What Is a Recommender System and What Are Its Types?

Recommender systems were conceptualized due to the growing interactions and activities of users on the internet. Online spaces allow users to freely indulge in their favorite activities. For instance, consider IMDB (Internet Movie Database). Binge watchers visit it and click a rating out of 10 to offer their insights on the movie’s quality. This approach gathers feedback through “ratings.” Other metrics for feedback include the browsing of a term which calculates the popularity of a product.

These metrics are used by organizations in recommender systems to determine consumer habits. The objective is to enrich the user experience. In the nomenclature of recommender systems, a product is an “item” and the individual who uses the recommender systems is a “user.”

Recommender systems rely on the concept that there is a strong correlation between the activities of a user and an item. For instance, if a user likes to purchase graphic cards for gaming, then it is more likely that their other activities would be to purchase a gaming mouse, headphones, or any other gaming-related equipment. Thus, their choices can be used to show them more and more relevant recommendations while companies are able to provide unmatched levels of personalization.

Types of Recommender Systems

Recommender systems are classified under the following categories.

Collaborative Filtering

In collaborative filtering, when several users provide their feedback, for instance in the form of ratings, then their “insights” are computed to generate recommendations. The resulting ratings matrices of collaborative filtering are known to be sparse. For instance, suppose there is a movie website in which users assign ratings to reflect their likes/dislikes. Now, if the movie is a popular one, like The Shawshank Redemption, then it is expected to receive a massive number of ratings.

However, each movie has a limited audience. The audience that has seen and rated the movie is grouped as observed or specified ratings while those who have not watched the movie are labeled as unobserved or unspecified ratings.

Collaborative filters work on the foundation that the unobserved ratings may also be imputed. This is done by using the high correlation between items and users. For instance, consider two users Phil and Dan. Both of them like the movies starring Leonardo Di Caprio, and therefore share identical taste. When their ratings get too similar, then the model uses it to calculate their recommendations. This means when there is an item in which only Phil has submitted a rating, then Dan’s rating could be estimated through Phil’s choice and therefore Dan is shown that movie as a recommendation.

Content Based Recommender Systems

When an item’s descriptive characteristics are analyzed and computed for the generation of recommendations, then it is likely that the recommender system is based on content. The term here is “descriptions.” Such systems aggregate the purchasing behaviors and ratings which are recorded from the users and mixed with an item’s description (content).

For instance, considering a movie website, Mark has given a positive rating to the movie “Sixth Sense.” However, data from other users is not available so collaborative filtering is inapplicable. Instead, the recommender system can use content-based methods to go through the description of the movie, like its genre (suspense). Therefore, the system would recommend similar suspense-based movies to the user afterward.

In such systems, the ratings and descriptions of the items are employed as training data for the generation of a regression model or classification (user-specific). Whenever users are assessed, the training data matches their buying history with the description of items.


In scenarios where users are not too active with the purchase of items, knowledge-based systems exist as a useful tool. For instance, when a user buys real estate, then this purchase is commonly long-term. On a similar note, buying automobiles or luxury products are those activities which occur once in a while for a user. As you can understand, there are not enough ratings to offer the users with recommendations. Similarly, the description of such items also changes with the passage of time as the models and versions of these items may also change.

Knowledge-based systems do not consider ratings to generate recommendations. Instead, they attempt to find a link between the item descriptions and the requirements of a user. This approach makes use of “knowledge bases.” Knowledge bases store the similarity functions and set of rules that assist them in retrieving the right item.

For example, if a user intends to purchase a car, then the knowledge-based systems would provide recommendations based on the specifications of the car like the model year of the car, model type (sedan, convertible, hatchback etc), color, price, and other car-related attributes.

Demographic Recommender Systems

At times, the data from the demographics of users is used for designing classifiers which apply a mapping between the purchasing behavior and ratings of the user to their demographics. These systems thrive on the idea that demographic data can prove valuable in the recommendation of items. For instance, a website can show web page recommendations by identifying the interaction of a demographic with a specific web page. Often, a context is added in such systems to further improve their recommendations.

