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Why It’s Vital for Companies to Focus on Data Engineering?

Digitalization is multiplying, making data the most prized asset in the world. Organizations are strategically moving towards insight-driven models where business decisions, process enhancement, and technology investments are handled with the knowledge gained from data. Big budgets are planned to make use of abundant data available, and this spending will only increase over the years. 

According to a recent IDC report, it is estimated that by 2025 the Global Datasphere will grow to 175 zettabytes (175 trillion gigabytes). It also states that 60% of this data will be created and managed by businesses, driven by Artificial Intelligence (AI), Internet of Things (IoT), and Machine Learning (ML). AI and ML are gaining mainstream focus among many industries, and global spending is expected to grow to $57.6B by 2021.

How Data Engineering is helping businesses succeed?

Organizations often consider Data Science to be the only method to gain meaningful insights necessary to drive their business goals. However, the real potential lies within Data Engineering, which allows companies to build large maintainable data reservoirs. These design data processes are scalable and ensure relevant data is available for Data Science and Data Analytics to process complex statistical programs and algorithms to provide useful results. Only with reliable and accurate insights created from diverse sources can help data analytics harness the full power of data. 

Today, AI and ML have become integral parts of organizations, helping them achieve higher operational efficiency, become agile, taper new market opportunities, launch new products with faster go-to-market, and provide higher customer satisfaction. But according to a survey done by MIT Tech Review, 48% of companies said that getting access to high quality and accurate data was the biggest obstacle in successfully implementing an AI program. To overcome this hurdle, businesses must focus on effective Data Engineering, which forms the basic building blocks for AI and ML.

Three advantages of effective Data Engineering:

1) Accelerates Data Science 

2) Removes bottlenecks from Data Infrastructure 

3) Democratizes data for Data Scientists and Data Analytics 

Once organizations understand and internalize this, it is easy to see how the potential of Data Engineering is limitless. 


How data engineering is helping businesses across industries

Industry influencers and other prominent stakeholders certainly agree that Data Engineering has become a big game-changer in most, if not all, types of modern industries over the last few years. As Data Engineering continues to permeate our day-to-day lives, there has been a significant shift from the hype surrounding it to finding real value in its use. 


Industry 4.0 is here, and the sooner organizations start their digital transformation, the better equipped they become to handle the evolving market conditions. What Industry 4.0 has brought is a significant shift in how manufacturing businesses are changing from being purely process-driven, to becoming data-driven. This essentially means that companies are either adding new digital components or updating their existing components with digital features. However, this creates a complex technology landscape where legacy systems have to interact with modern systems. 

An effective Data Engineering solution can communicate and retrieve data from different systems, sort out critical data from a pool of data, and process them to be analyzed further. Data Engineering bridges the gap between Production, Research Development, Maintenance, and Data Science. Data Engineering can help in enhancing the critical aspects of manufacturing industry—production optimization, quality assurance, preventive maintenance, effective utilization of resources, and, ultimately, cost reduction. 


Data has the power to make or break a business, and no one understands this better than Netflix. The incredibly successful data-driven company uses insights across its business functions to decide what new content to invest in and launch, enhance operational efficiency, and, most importantly, provide predictive recommendations for its global audience. 

Netflix has also used its robust Data Engineering system to convert over 700 billion raw events into business insights, which is one primary reason why the company continues to be the market leader.


The retail industry is continuously trying to tap into new business opportunities by gaining insights from data sources across the physical and virtual ecosystems. To gain these business insights, data must be gathered from a large network (comprising of POS systems, e-commerce platforms, social media, mobile apps, supply chain systems, vendor management systems, inventory management systems, in-store sensors, cameras, and a growing list of new sources). 

An effective Data Engineering solution can bring together massive sets of structured and unstructured data from entire value chain to provide trends, patterns, customer insights, and more. A retailer with stores across the globe and an omnichannel presence can harness data sources in innovative ways with Data Engineering to gain a detailed understanding of the market, the competition, and every step of the customer journey. 


