Let’s dive into the concepts of open banking and understand the drivers that are fueling this innovation, the challenges and threats it poses, and how banks and other players plan to transform and develop new revenue models through the open banking channel.

What is Open Banking?

Open banking, also known as ‘open bank data’, is a platform-based approach that is destined to stay and evolve. It is a banking practice that provides third-party financial service providers with open access to consumer banking, transaction, and other financial data. The consumer data is captured from banks and non-bank financial institutions through the use of application programming interfaces (APIs).

The Evolution of Open Banking

Financial institutions, since their inception, have been collecting precious information about their customers and their transactions, with little or no knowledge of how to harness this data to its effective value.

Today, financial institutions leverage the data to narrow down customers’ preferred choices and this includes everything from their favorite restaurant or coffee shop to which shops they buy most of their shirts. Financial institutions also capture non-consumer data known as meta-data from cash machines, branch locations, number of loans, mortgages, different account types, and volume of transactions. With all this data captured in heaps, it becomes easier to analyze customer preferences and suggest relevant products and services that could be of their interest.

Due to an increase of around 50% in access to additional customer data and an approximate 70% decrease in time to market, open banking is without a doubt garnering the most interest within the fintech industry.

If we think about the short term alone, open banking is expected to increase financial institutions’ revenue by at least 20%-30%. These numbers are jolting the fintech industry towards renewed innovation of banking and payment services, making it easier and more accessible for customers.

Conventional Banking Vs Open Banking

Driving Forces Behind Open Banking Adoption

Due to the global pandemic, the past few years have been quite challenging for financial institutions. This situation also built opportunities to innovate and introduce solutions that had the potential to drive a positive impact on their future profit goals.

1. Changing Customer Behavior and Expectation

Newer and older generations such as Generation Z or Generation Alpha, have distinctly different behavior and requirements, pushing financial institutions to rethink their process for creating and selling their products and services to them.

For instance, a bank has to consider whether the product or service they offer satisfies the customers’ needs or not. The shift from a product-centric approach to a customer-centric approach is important. This mindset has caused financial institutions to rethink and upgrade their offerings by keeping customer experience at the core of the product development process. Moreover, these days customers enjoy an unprecedented level of market transparency, and their satisfaction level goes beyond accepting a limited choice of products offered by their main bank. With exposure to frictionless user experiences, they can now quickly differentiate between a good and bad CX, and are now not in a state to accept anything mediocre.

2. Technology Fueled Innovation

Radical innovation in digital technology, exponential growth in smart devices, and the shift to instant payments, have opened new opportunities within financial services. Spurred on by the growth of APIs, they have now become the foundation of the entire open banking system. The integration of cloud-based platforms has further enhanced the agility, flexibility, and scalability of financial institutions’ abilities. Additionally, advancements in exponential technologies such as AI, real-time analytics, machine learning, and blockchain have further improved processes, services, and products across all levels.

3. Evolving Regulations

Governments across the globe have been ushered into taking a proactive approach to the “democratization” of financial products and services. Nudged on by EBA in the EU after the adoption of PSD2 in 2015, formally ushers in the concept of open banking. Regulation breeds innovations and naming the concept as ‘open’ denotes its explicit policy goal that the concept must be considered and adopted across all financial institutions. Compelling banks to make their proprietary data available to third-party providers.

4. Increased Competition

A large number of organizations – backed by technology giants like GAFA (Google, Amazon, Facebook, and Apple) – have entered the financial services market. These fintech organizations are providing quicker payment solutions, with seamless integration of cards, e-wallets, and other payment options fueling competition with the banks. As a matter of fact, these organizations are more ready and actively preparing to offer their services within the open banking ecosystem, further ramping up competition with banking institutions.

Unbundling of Banking Models

How Open Banking Will Take the Front Seat in the Financial Ecosystem

Currently, the ‘open revolution’ market consists of both: established financial institutions and new players. The range of applications begins from a ‘minimum approach’ that permits third-party access using APIs for the purpose of sharing selective data to ‘maximum implementation’ facilitates the integration of diverse functionalities by leveraging the Banking-as-a-Service platform (BaaS).

‘True’ open banking goes beyond the exchange of information and impacts the core elements of financial service providers including established processes and legacy core banking systems. They possess tremendous potential and allow players with varying needs to connect, therefore benefiting different bank types and the entire financial industry as a whole. The customers too benefit as they gain access to a wider range of products at a single touchpoint rather than reaching out to multiple service providers.

For some product categories like mutual funds, mortgage loans, or structured products, incorporating third-party products has been a common practice for banks for many decades thus far. This concept has also been applied to deposits, one of the most widely used products by bank customers and a major source of funding for banks.