For example, consider a website which provides book recommendations to its users. The user attributes are gathered through an interactive dialog box. The system would account factors like the age, country, language, occupation, status (student/employed/owner), and other relevant factors. For instance, if a user is between the ages of 5-10, then its suggestions would gear towards children books. Likewise, if a user works as a finance analyst, then the system would use to show finance related books. On a similar note, if a user lives in France, then he/she would receive French book recommendations.

Final Thoughts

Recommender systems have become one of the most in-demand applications of machine leaning. Amazon, Netflix, Google, Facebook, every organization uses recommendation system to improve the user experience. Therefore, if you deal extensively with customers, then you might succeed in improving the productivity and efficiency of your business through the installation of recommender systems.

Revolutionizing Call Centers with Speech Analytics

As the world entered into a new millennium in the year 2000, few people would have predicted how the next 19 years would completely change the face of several industries. All this transformation was made possible through the rising IT juggernaut. One such industry was the call center industry. Traditionally, the industry focused on dealing with as many customers as it could within a certain time period. Times have changed since then, and now the objective is to get the approval of customers by fixing their issues and generate quality CX (customer experience).

This paradigm shift was supported by speech analytics, which refers to a practice in which the voice of customers undergoes analysis and processing. This voice is converted into the text where analytics identify patterns in the recorded data. In this way, customer feedback like complaints, recommendations, suggestions, and other useful factors are collected easily, which are then applied to enhance the services and products of organizations.

Therefore, unsurprisingly, speech analytics has become one of the major reasons behind the resurgence of customer satisfaction and retention for many organizations. According to MarketsandMarket, by 2020, the speech analytics industry is estimated to balloon up to $1.60 billion.

Today, organizations harness speech analytics for raising the standards of their conversations, insights, and outcomes. If we examine through a historical outlook, then it was always the norm for organizations to comprehend and recognize the requirements of customers and adjust their offers accordingly.

For instance, Henry Ford got a breakthrough in the automotive industry when he divided the major issues at hand and went through their individual analysis. Then, he began implementing procedures that would supplement the efficiency of his organization. Ford’s approach was not too dissimilar to the modern-day analytics.

The Change in Mindset

Previously, call center agents were involved in repetitive outbound calls to their potential and actual clients. They received queries from inbound calls and the actual emphasis was on the length of the call. However, in this age, the emphasis has shifted towards customer engagement and overall experience. Organizations now understand that offering solutions to identified issues can help in fostering loyalty with customers and maximizing their own revenues.

Consider your surroundings to analyze accepted and normal behaviors. For instance, have you noticed how invested people have become lost in their smartphone screens? It is hard to imagine how days went by before the invention of the smartphone. Interpret the usage of IT and its impact on daily human interactions. Likewise, get a grip on the social media frenzy which has become the primary medium for communication.

Today, industries are actively pursuing speech analytics to boost their operations. The older voice solutions like IVR (Interactive Voice Recognition) have been shunned a long time ago. This is the speech analytics age where the technology is powerful enough to grasp the message of a customer and facilitate the management to respond with a workable and effective solution.

What Did Speech Analytics Do?

Speech analytics has triggered a revolution for call center agents through the following benefits.

Locating Customer Issues

The operational nature of call centers vary. However, standard practices are applied by all. It is important to understand that these practices may not be the best replacements. On the other hand, speech analytics ensures that a call center is able to mark the exact points from where customer grievances arise. Likewise, it can then guide agents to respond to those issues.

For instance, a customer purchases a winter jacket from an e-commerce website. After receiving the jacket, he might find its quality subpar or that the color does not match his order description. In response, the agitated customer calls for the cancellation of the order and demands a refund. Speech analytics can be used here to identify the common issues in these orders which can not only improve the service but also assist in adding a new feature in the product as a result, the churn cycle could be minimized to an extent.


There is a strong relationship between the sales and marketing departments of organizations, and therefore they are interdependent. With speech analytics, marketing experts can take notes from the calls of sales agents which can assist them in “connecting” to their customers.