Leading healthcare giants are progressively investing in integrating ML into their core functions. However, they are focusing on setting up their data infrastructure by building Data Engineering platforms. The healthcare industry is looking to unlock value from data to gain knowledge into the patient, healthcare worker, and the healthcare system on a large scale. 

Data Engineering brings together insights from electronic patient records and hospital data, as well as new advanced data sources like gene sequencing, sensors, and wearables. It offers them to Data Analytics to provide better medical treatment. 

How Data Engineering is fueling the businesses of the future

To manage data at large scale and segregate business-critical data from the rest, organizations need a long-term data strategy plan to be future-ready with Data Engineering as critical approach. 

Data Engineering creates scalable data pipelines

Distributed data processing systems can help create reliable data pipelines with low level of network management to meet huge volumes and tap into increasing data sources in a growing ecosystem of touchpoints. 

Data Engineering ensures that data is consistent, reliable, and reproducible

For data processing to be successful through the stages of ingestion, analytics, and insights, it is important that the data be compatible by ensuring it complies with the required formats and specifications. Data science can derive better insights from data by providing reliable and reproducible data.

Data Engineering helps ensure that processing latency is low

Most essential business insights are required to be in real-time to have an effective impact, be it with customer experience in the retail industry or predictive analysis in the financial sector. If the data being analyzed has a significant time delay, the insights can be less effective or completely ineffective. 

Data Engineering optimizes infrastructure usage and computing resources

Using the right algorithm for data engineering can save a considerable amount of money spent on resources. This can provide significant savings to organizations and help them optimally utilize their technology landscape. 

Businesses must design Data Engineering solutions that are unique to their needs and create customized frameworks rather than follow trends. At the same time, many new start-ups begin their data journeys with clearly defined data sets. In contrast, traditional organizations may have larger ones from legacy systems and data sets from new sources. It is important to understand that while the Data Engineering tools for a particular organization are zeroed, no general rule can be used. Only a comprehensive study of a company’s unique technology ecosystem and business needs can determine the type of Data Engineering systems that should be used. 

Data Engineering solutions must also be flexible. How data is produced and consumed is constantly evolving, so Data Engineering solutions or frameworks must be flexible to accommodate future requirements. Guiding the movement in this direction is the shift from traditional Extract Transform and Load (ETL) methods of the data pipeline to more pliable patterns like ingesting, model, enhance, transform, and deliver. The latter provides more flexibility by decoupling Data Pipeline services. 

Many experts focus on Data Engineering one step further by encouraging companies to adopt a Data Engineering Culture. This permanently recognizes the need for Data Engineering at all levels of an organization across functions and warns that business predictions will fail without effective Data Engineering and an appropriate ratio of Data Engineers to Data Scientists. 

The sooner organizations push for Data Engineering Culture and create organizational alignment, the more equipped they will be for the future, to which data holds the key. 

How TVS Next created a Data Engineering solution for one of India’s top utility companies

In the energy sector, large enterprises are turning real-time data to drive effective energy management. Energy corporations rely on data for efficient resource management, operational optimization, reduced costs, and increased customer satisfaction with better insights into supply and demand in real-time. 

TVS Next helped one of India’s leading utility companies build a distributed computing engine for processing and querying data at scale. The solution provided the company with tools to visualize key performance indicators using real-time data. With effective Data Engineering, the client improved the customer experience rather than relying on complex algorithms to predict outcomes. 

What are some of the achievements and challenges you have faced while planning a Data Engineering system for your organization? Share your story and get in touch with us here

Disruptive Technology for Healthcare

With immense technological advancement, global healthcare industry will transform tremendously and move towards digital. Factors like increasing population, declining healthcare budgets, and rise of chronic diseases add pressure on healthcare providers and governments to shift to innovative technologies. 

Consequently, the global healthcare industry is ideal for driving new technological advancements like Internet of Things (IoT). Smart devices such as smartphones, smartwatches, and other new emerging technologies will act as driving forces of this revolution.   

Let’s discuss some of the technologies that could transform the healthcare industry in the future. 