Flexibility and a More Complex Competitive Environment

Driving Value for Stakeholders

The open banking ecosystem is geared toward a holistic benefit approach that considers its customers as well as the industry stakeholders. Outlined below are a few instances of value created by the innovation open banking platforms have adopted.

1. Flawless User Experience

Due to the potential convergence of open banking and artificial intelligence, user experience is undergoing an incredible digital transformation. The continuous influx of data across several sources enables service providers to determine the exact customer sentiments and requirements resulting in highly personalized financial offerings. Several tedious procedures are also expected to become simplified and automated. Through banking APIs, fintech firms offer users the opportunity to improve their financial lives through financial planning capabilities and insights based on their own data. Essentially, opening banking enables banks and similar financial institutions to create a unique financial profile for each customer according to their financial data. Allowing them to predict their consumption patterns and behavior to execute product customization more efficiently.

2. Real-Time Payments Facilitating Easier Treasury and Cash Management for SMEs

Open banking facilitates near-instantaneous payments, as third-party providers can bundle all payments within a single digital interface. Typically, SMEs don’t have their own treasury departments, unlike their bigger counterparts. Real-Time Payment (RTP) transforms treasury management services, driving value for SMEs through increased visibility of their cash flows and liquidity positions. RTP also speeds up the Peer to Peer (P2P) payments, bill payments, and e-commerce payments ecosystem.

3. Data Sharing Prompting Product Innovation and Financial Freedom

Open banking ensures that banks only share their customer’ data with authorized third parties. This will lead to the development of better financial products as organizations can leverage the data to extract customer insights and subsequently become more innovative and customer-centric.

4. APIs Enhancing Cross-Selling and Cost Optimization Opportunities

Open Banking offers banks the opportunity to blend product and service features offered by third-party providers to create their own offerings using APIs as a plug-and-play model. Tying together such readily available services from third-party providers and vice versa, banks can quickly improve customer service, boost customer loyalty, create new revenue streams, and decrease bank operating costs. Moreover, banks can mitigate the risk and expenses of experimenting with newer products simply by adopting the plug-and-play model of integrating APIs of third parties along with their core products on their digital platform.

5. Data Transparency

The need for building transparency might seem obvious, but each platform and disruptive technology comes with its own story and unique set of challenges. For open banking platforms, these challenges have given credence to the regulatory and similar competent authorities to focus on the need for building transparency by ensuring that the customers’ interests and rights are at the heart of all focus areas.

The potential impact of open financial data on GDP and how it varies according to different regions.

Risks and Challenges Banks Need to Consider to Succeed in the Open Banking Ecosystem

Although the advent of open banking has been largely positive for the financial sector, it has also opened up several new challenges and risks for banking institutions. Many of these will have far-reaching consequences for their business prospects, possibly reaching the point of existential crisis.

Let’s consider some of the key points:

1. Rise of New Competition

Leading banks are now being challenged by pure digital entities like GAFA. These fintech are attracting customers in heaps by providing unbundled, innovative, and engaging financial products and services. Meanwhile, many leading banks are still relying upon legacy systems, and if the threat is not addressed soon, will risk the prospect of losing their market share, greater customer churn, and increased pressure on margins.

2. Data Security

Sharing financial data through APIs to third-party providers bears the inherent risk of data security and breaches. The absence of industry-wide technical standards and data sharing protocols might leave operating processes vulnerable to security breaches and fraudulent activities. Using complicated interconnections of data access, banks need to invest heavily in security initiatives and risk mitigation, which often heavily impacts their bottom line. At the same time, banks cannot afford to miss out on the potential revenue generated by these data streams within the open banking ecosystem.

3. Risk of Commoditization

Due to open APIs, leading banks will face the risk of being commoditized. Reason: The elimination of several existing barriers to switching accounts and shopping around for other products based on price only. Banks face the likelihood that a significant portion of their customer base might turn to the convenience of digital aggregators, resulting in the migration of their accounts and the profit pools tied to them.

Sustaining Long Term Growth Through Business Transformation

The business transformation gained from adopting a platform-based open banking ecosystem will foster an environment that goes beyond incremental change and value delivery. It incorporates strategic choices that affect financial institutions’ growth – how they operate and the kind of improvements they can expect going forward.