For instance, speech analytics pinpoints to the high pricing of a product as a major issue for a wide majority of customers who are primarily students. The marketing department can use this piece of information to promote an advertisement based on discounts for those students. Consequently, orders pour in and the marketing department scores an important victory.

The marketing department views speech analytics as a formidable tool which can provide it with the demographics of customers like their occupation, gender, age, location, etc. Hence, their marketing strategies are adjusted accordingly.


One of the most productive uses of speech analytics is to allow supervisors and managers to train their calling agents. The performance of those agents is duly assessed by going through these calls. Proper analytics can also recognize if a particular agent requires further training or not. Likewise, it can serve as the basis of promotion for an agent whose output was not noticed before.

Minimizing the Churn Rate

The loss of clients is referred to as the customer churn. Customer churn is an extremely important factor for organizations, especially the SMEs. Presently, consumer habits are ever-changing which means that same product which generated positive results in 2017 may not generate the same results today. This is where speech analytics can be leveraged to identify the changing trends so businesses can comprehend and tailor their processes accordingly for better results.

Final Thoughts

The inception of speech analytics has disrupted the conventional call center industry. Businesses are using it to increase their revenues. If you do not use it in your operations, then do give a thought or two about integrating it in your business processes.

How Do Search Engines Work?

Search engines are used on a daily basis by people all over the world. Individuals living in both first world, as well as third world countries, frequently rely on search engines to answer all of their questions. Whether you’re looking for the nearest restaurant or a new product to buy, you’re likely to use Google, Bing, or another search engine for this purpose. Have you ever wondered how do search engines skim through tons of data to offer you the best possible results? Well, here’s a quick look at how search engines work.


When a user types a search query, the search engine picks up the most relevant web page and sends it back to the user as a response. This web page has to be stored somewhere. To do this, search engines maintain a large index. The index assesses and arranges each website and its web pages so they can be linked with the phrases and words which are searched by users.

Additionally, the index has to put a specific rating for pages that are associated with a specific topic. To do this, they have to maintain a standard for quality so users can get the most relevant and useful content from search engines. For example, suppose a user searches “How to learn Java?” Such a topic is already discussed by different websites so how does the search engine know which pages to show to a user? To do this, search engines have designed their own criteria.


Website crawlers are programs that serve as the primary tool for search engines. They are responsible for recognizing the pages in the “Web.” Whenever a page is identified by a crawler, it is “crawled.” Crawl refers to the analysis of the page during which the crawler gathers and accumulates different types of data from the web page. It then adds the page in the index after which the hyperlinks of the page are utilized to go to other websites and the process is repeated. This process is similar to the life of a spider which weaves a web and crawls from one place to another easily. Hence, crawlers are also called as spiders. Web administrators can make their web pages more readily available to crawlers through two techniques.

  • Sitemap – A sitemap contains a hierarchical view of the complete website with topics and links which makes it easy for crawlers to navigate all the pages of a website.
  • Internal Links – Website administrators place hyperlinks in the web content which direct to the internal web pages across the entire website. When crawlers come across such links, they are able to crawl through them, thereby improving their efficiency.


One of the most important considerations for an index is to assign a relative rating to all the stored web pages. When a crawler finds 20,000 relevant web pages for a user query, then how is the search engine suppose to choose the ranking results?

In order to address these concerns, search engines like Google use an extensive list of algorithms. Examples are RankBrain and Hummingbird. Each search engine has a think-tank which initially decided on how to rank a web page. This understanding takes some months before a large algorithm is designed to rank websites.

How to Rank?

Search engines rank a website after considering multiple factors. Some of the common factors are the following.


If there are two websites A and B with the same attributes other than the fact that A was designed earlier than B, then A holds greater importance for the search engines. Search engines favor older websites and see them as reliable and authentic.


Perhaps the most important metric for search engines is to assess a keyword. When search engines look for the best results in terms of relevancy, they go through a list of words and phrases. These words are popularly known as keywords. The importance of keywords has to lead to the creation of a separate study known as keyword analysis.

Mobile Optimization

Search engines like Google have made it clear that if a website loads and works well on desktop PC then it is not enough. Nowadays mobiles and smart devices have swarmed the world. Wherever you go, everyone has a mobile in hand. Therefore, mobile optimization is necessary for search engines.