AI and Machine Learning

Machine Learning is a form of AI that offers learning capabilities to computers without being explicitly programmed. It means that computers can teach themselves to modify according to the need when exposed to new data. With AI, there will be a considerable amount of data to explore. Several tech conglomerates like Google, IBM, etc. have already started exploring these technologies’ potential applications in healthcare.  

There are truly exciting possibilities for the application of AI/ML for such digital surgery robots. 

  • A software-centric collaboration of robots with the aid of massive distributed processing 
  • Data-driven insights and guidance based on surgery histories (performed by both machines and humans) and their outcomes (favorable or not) 
  • AI-generated virtual reality space for real-time direction and guidance 
  • Possibility of telemedicine and remote surgery for relatively simple procedures 

Digital Therapy

These are healthcare interventions delivered to patients through smart devices like smartphones or laptops. They combine medical practice and therapy in a digital form. Computerized Cognitive Behavioral Therapy (CBT) is a new group of automated digital therapies that aims to provide CBT at scale with better engagement.  

“Digital Therapeutics: Combining Technology and Evidence-based Medicine to Transform Personalized Patient Care” 

Digital therapies are disease-specific treatment tools. Often, they are a substitute treatment (or the only treatment) with sensory stimuli; in other situations, they support or enhance conventional medicine incorporating electronic usage with tools or medications.

For example, some devices complement conventional treatment by helping patients manage and control their conditions, providing indicators such as remembering when and how much medication to take.

Apps and Smartphones

The full potential of smartphones is yet to be perceived by the healthcare sector. Companies are making efforts to curate quality apps, such as the NHS (National Health Service) app library. With powerful processing capabilities, smartphones can serve as the hub for new diagnoses and treatments.

Although the use of mHealth devices and applications is already common in clinical trials, pharmaceutical companies are now concentrating on connected drug delivery systems that will automatically identify and monitor medication usage by patients to enhance adherence. 

Health apps and smartphones help to: track personal health data, real-time communication with your doctor or another healthcare provider, and improve the quality of life for doctors and their patients. 

Portable Diagnostics

With devices like portable X-ray machines, we can bring cutting-edge diagnostics at our doorstep. Doctors can provide better quality of care by more profound and meaningful engagements with patients. It also leads to the continuous capturing of crucial health-related information, which will eventually reduce the overall healthcare costs. Assistive devices like smart wheelchairs are used by patients with permanent disabilities, which help them perform specific tasks and gather essential health information. This information can later be used for modifying treatment procedures.  

Online Communities

Online communities and famous healthcare networks like MedHelp bring medical experts and patients together to share health advice and tips. They also serve as a platform for tracking health data, which helps people better manage their conditions.  

Advanced technologies like these offer new opportunities to the healthcare system in improving correctness and usefulness of medical information. It also provides new ways to prevent, detect, and treat diseases at early stages rather than reach the terminal phase.  


Implantable Drug Delivery Systems  

It is estimated that nearly one-third of all medication prescribed to patients with long-term health conditions are not adequately taken as recommended by the physicians. Emerging technologies could change this by enabling healthcare professionals with continuous monitoring capabilities.  

In the future there could be sensors that are tiny and can be swallowed along with drugs. As soon as the pill dissolves in the patient’s stomach, the sensor will get activated and transmit data to a smartphone app. Patients and doctors can see how well they are adhering to the prescription, though it raises questions about the patient’s privacy.  


Blockchains are decentralized databases that keep record of how data is generated and changed over time. The main feature of Blockchains is that it can be trusted, as the records are authentic without a central authority guaranteeing accuracy and security.  

Though Electronic Health Records are commonly used, they are usually centralized. Some analysts state that Blockchain would bring several benefits to patients and doctors as compared to other records.  

Genome Sequencing

Breakthroughs in genome sequencing and its associated field will help us better understand the diseases. Genome sequencing gives a genetic profile of a patient’s illness by which doctors can predict their treatment.  