Listed below are a few imperatives for creating long term growth for financial institutions:

• Improve the existing range of offerings by reinforcing the core through collaboration with third-party providers.
• Build new value propositions by incorporating customer needs and financial position within service integration. This will allow credit scoring, pricing of loans, and other products to be refined and curated on a more personal, almost one-to-one basis.
• Collaboration and partnership between banks, third-party providers, and merchants will create a marketplace-like ecosystem. Allowing financial products to be bundled along with other non-financial products leads to newer cross-selling opportunities.
• Diversify the traditional service portfolio by building strong API portfolios, boosting engagement with the developer community, and promoting cross-collaboration across marketplaces.
• Concentrate on the adoption of the Banking-as-a-Platform (BaaP) model with an API-enabled network of partners, allowing core services to be bundled with third-party providers – facilitating advisory, business management as well as traditional banking services.

It is clear that open banking is set to fundamentally alter the financial service landscape through innovative services and new business models. The emergence of fintech will bolster collaboration as well usher in a new ecosystem that will change the role of banks significantly. Also, there are several issues surrounding regulation and data privacy causing a varied approach toward implementation across countries. However, irrespective of their geography, the momentum gathered by open banking is high, requiring banks and other fintech institutions to increase collaboration with each to ensure success within this new emerging ecosystem.

NeoSOFT’s Use Cases

Financial institutions across the globe leverage our expert open banking capabilities to enhance their customer experience, boost innovation, and improve adherence to data security and governance. Take a glance at how our solutions have impacted clients…

Helping a leading bank enter new markets, extend its customer base and increase the volume of transactions.

NeoSOFT was tasked with helping the bank meet changing customer expectations by leveraging alternative tech solutions that help the client address their money management requirements. Our engineers devised solutions to establish fintech partnerships, facilitating an increase in account acquisition through APIs and growth in transaction volume

Facilitating high-velocity innovation through banking APIs and an API management platform for a renowned financial services provider.

The client wanted a defined organization-wide API strategy that aligns with overall business goals while maintaining autonomy. Our solutions enabled the client to build a single developer portal for all their branches to provide insight into API adoption patterns. Our team of engineers were also able to balance organization-wide governance and cross-geography oversight for better management.

Amplifying the API Management platform for one of the largest and most popular BFSI clients.

The requirement was to lay the foundation for loyalty-driving open banking services, increase compliance and accelerate internal integration to a secure API platform. Our solutions enabled the client to adhere to its regulatory obligations while delivering an innovative customer-facing service. Additionally, it also delivered a notable uptake in operational efficiency across the organization.

To achieve this your DevOps teams, structure & ecosystem should be well-oiled. Therefore it is critical to understand how to build an ideal CI/CD pipeline that will help to deliver features at a rapid pace.

Through this blog, we shall be exploring important cloud concepts, execution playbooks, and best practices of setting up CI/CD pipelines on public cloud environments like AWS, Azure, GCP, or even hybrid & multi-cloud environments.

HERE’S A BIRD’S EYE VIEW OF WHAT AN IDEAL CI/CD PIPELINE LOOKS LIKE

Let’s take a closer look at what each stage of the CI/CD involves:

Source Code:

This is the starting point of any CI/CD pipeline. This is where all the packages and dependencies relevant to the application being developed are categorized and stored. At this stage, it is vital to have a mechanism that offers access to some reviewers in the project. This prevents developers from randomly merging bits of code into the source code. It is the reviewer’s job to approve any pull requests in order to progress the code into the next stage. Although this involves leveraging several different technologies, it certainly pays off in the long run.

Build:

Once a change has been committed to the source and approved by the reviewers, it automatically progresses to the Build stage.

1) Compile Source and Dependencies The first step in this stage is pretty straightforward, developers must simply compile the source code along with all its different dependencies.

2) Unit Tests This involves conducting a high coverage of unit tests. Currently, many tools show whether or not a line of code is being tested. To build an ideal CI/CD pipeline, the goal is to essentially commit source code into the build stage with the confidence that it will be caught in one of the later steps of the process. However, if high coverage unit tests are not conducted on the source code then it will progress directly into the next stage, leading to errors and requiring the developer to roll back to a previous version which is often a painful process. This makes it crucial to run a high coverage level of unit tests to be certain that the application is running and functioning correctly.

3) Check and Enforce Code Coverage (90%+) This ties into the testing frameworks above, however, it deals with the output code coverage percent related to a specific commit. Ideally, developers want to achieve a minimum of 90% and any subsequent commit should not fall below this threshold. The goal should be to achieve an increasing percentage for any future commits – the higher the better.

Test Environment:

This is the first environment the code enters. This is where the changes made to the code are tested and confirmed that they’re ready for the next stage, which is something closer to the production stage.

1) Integration Tests The primary thing to do as a prerequisite is to run integration tests. Although there are different interpretations of what exactly constitutes an integration test and how they compare to functional tests. To avoid this confusion, it is important to outline exactly what is meant when using the term.