External links remain as one of the most crucial metrics for search engines to rank a page. If your web page is linked by a credible and established website, then the worth of your page increases in the eyes of the search engines. It conveys them a message that your web page has informative and high-quality content that made others reference your website. The more external links a web page receives, the more quickly the website gains a boost in its online ranking.


How often have you opened a website, only to be annoyed by the slow loading speed? Factors like these also weigh on ranking. When crawlers visit a link and find slow loading, they record it and inform the search engine. Consequently, the search engines cut off your mark in ranking and your page will lose its online ranking. Likewise, search engines monitor the number of instances where a user enters your web page and immediately leaves it, thereby signaling the possibility of slow loading.

The Birth of a New Field

Brick-and-mortar businesses are a thing of the past. Today, every brand has gone digital to market and advertise their services. This has given rise to the creation of an in-demand field known as SEO (Search Engine Optimization). Since every website competes to rank well on search engines, mainly Google, website owners have to understand how search engines work. To do this, there are several SEO strategies which are formed on the basis of search engine metrics and allow businesses to rank high on search engines.

What Is Bean in Spring? What is the Scope and Lifecycle of Bean?

What Is Bean in Spring? What is the Scope and Lifecycle of Bean?

To learn about Java Spring, it is necessary to understand what a bean is. Similarly, learn about the scope and lifecycle of a bean.

What Is a Bean in Spring

In Spring, the Spring IoC container manages objects which powers the entire application. These objects are known as beans. All objects which Spring IoC instantiates assembles, and supervises fall into the category of a bean.

You can create a bean by providing the configuration metadata to your container. A bean has the following properties.

  • class: It is a mandatory attribute and defines the bean class which will be required for the generation of the bean.
  • name: It defines a unique name for the bean identifier. If you are working with configuration metadata which is powered by XML, you can utilize the name or id to define the bean identifier.
  • Scope: It defines the scope for all those objects which are generated through a single bean definition.
  • constructor-arg: It is used for the dependency injection.
  • properties: It is used for the dependency injection.
  • autowiring mode: It is used for the dependency injection.
  • lazy-initialization mode: By using a lazy-initiated bean, you can generate an instance of bean whenever the first request comes as opposed to the bean creation at the startup.
  • initialization method: It is used as a callback when the IoC container defines the mandatory properties of the bean.
  • destruction method: It is a callback which is used when the container which holds the bean is eliminated.

Scope Of Bean

While specifying a bean, you can also define its scope. For instance, you can set a “prototype” attribute for a bean’s scope; this means that the Spring has to generate a new instance of bean whenever it is needed. On a similar note, you can use the “singleton” attribute if you need to return bean instances in accordance with your requirements. In total, there are five bean scopes.

  • singleton: It defines a single bean instance for each Spring IoC container.
  • prototype: It defines a single bean instance with multiple instances for an object.
  • request: It defines an HTTP request for a bean.
  • session: It defines an HTTP session for a bean.
  • global-session: It defines a global HTTP session for a bean.


When a bean’s scope is defined as a singleton, then precisely one object instance is generated by the Spring IoC container. The instance is then saved in a cache which is used to store all the beans associated with the singleton scope. Afterward, any reference or request which comes for that bean sends back the cached object. By default, the scope of a bean is always set to a singleton. In case, you only require a single bean instance, configure your scope property and change it to single. For instance, consider the following format.

<!—Using the singleton scope to define a bean –>

<bean id = “…” class = “…” scope = “singleton”>

<!–write the configuration and collaborators of the bean here –>



When the scope is specified as a prototype, a new bean instance is generated by the Spring IoC container whenever a request is generated for that bean. Therefore, utilize the prototype scope when your beans are stateful while using the singleton when they are stateless. To specify a prototype scope, you can use the following format.