Globally, several big projects are underway to understand the association between genes and health conditions. In the UK, the government is funding 100,000 Genomes projects. In the US, one company has promised to build a database featuring 1 million genomes by 2020.  


Maintaining patient care in today’s hyper-connected environment depends almost entirely on maintaining and leveraging their network and services. Network administrators need to be vigilant and disciplined – not just for performance, but to prevent security disruptions. 

CIOs and CCIOs (chief clinical information officers) in healthcare organizations face the urgent need to keep pace with technology. They introduce next-generation technologies in an attempt to improve overall efficiency, speed and safety. 

Healthcare is evolving, and modern technologies will transform human life for the better again, just as antibiotics and anesthesia have changed for decades. For hospitals and suppliers working together to protect data, the future seems bright. 

Extracting Acronyms through Natural Language Processing


An acronym is a pronounceable word created from the first letter of each word in a phrase or title. An acronym is a kind of abbreviation consisting of a first letter or initial letters in a word. It’s also called short descriptors of phrase.  

Interesting Fact: Acronym was introduced as a modern linguistic element of English during the 1950s. Because acronym is called a term, its meaning is called expansion.  

Usage & Challenges 

An acronym is primarily used in language processing, web search, ontology mapping, question answering, text messaging, and social media sharing. Acronyms evolve each day dynamically, and finding their definition/expansion becomes a daunting task due to its diverse characteristics. Several researchers experimented with plain text and network expansion pairs for mining acronyms over the past two decades. Manually edited online archives have pairs of acronyms, but regularly reviewing all possible meanings is intimidating.  


To handle this issue, TVS Next has built a specialized product to extract acronyms from a document in a few seconds. This product is built on Python for Natural Language Processing.  

Below are some pointers that describe how our research works that help us solve the problem mentioned above.   

Heuristics Approach 

NLP (Natural Language Processing) and pattern-based methods include heuristics. 

  • The NLP-based approach uses a fuzzy-matching Statistical Model based on the principles of Levenshtein’s Distance algorithm.  
  • The pattern-based approach uses custom rules that work with data from multiple domains, combined with Statistical Modelling to extract the Acronyms and their Expansions. These methods are written after considering features in the text as characteristics of acronyms – ambiguity, nesting, uppercase letters, length, and para-linguistic markers.  

An Acronym Finding Program (AFP) is a simple, free-text expansion recognition method. This program applies an inexact matching algorithm for mining AE pairs. A tool known as Three Letter Acronym (TLA) uses para-linguistic markers such as parenthesis, commas, and periods to derive acronym meaning from technical and government documents.  

Developing the Product  

A Statistical model has created to provide the user with a Solution that gives ease of access to acronyms that appear throughout the document. The designed solution can be integrated into various tools and technologies that deal with text-based information. The solution proves to be useful while combining it with tools that parse PDF documents. It deals with – tables, free-flowing text.  

A document consists of multiple tables that are very similar in structure; hence our solution uses a Table Classification method to differentiate the acronym table from the rest. Various types of Statistical Methods were incorporated to quantify features/patterns that help define what an acronym will look like. This solution was used to classify an acronym table from the rest and then extract acronyms from the table.   

For free-flowing text, a similar technique has been used where the patterns/features of an acronym are incorporated to differentiate it from the rest of the free-flowing text. There are words extracted that can turn out to be acronyms. These words appear along with their expansion in the text. After extracting suspected acronyms, we quantify the words that consist of acronyms using statistical models and compare them to their expansions.  

By enforcing the following statistical models, 80% of acronyms are obtained that are present in a document. It is essential to accommodate variations in how text is written. Simple human punctuation errors can affect the entire acronym, not falling under rules of how acronyms are generally written. A dynamic method where custom rules that works with data from multiple domains are combined along with specific Statistical Models has been implemented that will uncommonly parse texts.  

On executing this dynamic method and testing various documents, we could conclude that the Statistical Model-based acronym extraction method has been performing with over 95% accuracy, even surpassing open source solutions provided by Spacy called Blackstone available in the market at the moment. Blackstone works on the techniques mentioned in a research paper written by Ariel S Schwartz et al. [2]., Multiple comparisons were made, between Blackstone and the Statistical-method based Acronym Extraction.