In this case, let’s assume there is an integration test that executes a ‘create order’ API with an expected input. This should be immediately followed with a ‘get order’ API and checked to see if the order contains all the elements expected of it. If it does not, then there is something wrong. If it does then the pipeline is working as intended – congratulations.

Integration tests also analyze the behavior of the application in terms of business logic. For instance, if the developer inputs a ‘create order’ API and there’s a business rule within the application that prevents the creation of an order where the dollar value is above 10,000 dollars; an integration test must be performed to check that the application adheres to that benchmark as an expected business rule. In this stage, it is not uncommon to conduct around 50-100 integration tests depending on the size of the project, but the focus of this stage should mainly revolve around testing the core functionality of the APIs and checking to see if they are working as expected.

2) On/Off Switches At this point, let’s backtrack a little to include an important mechanism that must be used between the source code and build stage, as well as between the build and test stage. This mechanism is a simple on/off switch allowing the developer to enable or disable the flow of code at any point. This is a great technique for preventing source code that isn’t necessary to build right away from entering the build or test stage or maybe preventing code from interfering with something that is already being tested in the pipeline. This ‘switch’ enables developers to control exactly what gets promoted to the next stage of the pipeline.

If there are dependencies on any of the APIs, it is vital to conduct testing on those as well. For instance, if the ‘create order’ API is dependent on a customer profile service; it should be tested and checked to ensure that the customer profile service is receiving the expected information. This tests the end-to-end workflows of the entire system and offers added confidence to all the core APIs and core logic used in the pipeline, ensuring they are working as expected. It is important to note that developers will spend most of their time in this stage of the pipeline.

ON/OFF SWITCHES TO CONTROL CODE FLOW

Production:

The next stage after testing is usually the production stage. However, moving directly from testing to a production environment is usually only viable for small to medium organizations where only a couple of environments are used at the highest. But the larger an organization gets, the more environments they might need. This leads to difficulties in maintaining consistency and quality of code throughout the environment. To manage this, it is better for code to move from the testing stage to a pre-production stage and then move to a production stage. This becomes useful when there are many different developers testing things at different times like QA or a new specific feature is being tested. The pre-production environment allows developers to create a separate branch or additional environments for conducting a specific test.

This pre-production environment will be known as ‘Prod 1 Box’ for the rest of this article.

Pre-Production: (Prod 1Box)

A key aspect to remember when moving code from the testing environment is to ensure it does not cause a bad change to the main production environment where all the hosts are situated and where all the traffic is going to occur for the customer. The Prod 1 Box represents a fraction of the production traffic – ideally around less than 10% of total production traffic. This allows developers to detect when anything goes wrong while pushing code such as if the latency is really high. This will trigger the alarms, alerting the developers that a bad deployment is occurring and allowing them to roll back that particular change instantly.

The purpose of the Prod 1 Box is simple. If the code moves directly from the testing stage to the production stage and results in bad deployment, it would result in rolling back all the other hosts using the environment as well which is very tedious and time-consuming. But instead, if a bad deployment occurs in the Prod 1 Box, only one host is needed to be rolled back. This is a pretty straightforward process and extremely quick as well. The developer is only required to disable that particular host and the previous version of the code will be reverted to in the production environment without any harm and changes. Although simple in concept, the Prod 1 Box is a very powerful tool for developers as it offers an extra layer of safety when they introduce any changes to the pipeline before it hits the production stage.

1) Rollback Alarms When promoting code from the test stage to the production stage, several things can go wrong in the deployment. It can result in:

• An elevated number of errors
• Latency spikes
• Faltering key business metrics
• Various abnormal and expected patterns

This makes it crucial to incorporate the concept of alarms into the production environment – specifically rollback alarms. Rollback alarms are a type of alarm that monitors a particular environment and is integrated during the deployment process. It allows developers to monitor specific metrics of a particular deployment and that particular version of the software for issues like latency errors or if key business metrics are falling below a certain threshold. The rollback alarm is an indicator that alerts the developer to roll back the change to a previous version. In an ideal CI/CD pipeline these configured metrics should be monitored directly and the rollback initiated automatically. The automatic rollback must be baked into the system and triggered whenever it determines any of these metrics exceed or fall below the expected threshold.

2) Bake Period The Bake Period is more of a confidence-building step that allows developers to check for anomalies. The ideal duration of a Bake Period should be around 24 hours, but it isn’t uncommon for developers to keep the Bake Period to around 12 hours or even 6 hours during a high volume time frame.