<!—Use the prototype scope to define a bean –>

<bean id = “…” class = “…” scope = “prototype”>

<!– write the configuration and collaborators of the bean here –>


Bean Life Cycle

After the instantiation of a bean, it can execute initialization so it changes into a usable state. After the need for a bean is completed and subsequently, it is deleted, there has to be some sort of cleanup. In the post, we would discuss 2 of the most crucial lifecycle callback methods for beans.

To begin with, you can write <bean> and use with destroy-method or init-method parameters. The latter is used to define a method which is called first after the bean’s instantiation. The destroy method is used to define a method which is revoked after the deletion of a bean from the container.

Initialization Callbacks

In the org.springframework.beans.factory.InitializingBean interface, you can use the following method.

void afterPropertiesSet() throws Exception;

Therefore, you can easily use this interface and initialize by using the following format.

public class BeanExampleOne implements InitializingBean {

public void afterPropertiesSet() {

// write the code for initialization



If you are using configuration data which uses XML, then you utilize the attribute, “init-method”, to define a method name having a signature without any argument.

<bean id = “beanExampleOne” class = “examples.BeanExampleOne” init-method = “init”/>

Consider the following definition for your class.

public class BeanExampleOne {

public void init() {

// write the code for initialization



Destruction Callbacks

In the org.springframework.beans.factory.DisposableBean interface, you can use the following method.

void destroy() throws Exception;

Therefore, you can easily use this interface and initialize by using the following format.

public class BeanExampleTwo implements DisposableBean {

public void destroy() {

// write the code for destruction



If you are using configuration data which uses XML, then you utilize the attribute, “destroy-method”, to define a method name having a signature without any argument.

<bean id = “beanExampleTwo” class = “examples.BeanExampleTwo” destroy-method = “destroy”/>

Consider the following definition for your class.

public class BeanExampleTwo {

public void destroy() {

// write code for destruction



When you are working in a non-website application setup with a Spring IoT container, for instance, in a desktop environment, then you can configure a shutdown for the Java Virtual Machine. This helps to create an efficient shutdown and revokes only the appropriate destroy methods.

It must be noted that the use of Disposable Bean or Initializing Bean callbacks is not recommended as XML configuration allows a greater degree of flexibility for the XML configuration.


















Introduction to Angular JavaScript

Over the past years, the demand for JavaScript development has touched unprecedented heights. JavaScript is being used in several front-end frameworks to incorporate modern functionalities and features in websites. AngularJS is among one of these frameworks. Like, traditional JavaScript, you can add AngularJS code to any HTML document by using the following tag.

AngularJS is a client-side JS MVC (model view controller) framework which is mainly used for the production of modern dynamic websites. The project was initially conceptualized by the tech giant Google, however, later it was made available for the global software community. The entire AngularJS syntax is based on JavaScript and HTML; therefore you do not have to learn any other language.

You can convert static HTML to dynamic HTML through the use of AngularJS. It helps to enrich the capabilities of HTML-based website through the addition of built-in components and attributes. To understand some basic concepts in AngularJS, read further.


Expressions are used to bind data with HTML in AngularJS. These expressions are defined through double braces. On a similar note, you can also use the ng-bind=”expression” to define your expressions. When AngularJS counters an expression then it instantly works to execute it and sends back the result to the point from where it was called. Similar to the expressions in JavaScript, AngularJS has its own expressions which store operators, variables, and literals. For instance, consider the following example where an expression is displayed.

<!DOCTYPE html>




The calculation result is {{ 4 * 6 }}




Modules in AngularJS are used to specify the type of application. It is essentially a container which holds several application components like controllers. To create a module, you simply have to use the angular. module method.

var e1 = angular.module(“example1”, []);

Here “example1” is used for referencing an HTML element which is used to run the application. After defining your module, you can add other Angular JS elements like directives, filters, and controllers.


To modify HTML elements, you can use directives in AngularJS. You can either used built-in directives to add any functionality or specify your own directive to add behavior. Directives are distinct due to the fact that they begin with a special prefix, ng-.

  • You can use the ng-app directive to initialize an application in the AngularJS.
  • You can use the ng-init directive to initialize an application data in the AngularJS.
  • You can use the ng-model directive to bind values from HTML controls and connect them with the application data.

