spaces in factories. In addition, these resources enhance the efficiency of data flow to management and help keep their workforce safe.

Low-power wide-area networks, 5G Networks, Edge Computing, and AI are improving the functionality and application of smart machine technology to put the control of the factory and its output in the factory leader’s hands. Read about the technological improvements these smart machines can bring to your company and the use cases where Industry 4.0 technology can improve your factories.


Over the last few decades, huge amounts of data have been generated from different types of sources. Enterprises increasingly want to utilize the new age data paradigms to drive better decisions and actions. It provides them an opportunity to increase efficiencies, push newer ways of doing business, and optimize spends.

However, a lot of companies are struggling with data issues because of the advanced technological stacks involved and the complex data pipelines that keep changing due to newer business goals. It has become imperative to leverage best practices for implementing data quality and validation techniques to ensure that data remains usable for further analytics to derive insights.

In this blog, we look at the data quality requirements and the core design for a solution that can help enterprises perform data quality and validation in a flexible, modular, and scalable way.

Data Quality Requirements

A data platform integrates data from a variety of sources to provide processed and cleansed datasets that comply with quality and regulatory needs to analytical systems so that insights can be generated from them. The data being moved from the data sources to the storage layers need to be validated, either as part of the data integration pipeline itself, or independently compared between the source and the sink.

Below are some of the requirements that a data quality and validation solution needs to address:

  • Check Data Completeness: Validate the results between the source and target data sources, such as:
    • Compare row count across columns
    • Compare output of column value aggregation
    • Compare a subset of data without hashing or full dataset with SHA256 hashing of all columns
    • Compare profiling statistics like min, max, mean, quantiles


  • Check Schema/Metadata: Validate results across the source and target, or between the source and an expected value.
    • Check column names, data type, ordering or positions of columns, data length


  • Check Data Transformations: Validate the intermediate step of actual values with the expected values.
    • Check custom data transformation rules
    • Check data quality, such as whether data is in range, in a reference lookup, domain value comparison, or row count matches a particular value
    • Check data integrity constraints like not null, uniqueness, no negative value


  • Data Security Validation: Validate different aspects of security, such as:
    • Verify if data is compliant as per regulations and policies applicable
    • Identify security vulnerabilities in underlying infrastructure, tools leveraged, or code that can impact data
    • Identify issues at the access, authorization, and authentication level
    • Conduct threat modeling and testing data in rest and transit


  • Data Pipeline Validation: Verify pipeline related aspects such as whether:
    • If the expected source data is picked
    • Requisite operations in the pipeline are as per requirements (e.g., aggregation, transformations, cleansing)
    • The data is being delivered to the target


  • Code & Pipelines Deployment Validation: Validate that the pipelines with code have been deployed correctly in the requisite environment
    • Scale seamlessly for large data volumes
    • Support orchestration and scheduling of validation jobs
    • Provide a low code approach to define data sources and configure validation rules
    • Generate a report that provides details about the validation results across datasets for the configured rules


High-Level Overview of the Solution

Below is a high-level design for a data quality and validation solution that addresses the above-mentioned requirements.

  • Component Library: Generalize the commonly used validation rules as a stand-alone component that can be provided out-of-box through a pre-defined Component Library.


  • Components: For advanced users or for certain scenarios, custom validation rules might be required. These can be supported through an extensible framework that supports the addition of new components to the existing library.


  • Job Configuration: A typical QA tester prefers a low-code way of configuring the validation jobs without having to write code. A JSON or YAML-based configuration can be used to define the data sources and configure the different validation rules.


  • Data Processing Engine: The solution needs to be able to scale to handle large volumes of data. A big data processing framework such as Apache Spark can be used to build the base framework. This will enable the job to be deployed and executed in any data processing environment that supports Spark.


  • Job Templates: Pre-defined job templates and customizable job templates can provide a standardized way of defining validation jobs.


  • Validation Output: The output of the job should be a consistent validation report that provides a summary of the validation rules output across the data sources configured.


Accelerate Your Own Data Quality Journey

At GlobalLogic, we are working on a similar approach as part of our GlobalLogic Data Platform. The platform includes a Data Quality and Validation Accelerator that provides a modular and scalable framework that can be deployed on cloud serverless Spark environments to validate a variety of sources.

We regularly work with our clients to help them with their data journeys. Tell us about your needs through the below contact form, and we would be happy to talk to you about next steps.


I had an opportunity recently to play with test cases and asked my colleague, “What do I need to test?”

He said, “Mate, this is a unit test, and you need to decide the test cases according to the request and response, which should cover all the scenarios.”

This presented a dilemma for me, so I decided to write this complete guide for test cases. Let’s begin with my first question.

What is a Test Case?

In their simplest form, test cases are the set of conditions under which a tester determines whether the software satisfies requirements and functions properly. In layman’s terms, these are predefined conditions to check that the output is correct.

What Do I Need to test?

There is usually a simple answer to this question, by using a coverage package that measures the code coverage that also works during test execution. You can learn more about this in its official documents. Unfortunately, this was not the case in my situation.

The second approach is fairly straightforward. Typically, test cases are written by the developer of the code – and if you are the developer of the code, you are well aware of the flow of the code. In this situation, you need to write your test cases around the request and expected response of the code.

For example, if you are writing test cases for the division of a number, you must think about the code’s expected input and expected output.