Quite often when a change is introduced to an environment, errors might not pop up right away. Errors and latency spikes might be delayed, unexpected behavior of APIs or a certain code flow of APIs doesn’t occur until a certain system calls it, etc. This is why the Bake Period is important. It allows developers to be confident with the changes they’ve introduced. Once the code has sat for the set period and nothing abnormal has occurred, it is safe to move the code onto the next stage.

3) Anomaly Detection or Error Counts and Latency Breaches During the Bake period, developers can use anomaly detection tools to detect issues however that is an expensive endeavor for most organizations and often is an overkill solution. Another effective option, similar to the one used earlier, is to simply monitor the error counts and latency breaches over a set period. If the sum of the issues detected exceeds a certain threshold then the developer should roll back to a version of the code flow that was working.

4) Canary A canary tests the production workflow consistently with expected input and expected outcome. Let’s consider the ‘create order’ API we used earlier. In the integration test environment, the developer should set up a canary on that API along with a ‘cron job’ that triggers every minute.

The cron job should be given the function of monitoring the create order API with expected input and hardcoded with an expected output. The cron job must continually call or check on that API every minute. This would allow the developer to immediately know when this API begins failing or if the API output results in an error, notifying that something wrong has occurred within the system.

The concept of the canary must be integrated within the Bake Period, the key alarms as well the key metrics. All of which ultimately links back to the rollback alarm which reverts the pipeline to a previous software version that was assumed to be working perfectly.

Main Production:

When everything is functioning as expected within the Prod 1 Box, the code can be moved on to the next stage which is the main production environment. For instance, if the Prod 1 Box was hosting 10% of the traffic, then the main production environment would be hosting the remaining 90% of that traffic. All the elements and metrics used within the Prod 1 Box such as rollback alarms, Bake Period, anomaly detection or error count and latency breaches, and canaries, must be included in the stage exactly as they were in the Prod 1 Box with the same checks, except on a much larger scale.

The main issue most developers face is – ‘how is 10% of traffic supposed to be directed to one host while 90% goes to another host?’. While there are several ways of accomplishing this task, the easiest is to transfer it at the DNS level. Using DNS weights, developers can shift a certain percentage of traffic to a particular URL and the rest to another URL. The process might vary depending on the technology being used but DNS is the most common one that developers usually prefer to use.

DETAILED IDEAL CI/CD PIPELINE

Summary

The ultimate goal of an ideal CI/CD pipeline is to enable teams to generate quick, reliable, accurate, and comprehensive feedback from their SDLC. Regardless of the tools and configuration of the CI/CD pipeline, the focus should be to optimize and automate the software development process.

Let’s go Over the key Points Covered One More Time. These are the key Concepts And Elements that Make up an Ideal CI/CD Pipeline:

• The Source Code is where all the packages and dependencies are categorized and stored. It involves the addition of reviewers for the curation of code before it gets shifted to the next stage.
• Build steps involve compiling code, unit tests, as well as checking and enforcing code coverage.
• The Test Environment deals with integration testing and the creation of on/off switches.
• The Prod 1 Box serves as the soft testing environment for production for a portion of the traffic.
• The Main Production environment serves the remainder of the traffic

NeoSOFT’s DevOps services are geared towards delivering our signature exceptional quality and boosting efficiency wherever you are in your DevOps journey. Whether you want to build a CI/CD pipeline from scratch, or your CI/CD pipeline is ineffective and not delivering the required results, or if your CI/CD pipeline is in development but needs to be accelerated; our robust and signature engineering solutions will enable your organization to

• Scale rapidly across locations and geographies,
• Quicker delivery turnaround,
• Accelerate DevOps implementation across tools.

NEOSOFT’S DEVOPS SERVICES IMPACT ON ORGANIZATIONS

Solving Problems in the Real World

Over the past few years, we’ve applied the best practices mentioned in this article.

Organizations often find themselves requiring assistance at different stages in the DevOps journey; some wish to develop an entirely new DevOps approach, while others start by exploring how their existing systems and processes can be enhanced. As their products evolve and take on new characteristics, organizations need to re-imagine their DevOps processes and ensure that these changes aren’t affecting their efficiencies or hampering the quality of their product.

DevOps helps eCommerce Players to Release Features Faster

When it comes to eCommerce, DevOps is instrumental for increasing overall productivity, managing scale & deploying new and innovative features much faster.

For a global e-commerce platform with millions of daily visitors, NeoSOFT built their CI/CD pipeline. Huge computational resources were made to work efficiently, giving a pleasing online customer experience. The infrastructure was able to carry out a number of mission-critical functions with substantial savings resulting in both: time and money.