For example, consider the following example.

<!DOCTYPE html>




Type anything in the textbox.

User Name:

You typed: {{ userName }}



In this example, the ng-app first relays to the AngularJS that the

is the application’s owner. Then the ng-init initializes the application data .i.e. the username, and lastly the ng-model binds the username to the HTML paragraph tag. Therefore, you can observe real time change by typing in the textbox, something which was not possible with HTML alone.

You can use the “.directive” function to create your own directives. To generate a new directive, use the same tag name for your directive which is used by your HTML element. Consider the following example.


var a = angular.module(“example1”, []);

a.directive(“newdirective”, function() {

return {

template : “

This text is created through a user-defined directive!




As explained before, the ng-model directive is used to bind HTML controls. Consider the following example,


Name of the Employee:

var a = angular.module(‘Example1’, []);

a.controller(‘first’, function($scope) {

$scope.empname = “Jesse James”;







Here, the controller was used to create a property after which the ng-model was used for data binding.  Similarly, you can use it to validate user input.  For example, consider the following code. Whenever a user types irrelevant information, an error messages appears.

<!DOCTYPE html>




<form ng-app=”” name=”login”>

Email Address:

<input type=”email” name=”emailAddress” ng-model=”text”>

<span ng-show=”login.emailAddress.$error.email”>This is an invalid email address</span>




Data Binding

In Angular JS, the synchronization between the view and model is referred to as data binding. In general terms, you can think of data binding as a procedure through which a user can dynamically change the elements of a web page.

A standard application in AngularJS consists of a data model which stores the complete data of that specific application.

By view, we mean the HTML container in which we have defined our AngularJS application. Afterward, an access is configured to the model for the view. For data binding, you can also use the ng-bind directive. This directive applies binding on an element’s innerHTML and the defined property for the model. For example,

<!DOCTYPE html>




Student Name: {{studentname}}

var ajs = angular.module(‘angjs1’, []);

ajs.controller(‘firstController’, function($scope) {

$scope.studentname = “Matthew”;




Note, that double braces are used in HTML elements to show the stored data from any model.


Controllers form the backbone of AngularJS applications and contain the business or central logic of the application. Since, AngularJS synchronizes the view and the model via data binding; therefore the controller needs to only focus on data on the model. An example, of controller is the following.

<!DOCTYPE html>





var a = angular.module(‘app1’, []);

a.controller(‘first’, function($scope) {

$scope.name = “Larry”;

$scope.modifyname = function() {

$scope.name = “David”;






When you click on the heading, then the controller dynamically changes the content of the element.

Data Management Patterns for Microservices Architecture

Data is the primary requirement of any software. Thus, efficient and effective data management can make or break a business. For starters, you have to ensure that data is available to the end user at the right time. Monolithic systems are notorious for their complex handling of data management. In contrast, microservices architecture paints a different picture. Here are some of data management patterns for this type of architecture.

Database Per Service

In this model, data is managed separately by each microservice. This means that one microservice cannot access or use the data of another one directly. In order to exchange data or communicate with each other, a number of well-designed APIs are required by the microservices.

However, the pattern is one of the trickiest to implement. Applications are not always properly demarcated. Microservices require a continuous exchange of data to apply the logic. As a result, spaghetti-like interactions develop with different application services.

The pattern’s success is reliant on carefully specifying the application’s bounded content. While this is easier in newer applications, large systems present a major problem at hand.

Among the challenges of the pattern, one is to implement queries that can result in the exposure of data for various bounded contexts. Other options include the implementation of business transactions that cover multiple microservices.

When this pattern is applied correctly, the notable benefit of it includes loose coupling for microservices. In this way, your application can be saved from the impact-analysis-hell. Moreover, it helps in the individual scaling up of microservices. It is flexible for software architects to select a certain DB solution while working with a specific service.

Shared Database

When the complexity of database per service is too high, then a shared database can be a good option. A shared database is used to resolve similar issues while using a more relaxed approached as a single database receives access by several microservices. Usually, this pattern is considered safe for developers because they can make use of existing techniques. Conversely, doing such always restricts them from using microservices at its best. Software architects from separate teams require cooperation to modify the schema of tables. It is possible that the runtime conflicts occur in case two or more services attempt using a single database resource.