The Statistical Model-based acronym extraction method scanned an entire document of 100+ pages in milliseconds and displayed 98% accuracy. The average time taken to scan a document is a few seconds, and the accuracy of this product has been achieved between 94-98%. The product was tested on documents belonging to various domains, and it still yielded similar results. The product is developed on an experimental basis, and we are set to improve its efficiency and performance each day. There is plenty of room for improvement with subject to market changes. The product experiments with a set of Statistical models and custom rules, and the team is working on dynamic changes using AI that scans documents based on results. This product proves to be useful for lengthy and complicated engineering and medical documents. This product is one of its kind, and we are proud of our development.

At TVS Next, we re-imagine, design, and develop software to enable our clients to build a better world.  

How to Pivot to the Culture of Quality Assurance


The position of QA engineers has evolved from finding errors to enhancing user experiences. It indicates that QA engineers and testing must align themselves with business objectives and the larger picture.  

While technology plays a vital role in the entire quality ecosystem, here are five ways how it aids in improving quality processes.  

1. Advance data analysis and Machine Learning   

Artificial Intelligence (AI) is steadily gaining ground in the market. Machine learningsubset of AI, ‘learns’ programs by finding patterns from which results are predicted. An example may be a medical device company that analyzes complaint data to determine when a problem occurs. All complaint data are stored in a centralized digital archive for consistency in identifying and mitigating the problem.  

20% of businesses have already made use of on-demand applications such as Internet of Things (IoT), Robotics Process Automation, AI, and Machine Learning, according to 2019 KPMG report. In the same survey, 52% of organizations estimated their technology spending to be increased in the next year.  

2. Value beyond regulatory compliance  

While compliance is critical, quality also affects business. Sometimes quality takes a band-aid approach to solve problems quickly and does not examine the root cause or prevent future problems. Some companies are sufficiently taking a forward-thinking approach for proactive management of quality. It is becoming a necessity in today’s regulatory landscape. Firstly, many businesses globally sell through several regulatory bodies’ jurisdictions. Although specific regulations overlap, the effort to improve overall quality is always rewarded. In comparison to solving a single problem, this significant picture is changing an organization.  

Quality goes beyond its department as part of this significant approach. It must be part of the culture of an organization that permeates all the activities of the business. Quality 2030 is subjected to remove departmental siloes between quality and production, between manufacturers, suppliers, and regulatory agencies. Quality 2050 is predicted to eliminate siloes. By digitizing software systems that interact through several divisions, businesses are now able to take steps in that direction to eliminate these siloes.  

3. Customer analysis to find and resolve problems  

Medical device or pharmaceutical service departments usually do not communicate with patients who benefit from their products. Healthcare is becoming customer-focused and applies to companies in the life sciences space that supply consumer goods. The 21st Century Cures Act requires patient data to collected in drug development processes. It is mentioned in the Cures Act.  

Companies learn to improve quality beyond regulations when talking to consumers. However, given that patients play an active role in their healthcare, their perception of pharmaceutical and medical device companies becomes essential.  

4. Harmonization and monitoring of processes to reduce total costs  

The importance of quality can never be overemphasized. Excellence for all organizations is a competitive priority. Cost and quality were considered as two different ends of the same continuum but this view has changed dramatically over time. Improved quality has reduced the total cost function on a long-term basis thanks to the continuous improvement of processes. Doing things right for the first time, managing transactions based on facts, and applying statistical process controls has led to better quality for organizations and reduced costs.  

5. Training and communication systems to build quality competence and culture  

Training and learning programs used by organizations are successful ways to address quality proactively. The problem could mean a significant overhaul of the company’s current processes and systems. For example, if a company has a different Quality Management System (QMS) for each site, data for those sites must be compiled manually to be used. Manual data input increases the likelihood of human error and compromises the integrity of the data. It is better to switch from paper-based or silo-based systems to digital, centralized ones.  