With savings up to 40% on computing & storage resources matched with an enhanced developer throughput, an ideal CI/CD pipeline is critical to the eCommerce industry.

Robust CI/CD Pipelines are Driving Phenomenal CX in the BFSI Sector

DevOps’ ability to meet the continually growing user needs with the need to rapidly deploy new features has facilitated its broader adoption across the BFSI industry with varying maturity levels.

When executing a digital transformation project for a leading bank, NeoSOFT upgraded the entire infrastructure with an objective to achieve continuous delivery. The introduction of emerging technologies like Kubernetes into the journey enabled the institution to move at startup speed, driving the GTM 10x faster rate.

As technology leaders in the BFSI segment look to compete through digital capabilities, DevOps & CI/CD pipelines start to form their cornerstone of innovation.

A well-oiled DevOps team, structure, and ecosystem can be the difference-maker in driving business benefits and leveraging technology as your competitive edge.

Begin your DevOps Journey Today!

Speak to us —let’s Build.

What is price discrimination? Why does it take place? How can you get the best prices for a product/service? We’ll answer all these questions in the post. So, settle down and read!

By its literal definition, dynamic pricing is an approach by which businesses sell the same product at variable prices to different customers.

But how does it happen?

Let’s find out!

Well, we all use the internet to buy various products, for online booking and to leverage various services. Generally, when we browse the web, our information like location, device, browser and demographics is left behind in the cloud. This data is then used by companies to set the ‘ideal’ price or the price that we can afford for a particular product/service. The companies use various factors to find our financial power and then set the ideal price.

Now, let’s understand it better how dynamic pricing takes place.

1. Price discrimination based on location

Many companies track the geographic locations of users and exploit machine learning algorithms to set the ideal price. For example, users placing an order from developed countries like the US will have to pay a higher price than those users from developing or under-developed countries.

2. Price based on devices

Users can place an order from multiple devices like the laptop, mobile phone, tablet, etc. Hence, many companies use the ‘device type’ as a metric to set price. For example, users that make a purchase via iPhone will be charged more than the users with android phones.

3. Price based on time of purchase

Arrays of companies follow the practice of charging users based on the time they’ll make a purchase. For example, prices for commodities are higher during festivities while the prices may be lowered when the commodities are reaching their expiry period.

4. Segmented pricing

Many times, companies gauge the ‘willingness of buyers to pay more for the specified product/service’ to set the ideal price. For example, a product with a warranty may be charged higher. Similarly, the customers that expect faster service will be charged higher prices than others.

5. Peak user pricing

This is one of the most common strategies of dynamic pricing. Under this strategy, users have to pay higher prices for the same product/service at peak hours. For example, airlines and other transportation companies will charge higher during rush hours i.e. weekdays while the charges might be lower at weekends.

These were some strategies that companies exploit to exercise dynamic pricing. However, as a user, we all have to suffer. Just because we earn an iPhone and live in a developed country, doesn’t mean we will be happy to pay more. Everyone like savings.

So, what’s the solution? How can you land with the best deal and avoid dynamic pricing?

This is where PricingBlocker steps in. It is a robust browser extension that will enable you to get better prices for products/services. It does so by blocking your information so that it is not shared on the web. Furthermore, it also optimizes the information that you’ll share on the web. In a nutshell, the extension allows you to shop anonymously. In this way, companies won’t be able to track your financial power and you’ll be charged with normal pricing for the product/service.

Some of the key features of this tool are:

• It blocks ads
• The extension blocks Geo location tracking
• It facilitates Incognito mode
• It offers proxy anonymization
• The extension helps in switching browser, browser language
• It offers Operating System Switcher
• It offers the timestamp optimization
• The extension works on most of the websites including Airbnb, Air Asia, Amazon, Ali Express and Agonda.

As you can see this extension works wonders. All you have to do is download the extension from chrome store, install it and let it unfold its magic!

Source: Pricing Blocker

Every other day we discover a new online application that tries to make our lives more convenient. And as soon as we get to know about it, we register ourselves for that application without giving it a second thought. After the one-time registration, whenever we want to use that app again, we just need to log in with our user name and password —the app or system automatically remembers all our data that was provided during the registration process. Ever wondered how a system is able to identify us and recollect all our data on the basis of just a user name and password? It’s all because of the database in which all our information or data gets stored when we register for any application.

Similarly, when we browse through millions of product items available on various online shopping applications like Amazon, or post our selfies on Facebook to let all our friends see them, it’s the database that is making all this possible.