API Composition

In a microservices architecture, while working with the implementation of complex queries, API composition can be one of the best solutions. It helps in the invocation of microservices for the needed arrangement. When results are fetched, a join (in-memory) is executed of the data after which the consumer receives it. The pattern’s drawback is its utilization of in-memory joins—particularly when they are unnecessary—for bigger datasets.

Command Query Responsibility Segregation

Command Query Responsibility Segregation (CQRS) becomes useful while dealing with the API composition’s issues.

In this pattern, the domain events of microservices are ‘listened’ by an application which then updates the query or view database accordingly. Such a database can allow you to handle those aggregation queries which are deemed complex. It is also possible to go with the performance optimization and go for the scaling up of the query microservices.

On the flipside, this pattern is known for adding more complexity. Suddenly it forces that all the events should be managed by the microservice. As a consequence, it is prone to get latency issues as the view DB exercises consistency in the end. The duplication of code increases in this pattern.

Event Sourcing

Event sourcing is used in order to update the DB and publish an event atomically. In this pattern, the entity’s state or the entity’s aggregate in the form of events—where states continue to change—are stored. Insert and update operations cause the generation of a new event. Events are stored in the event store.

This pattern can be used in tandem with the command query responsibility segregation. Such a combination can help you fix issues related to the maintenance of data and event handling. On the other hand, it has a shortcoming as well; the imposition of an unconventional programming style. Moreover, eventually, the data is consistent, not always the best factor for all the applications.


When business transactions extend over several microservices, then the saga pattern is one of the best data management patterns in a microservices architecture. A saga can be seen as simply local transactions—in a sequence or order. When Saga is used to perform a transaction, an event is published by its service. Consequently, other transactions follow after being invoked due to the prior transaction’s output. In case, failure arises for any of the chain’s transactions, a number of transactions (as compensation) are executed by the Saga to repair the previous transactions’ effect.

In order to see how Saga works, let’s consider an instance. Consider an app which is used for food delivery. If a customer requests for an order of food, then the following steps happen.

  1. The service of ‘orders’ generates an order. In this specific time period, a pending state marks the order. The events chain is managed by a saga.
  2. The service of ‘restaurant’ is contacted by the saga.
  3. The service of ‘restaurant’ begins the process to start the order for the selected eatery. When the eatery confirms, a response is generated and sent back.
  4. The response is received by the Saga. Considering the response contents, it can either proceed with the approval or rejection of the order.
  5. The service of ‘orders’ modifies the order state accordingly. In the scenario of the order approval, the customer receives the relevant details. In the scenario of order rejection, the customer receives the bad news in the form of an apology.

By now, you might have realized that such an approach is too distinct from the point-to-point strategy. While this pattern may add complexity, it is a highly formidable solution to respond to several tricky issues. Though, it is best to use it occasionally.

Are Microservices the Right Fit For You?

The term Microservices was originally coined in 2011. Since then it has been on the radars of modern development organizations.  In the following years, software architecture has gained traction in various IT circles. According to a survey, the enterprises which used microservices were around 36 percent while 26 percent were thinking to include it in the future.

So, why exactly should you use microservices your company? There has to be something unique and more rewarding in it that can compel you to leave your traditional architecture in favor of it. Consider the following reasons to decide for yourself.

Enhance Resilience

Microservices can help to decouple and decentralize your complete application into multiple services. These services are distinct because they operate independently and are separate from each other. As opposed to the conventional monolithic architecture in which code failure can disrupt one function or service, there are little to no possibilities a single service failure to affect another. Moreover, even if you have to do maintain code for multiple systems, it will not be noted by your users.

More Scalability

In a monolithic architecture, when developers have to scale a single function, they have to tweak and adjust other functions as well. Perhaps, one of the biggest advantages of microservices is the scalability which it brings to the table. Since all the services in microservices architecture are separate, therefore it is possible to scale one service or function without having to worry about scaling up the complete application. You can deploy critical business services on different servers to improve the performance and availability of your application whereas your other services remain unaffected.