Digitization and automation would not remove quality jobs – only the value-added activities would return to the focus of work. The tasks that are eliminated are repetitive and monotonous.  


Like software development, QA testing is a constantly changing field, with new developments every day. The need for dedicated QA professionals is only growing, as businesses are recognizing the need to produce high-quality software in a competitive market. The success of their products and businesses as a whole will depend on quality assurance.   

The indispensable contribution of Big Data in the Healthcare industry



There’s no bigger business than the business of saving lives. And there hasn’t been a more pertinent time for businesses in healthcare to think out of the box to find solutions to pressing needs. As Centers for Disease Control and Prevention (CDC) reported, in 2012, about half of all adults, nearly 117 million people, worldwide, had chronic diseases and conditions such as heart disease, stroke, cancer, Type 2 diabetes, obesity and arthritis. The need is to prioritize prevention as much as finding cures for diseases as this is the only way to check their rampant spread. 

In a span of ten years, there has been a tremendous generation of data and the use of technology to analyze the same. This has given birth to a new industry. The industry of Big Data. By using Big Data effectively, healthcare businesses have found new ways of reducing the number of preventable deaths, curing disease and improving the quality of life, while cutting their business overheads and increasing profitability. Treatment modalities have transformed and that has a lot to do with the way healthcare professionals are using Big Data to make informed decisions about patient care. Now, the impetus is on understanding patient information better and quicker, to predict the onset of illnesses and to cure them in the early stages. 

The Inception 

One of the most tangible ways data has changed healthcare is in the method used to collect it. Electronic Health Records (EHR) are now a reality across most hospitals in the U.S. at a staggering 94% adoption rate and by this year, a centralized European Health Record system is likely to come into being. EHRs have eliminated the need for paperwork, reduced data duplicity and also allowed for better treatment tracking. Today, the novelty of EHRs has worn off as technology has gotten avant-garde. 

Telemedicine has been around for no less than four decades but mobile technology has changed the face of it. With video conference tools and wireless devices, remote yet personalized treatment has been made possible and this has significantly cut costs in healthcare. Patients save money on repeat visits to hospitals and doctors save on valuable time as remote treatment has made some facets of medical treatment location agnostic. Smart wearables have also made their way into the daily life of the common man and it isn’t uncommon for friends and peers to exchange personal data that is collected by means of these devices. Industry experts predict that there will soon come a time when doctors will rely on Big Data as step one in charting treatment plans. 

The very fact that some companies are looking to collect and analyze an intangible variable such as stress is a testament to difference Big Data can make. The adoption of preventive analysis, as opposed to traditional statistical analysis, is a clear sign of things to come. Prediction modeling, the basis of preventive analysis, creates a prediction algorithm or profile of an individual using techniques such as artificial intelligence to analyze data. This can lead to better and higher individual outcomes, improve the accuracy of predictive research, and lead to pharmaceuticals creating more effective drugs. 

5 ways Big Data is Changing Healthcare 

The healthcare industry is booming faster and the need to handle patient care and innovate drugs has risen synonymously. With the rise in such needs, industry adopts new technologies. One such significant shift in the future is the use of Big Data and analytics in the healthcare sector. 

Health monitoring 

Continuous body vital monitoring along with sensor data collection would allow healthcare providers to keep patients out of the hospital because they can detect possible health problems and provide treatment before the condition gets worse. 

Reduced expenses 

Insurance companies can save money by endorsing wearables and fitness trackers to ensure patients don’t waste time in hospital. This will save patient waiting times because the hospital already has enough staff and beds available as per the study. Predictive analytics also helps minimize costs by reducing hospital readmissions. 

Assisting high-risk patients 

Once all medical records are digitized, the ideal data can be obtained to recognize other patients’ patterns. This may consistently recognize patients entering hospital and recognize their medical conditions. This awareness can help improve care for these patients and provide insight into corrective steps to minimize repeated visits. 

Preventing human errors 

It has been noted several times that the doctors either administer a wrong drug or wrongly assign another medication. These errors may usually be minimized because Big Data can be used to evaluate consumer data and prescription medication. 