According to Wikipedia, a database is an organised collection of data. Now, why does data need to be in an organised form? Let’s flash back to a few years ago, when we didn’t have any database and government offices like electricity boards stored large heaps of files containing the data of all users. Imagine how cumbersome it must have been to enter details pertaining to a customer’s consumption of electricity, payments made or pending, etc, if the names were not listed alphabetically. It would have been time consuming as well. It’s the same with databases. If the data is not present in an organised form, then the processing time in fetching any data is quite long. The data stored in a database can be in any organised form—schemas, reports, tables, views or any other objects. These are basically organised in such way as to help easy retrieval of information. The data stored in files can get lost when the papers of these files get older and, hence, get destroyed. But in a database, we can store data for millions of years without any such fear. Data will get lost only when the system crashes, which is why we keep a backup.

Now, let’s have a look at why any application needs a database.

1. It will be difficult for any online app to store huge amounts of data for millions of its customers without a database.
2. Apart from storing data, a database makes it quite easy to update any specific data (out of a large volume of data already residing in the database) with newer data.
3. The data stored in a database of an app will be much more secure than if it’s stored in any other form.
4. A database helps us easily identify any duplicate set of data present in it. It will be quite difficult to do this in any other data storage method.
5. There is the possibility of users entering incomplete sets of data, which can add to the problems of any application. All such cases can be easily identified by any database.

A user cannot directly interact with any database—there needs to be an interface or intermediate system, which helps the user to interact with it. Such an interface is referred to as a database management system (DBMS). It is basically a computer software application that interacts with the user or other applications, and even with the database itself, in order to capture and analyse the data. Any DBMS such as MySQL is designed in such a way that it allows the definition, querying, creation, updation and administration of the whole database. It is where we request the database to give us the required data in the query language.

Different types of databases

Relational database: This is one of the most common of all the different types of available databases. In such types of databases, the data is stored in the form of data tables. Each table has a unique key field, which is used to connect it to other tables. Therefore, all the tables are related to each other with the help of several key fields. These databases are used extensively in different industries and will be the type we are most likely to come across when working in IT.

Operational database: An operational database is quite important for organisations. It includes the personal database, customer database and inventory database, all of which cover details of how much of any product the company has, as well as the information on the customers who buy the products. The data stored in different operational databases can be changed and manipulated based on what the company requires.

Data warehouses:Many organisations are required to keep all relevant data for several years. This data is also important for analysing and comparing the present year data with that of the previous year, to determine key trends. All such data, collected over years, is stored in a large data warehouse. As the stored data has gone through different kinds of editing, screening and integration, it does not require any more editing or alteration.

Distributed databases:Many organisations have several office locations—regional offices, manufacturing plants, branch offices and a head office. Each of these workgroups may have their own set of databases, which together will form the main database of the company. This is known as a distributed database.

End user databases:There is a variety of data available at the workstation of all the end users of an organisation. Each workstation acts like a small database in itself, which includes data in presentations, spreadsheets, Word files, downloaded files and Notepad files.

Choosing the right database for your application

Choosing the right database for an application is actually a long-term decision, since making any changes at a later point can be difficult and even quite expensive. So, we cannot even afford to go wrong the first time. Let’s see what benefits we will get if we choose the right database the first time itself.

1. Only if we choose the right database will the relevant and the required information get stored in the database, putting data in a consistent form.
2. It’s always preferable that the database design is normalised. It helps to reduce data redundancy and even prevents duplication of data. This ultimately leads to reducing the size of the database.
3. If we choose the correct database, then the queries fired in order to fetch data will be simple and will get executed faster.
4. The overall performance of the application will be quite good.
5. Choosing the right database for an application also helps in easy maintenance.

Factors to be considered while choosing the right database for your application

Well, there is a difference between choosing any database for an application and choosing the right database for it. Let’s have a look at some of the important factors to be considered while choosing a database for an application.

Structure of data: The structure of the data basically decides how we need to store and retrieve it. As our applications deal with data present in a variety of formats, selecting the right database should include picking the right data structures for storing and retrieving the data. If we do not select the right data structures for persisting our data, our application will take more time to retrieve data from the database, and will also require more development efforts to work around any data issues.

Size of data to be stored: This factor takes into consideration the quantity of data we need to store and retrieve as critical application data. The amount of data we can store and retrieve may vary depending on a combination of the data structure selected, the ability of the database to differentiate data across multiple file systems and servers, and even vendor-specific optimisations. So we need to choose our database keeping in mind the overall volume of data generated by the application at any specific time and also the size of data to be retrieved from the database.