Test-driven Development Definition: “Test-driven development (TDD) is a software development process relying on software requirements being converted to test cases before software is fully developed, and tracking all software development by repeatedly testing the software against all test cases.”

Django’s unit tests use a Python standard library module called unit test. The below module shows tests using a class-based approach.

How to Start Writing Test Cases

Here, we have one example for the method and class where we can start the test cases.

Class ProfileTestCase(TestCase):

def setUp(self):


def test_my_test_1(self):


def test_my_test_2(self):


def tearDown(self):


The following is a general test template for writing test cases in Django python.

For this example, TestCase is one of the most important classes provided by the unit test module, and it provides the foundation for testing our functions.

Also, for this example, SetUp is the first method that we run in our testing of the code. Therefore, it helps us set up the standard code required for each method that we can use in our entire testing process inside our testing class.

A teardown test case always runs last, as it can delete all objects or tables made while testing. It will also clean the testing environment after completing a test.

Now, let’s write out the test case:

Class CourierServices(TestCase):

def setup(self):

self.courier_data = CourrierModel.objects.all()

self.url = ‘/courier/service/’ #This is the url which we are going to hit for the response

def test_route(self):

response = self.client.get(self.url)

self.assertEqual(response.status_code,200) #here we are checking for the status 200

def test_zipcode(self):


zip_code = “110001”

query_param = {‘zip_code’: zip_code}

response = self.client.get(self.url, data=query_params) #(we are trying to hit the url(self.url) using #parameter(data=query_params) and collecting the response in (response))

self.assertEqual(200, response.status_code)

#here test the response code you get from the url and compare it with the 200

response_json = response.json()

results = response_json.get(‘results’, [])

self.assertIsInstance(results, list)

self.assertEqual(results[0][‘zip_code’], zip_code)

Here is another valuable code sequence, and here we are trying to test the most common code known as the Login Function:

Class loginTest(TestCase):

def setUp(self):

self.user = get_username_model().objects.create_user(username=’test’, password=’test123′, email=’’,mobile_no=1234567890)

def test_correct_user_pass(self):

user = authenticate(username=’test’, password=’test123′)

self.assertTrue((user is not None) and user.is_authenticated)

def test_wrong_username(self):

user = authenticate(username=’fakeuser’, password=’test123′)

self.assertFalse(user is not None and user.is_authenticated)

def test_wrong_password(self):

user = authenticate(username=’test’, password=’fakepassword’)

self.assertFalse(user is not None and user.is_authenticated)

def tearDown(self):


Note: A test method passes only if every assertion in the method passes. Now, you may be wondering, What do these assertions mean, and how do you know which ones are available? I will try to answer these questions as thoroughly as possible.

Here are some commonly used assertion methods:


Method Meaning
assertEqual(a, b) a==b
assertNotEqual(a, b) a != b
assertTrue(x) bool(x) is True
assertFalse(x) bool(x) is False
assertIs(a, b) a is b
assertIsNot(a, b) a is not b
assertIsNone(x) x is None
assertIsNotNone(x) x is not None
assertIn(a, b) a in b
assertNotIn(a, b) a not in b
assertIsInstance(a, b) isinstance(a, b)
assertNotIsInstance(a, b) not isinstance(a, b)

These methods are empowering; sometimes when we use them, an exact match isn’t required.

For example, how do I test for x-y = almost zero? This is where assertion methods can help. I see it as the “lifesaver” method.


Method Meaning
assertAlmostEqual(a, b) round(a-b,7)==0
assertNotAlmostEqual(a,b) round(a-b,7)!=0
assertGreater(a, b) a>b
assertGreaterEqual(a,b) a>=b
assertLess(a, b) a<b
assertLessEqual(a, b) a<=b
assertRegex(s, r)
assertNotRegex(s, r) not
assertCountEqual(a, b) a and b have the same elements in the same number, regardless of their order.
assertListEqual(a, b) It compare two list
assertTupleEqual(a, b) It compare two tuple
assertSetEqual(a, b) It compare two set
assertDictEqual(a, b) It compare two dictionary

Now that we know how to write the test cases, let me show you how to run them. Running the test cases is easy in Django python.

Write your test cases in the module, then go to the terminal and Run this command:

Python –m unittest my_test_module_1 my_test_module_2

If you want to run the test class, then use:

Python –m unittest my_test_module_1.TestClass

If you want to test your method, run this:

Python –m unittest my_test_module_1.TestClass.my_test_method

You can also run this test case:

Python -m unittest tests/

Sometimes, we want to run the test cases via docker. For that, you can use the following method.

  1. First, go inside your web container using exec:

docker exec -it my-own-services_web_1 \bin\bash


  1. Then you will get the cmd prompt like this:


Note: You need to check your docker-compose.yaml and see the volume path. It will look something like this – .:/opt/app and it may change in your case.

 python3 test test_folder.sub_folder.test_views.YourTestCases –settings=docker.test_settings

I hope this blog inspires you to start coding with the TDD approach, which will help make your code bug-free and robust too.

Remember the Golden Rules of TDD

  • Write production code only to pass a failing unit test.
  • Write no more of a unit test than is sufficient to fail (compilation failures are failures).
  • Write no more production code than is necessary to pass the one failing unit test.

Next blog will cover the same in detail…