Healthcare developments 

Big Data will significantly support science and technology advancement. Artificial Intelligence, like IBM’s Watson, can be used to surf through multiple data in seconds to find solutions for different diseases 

The common thread that runs through the applications of Big Data is the ability to provide real-time analysis of data. When it comes to making a decision on health, time is definitely of the essence and further use of Big Data will help professionals and patients take quick calls without compromising on accuracy. 


While most of the Big Data generated is not currently completely used due to limitations of the toolset and funds, it is certainly the future. Invest in the future and using Big Data Analytics as part of an emerging Healthcare Industry by finding support from an established company like ours. 

Can you have Agile without Continuous Delivery

Can you have Agile without Continuous Delivery

Challenges During Agile Transition 

Organizations performing an agile transition are often unhappy with the results. Usually, they follow scrum structure and build cross-functional teams with all the skills required.  

Agile coaches and scrum masters explain the three foundations of scrum — transparency, inspection, and adaptation. They also help us understand the different scrum activities such as sprint, daily scrum, sprint planning, sprint analysis, and sprint retrospective.  

Teams are mentored on the value of quality improvement and they are demonstrated that every scrum procedure is an opportunity to inspect and adapt.  

Despite these efforts, the business value delivery seems to take far longer than expected and enterprises are not reaping the agile transformation rewards or promises.  

Software Development Evolution 

A usefully simplified description of the advances in software development are listed below: 

  • Waterfall claims that a team would only start getting the software ready for releases after all the release functionalities (i.e., full featureare created.  
  • Agile insists that the team will be ready to release its software throughout development. Most types of agile assume that this will happen regularly.  
  • Continuous delivery requires the team to keep the software always ready to be released during development. It is not traditional agile as it does not require waiting and making a special effort to build a releasing framework. 

Agile Software Development  

Agile software development is not a methodology in the strict sense of the term. It is more of a culture or an approach where you recognize the needs of the situation and adapt to it accordingly.  

This technology requires adaptive planning and evolutionary development in addition to early delivery. Hence, there is a need for continuous improvement as it encourages a flexible response to the changes in the surroundings.  

Applications of Agile: Simple Examples  

Agile software development has a variety of applications in diverse fields. You can sense the difference in the technologies involved in activities we do almost every day. Take the internet or mobile banking, for instance. Transferring funds from one account to another is a regular activity for any person or business. You need security layers to be in place to ensure the safety of your funds.  

Now, these security layers need constant updates so that hackers are not able to crack the code. Hence, based on feedback received from industry experts and consumers, the mobile banking app developers keep updating their systems. Therefore, you find something new every fifteen days or so. In the early days, there were only passwords as a layer of security. Now you have additional layers like grid combinations, 2-way authentication measures like OTP and so on. The process of improvisation is continuous.  

This is agile software for you. They cannot wait until something drastic happens. The process of delivery has to be continuous. Agile software specializes in identifying threats or problems before they materialize. Thus, it is ready with a solution beforehand. However, having a solution alone is not enough. The critical aspect is the delivery and utility of the solution. Thus, one can say that agile software is of no use unless there is continuous delivery.  

Hence, you can see that continuous improvement is always necessary for every industry.  

Continuous Delivery — A Subset of Agile 

The very definition of agile software development says that it is a group of software development procedures that are based on iterative and incremental development. There is a continuous evolvement that requires collaboration among various factors. Therefore, agile software cannot work without continuous delivery. There has to be a constant and continuous change in the circumstances.  

You can see the application of agile software in a project management process. Breaking down a large project into smaller doable actions is the best way to approach any project. In this way, you will always be ready to change your plan of action should anything go wrong somewhere in between. Web designing is also an excellent example of the application of agile. You keep on improvising the design to suit customer preferences. You gauge these preferences by interacting with the customers at frequent intervals. Thus, you can see that agile is all about flexibility and adaptability.  