Speed and scalability: This decides the speed we require for reading the data from the database and writing the data to the database. It addresses the time taken to service all incoming reads and writes to our application. Some databases are actually designed to optimise read-heavy applications, while others are designed in a way to support write-heavy solutions. Selecting a database that can handle our application’s input/output needs can actually go a long way to making a scalable architecture.

Accessibility of data: The number of people or users concurrently accessing the database and the level of computation involved in accessing any specific data are also important factors to consider while choosing the right database. The processing speed of the application gets affected if the database chosen is not good enough to handle large loads.

Data modelling:This helps map our application’s features into the data structure and we will need to implement the same. Starting with a conceptual model, we can identify the entities, their associated attributes, and the entity relationships that we will need. As we go through the process, the type of data structures we will need in order to implement the application will become more apparent. We can then use these structural considerations to select the right category of database that will serve our application the best.

Scope for multiple databases:During the modelling process, we may realise that we need to store our data in a specific data structure, where certain queries cannot be optimised fully. This may be because of various reasons such as some complex search requirements, the need for robust reporting capabilities, or the requirement for a data pipeline to accept and analyse the incoming data. In all such situations, more than one type of database may be required for our application. When choosing more than one database, it’s quite important to select one database that will own any specific set of data. This database acts as the canonical database for those entities. Any additional databases that work with this same set of data may have a copy, but will not be considered as the owner of this data.

Safety and security of data: We should also check the level of security that any database provides to the data stored in it. In scenarios where the data to be stored is highly confidential, we need to have a highly secured database. The safety measures implemented by the database in case of any system crash or failure is quite a significant factor to keep in mind while choosing a database.

A few open source database solutions available in the market

MySQL

MySQL has been around since 1995 and is now owned by Oracle. Apart from its open source version, there are also different paid editions available that offer some additional features, like automatic scaling and cluster geo-replication. We know that MySQL is an industry standard now, as it’s compatible with just about every operating system and is written in both C and C++. This database solution is a great option for different international users, as the server can provide different error messages to clients in multiple languages, encompassing support for several different character sets.

Pros

• It can be used even when there is no network available.
• It has a flexible privilege and password system.
• It uses host-based verification.
• It has security encryption for all the password traffic.
• It consists of libraries that can be embedded into different standalone applications.
• It provides the server as a separate program for a client/server networked environment.

Cons

• Different members are unable to fix bugs and craft patches.
• Users feel that MySQL no longer falls under the category of a free OS.
• It’s no longer community driven.
• It lags behind others due to its slow updates.

SQLite

SQLite is supposedly one of the most widely deployed databases in the world. It was developed in 2000 and, since then, it has been used by companies like Facebook, Apple, Microsoft and Google. Each of its releases is carefully tested in order to ensure reliability. Even if there are any bugs, the developers of SQLite are quite honest about the potential shortcomings by providing bug lists and the chronologies of different code changes for every release.

Pros

• It has no separate server process.
• The file format used is cross-platform.
• It has a compact library, which runs faster even with more memory.
• All its transactions are ACID compliant.
• Professional support is also available for this database.

Cons

It’s not recommended for:

• Different client/server applications.

All high-volume websites.

• High concurrency.
• Large datasets.

MongoDB

MongoDB was developed in 2007 and is well-known as the ‘database for giant ideas.’ It was developed by the people behind ShopWiki, DoubleClick, and Gilt Group. MongoDB is also backed by a large group of popular investors such as The Goldman Sachs Group Inc., Fidelity Investments, and Intel Capital. Since its inception, MongoDB has been downloaded over 15 million times and is supported by more than 1,000 partners. All its partners are dedicated to keeping this free and open source solution’s code and database simple and natural.

Pros

• It has an encrypted storage engine.
• It enables validation of documents.
• Common use cases are mobile apps, catalogues, etc.
• It has real-time apps with an in-memory storage engine (beta).
• It reduces the time between primary failure and recovery.

Cons

• It doesn’t fit applications which need complex transactions.
• It’s not a drop-in replacement for different legacy applications.
• It’s a young solution—its software changes and evolves quickly.

MariaDB

MariaDB has been developed by the original developers of MySQL. It is widely used by tech giants like Facebook, Wikipedia and even Google. It’s a database server that offers drop-in replacement functionality for MySQL. Security is one of the topmost concerns and priorities for MariaDB developers, and in each of its releases, the developers also merge in all of MySQL’s security patches, even enhancing them if required.

Pros

• It has high scalability with easier integration.
• It provides real-time access to data.
• It has the maximum core functionalities of MySQL (MariaDB is an alternative for MySQL).
• It has alternate storage engines, patches and server optimisations.

Cons

• Password complexity plugin is missing.
• It does not support the Memcached interface.
• It has no optimiser trace.