This brings us back to the question Can you have agile without continuous delivery”? You can see that it is just not possible to do so. The principal characteristic of agile is adaptability. Now, adaptability means continuous change concerning the situation. When the situation demands a particular solution, you need to adapt. This is what makes agile an exciting piece of software development process.  

How Healthcare Technology is Transforming Patient Care?

Healthcare technology

Be it any field, today’s innovative technology is literally revolutionizing every industry, and healthcare is no exception. Digital innovation in healthcare is here to stay. Like most industries, healthcare is evolving with new technology.

Technology will transform different aspects of health care including diagnostics, treatments, and delivery of care in the future. Though technology has been the major force behind innovation in every industry, healthcare has been less affected by the rapid growth of technological innovation. However, this is changing.

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Can a Monolithic team handle Microservices?



As monolithic structures become too big to manage, many companies are drawn to break them down into microservices. It’s a worthwhile trip, but it’s not simple. To do this well, we need to start out with a simple service and then build services based on vertical capabilities that are essential for the company and are subject to frequent changes. Such services should at first be broad and ideally not depend on the remaining monolith. We should ensure that every migration step represents an improvement on the architecture as a whole.

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Kotlin For Android: A Boon To Developers



JetBrains, known for IntelliJ IDEA (Android Studio is based on IntelliJ), has introduced the Kotlin language. Kotlin is a statically-typed programming language that runs on the JVM. It can also be compiled to JavaScript source code. Kotlin has some amazingly cool features!

Kotlin is a great fit for developing Android applications, bringing all of the advantages of a modern language to the Android platform without introducing any new restrictions:

  • Compatibility: Kotlin is fully compatible with JDK 6, ensuring that Kotlin applications can run on older Android devices with no issues.
  • Performance: A Kotlin application runs as fast as an equivalent Java one, thanks to very similar bytecode structure.
  • Interoperability: Kotlin is 100% interoperable with Java, allowing to use all existing Android libraries in a Kotlin application.
  • Footprint: Kotlin has a very compact runtime library, which can be further reduced through the use of ProGuard.
  • Compilation Time: Kotlin supports efficient incremental compilation, so while there’s some additional overhead for clean builds, incremental builds are usually as fast as or faster than with Java.
  • Learning Curve: For a Java developer, getting started with Kotlin is very easy. The automated Java to Kotlin converter included in the Kotlin plugin helps with the first steps.

Kotlin has been successfully adopted by major companies, and a few of them have shared their experiences: Pinterest has successfully introduced Kotlin into their application, used by 150M people every month.

Basecamp’s Android app is 100% Kotlin code, and they report a huge difference in programmer happiness and great improvements in work quality and speed. Keepsafe’s App Lock app has also been converted to 100% Kotlin, leading to a 30% decrease in source line count and 10% decrease in method count.


Kotlin aims at creating a language that would be in line with the same principles. It drive tools to create something that helps developers with the tedious and mundane tasks, allowing them to focus on what’s truly important. And of course make the process as enjoyable and fun as possible.


The Kotlin’s goals are to be a language that is available on multiple platforms and this will always be the case. It will keep supporting and actively developing Kotlin/JVM (server-side, desktop and other types of applications), and Kotlin/JS. We are working on Kotlin/Native for other platforms such as macOS, iOS and IoT/embedded systems

When you run a Java application, the app is compiled into a set of instructions called Bytecode and runs in a virtual machine. Over the past several years, a number of new programming languages have been introduced to also run on the Java virtual machine. Whilst the resulting app looks the same for the virtual machine, the idea is the language features can help developers write simpler code and fix some of Javas issues.


Kotlin aims to fill that gap of a missing modern language for the Android platform. There are a few core tenets that Kotlin lives by; it strives to be:

  • Concise to reduce the amount of boilerplate code you need to write
  • Expressive to make your code more readable and understandable.
  • Safe to avoid entire classes of errors such as null pointer exceptions.
  • Versatile for building server-side applications, Android apps or frontend code running in the browser.
  • Interoperable to leverage existing frameworks and libraries of the JVM with 100 percent Java interoperability.