Right Tool for the Right Task

Microservices ensure that a single vendor does not make you pigeonholed. It can help you to infuse greater flexibility for your projects so rather than trying to make things work with a single tool, you can instead look up for the right tool which can fit your requirements. Each of your services can use any framework, programming language, technology stack, or ancillary services. Despite this heterogeneousness, they can still communicate and connect easily.

Promotion of Services

In microservices, there is no need to rewrite and adjust the complete codebase if you have to change or incorporate a new feature in your application. This is because microservices are ‘loosely coupled’. Therefore, you only have to modify a single service if it is required. The strategy to code your project in smaller increments can help you to test and deploy them independently. In this way, you can promote your services and application quickly, as soon as you complete one service after another.

Maintenance and Debugging

Microservices can help you to test and debug applications easily. The use of smaller modules via continuously testing and delivery means that you can create applications from bugs and errors, thereby improving the reliability and quality of your projects.

Better ROI

With microservices, your resource optimization is instantly improved. They allow different teams to operate by using independent services. As a result, the time needed to deploy is reduced. Moreover, the time for development is also significantly decreased while you can achieve greater reusability as well for your project. The decoupling of services also means that you do not have to spend much on high-priced machines. You can use the standard x86 machines as well. The efficiency which you get from microservices can minimize the costs of infrastructure along with the downtime.

Continuous Delivery

While working with a monolithic architecture, dedicated teams are needed to code discrete modules like front-end, back-end, database, and other parts of the application. On the other hand, microservices allow project managers to add cross-functional teams in the mix who can manage the application lifecycle through a delivery model which is entirely continuous in nature. When testing, operations, and development teams use a single service at the same time, debugging and testing is quickened and made easier. This strategy can help you to develop, test, and deploy your code ‘continuously’. Moreover, you do not have to write new code, instead, you can write code with the help of the existing libraries.

Considerations before Deciding to Use Microservices

If you have decided to use a microservices-based architecture, then review the following considerations.

The State of Your Business

To begin with, you have to think if your business is big enough that it warrants your IT team to work on complex projects independently. If you are not, then it is better to avoid microservices.

Assess the Deployment of Components

Analyze the components and functions of your software. If there are two or more components which you deploy in your project which are completely separate from each other in terms of business processes and capabilities, then it is a wise option to use microservices.

Decide if Your Team Is Skilled for the Project

The use of microservices allows project managers to use smaller teams for development that are well-skilled in their respective expertise. As a result, it helps to quickly generate new functionalities and release it.

Before you adopt the microservices architecture, you have to make sure that your team members are well positioned to operate with continuous integration and deployment. Similarly, you have to see if they can work in a DevOps culture and are experienced enough to work with microservices. In case, they are not good enough yet, you can focus on creating a group who is able to fulfill your requirements to work with microservices architecture. Alternatively, you can also hire experienced individuals to make up a new team.

Define Realistic Roadmap

Exponential scaling is the key to success. Despite the importance of businesses to be agile, it is not necessary for all businesses to scale. If you feel that complexity cannot help you much, then it is better to avoid a microservices architecture. You have to decide on some realistic goals about how your business is going to operate in the future to decide if the adoption of microservices architecture can reap your benefits.

Evolution of Event-Driven Architecture

As I explained in my previous post (here and here) Event Driven Architecture is key to digital transformation. here I will be talking about how it evolved.

Nowadays, the trend in event-driven architectures is one in which messaging is complex and quite different from a basic pipe which establishes connections between systems. Today, event-driven architectures combine elements from distributed database systems and streaming systems through where users can join, aggregate, modify and store data. The modern day implementations of these practices have been classified into four general patterns.

1.   Global Event Streaming Platform

This pattern bears similarity to the traditional enterprise messaging architectures. By using this event-driven approach, a company uses core datasets. These datasets share data from application modules like customers, payments, accounts, orders, and trades while an event streaming platform (for .e.g. Apache Kafka) is used.

This approach is a replacement for point-to-point communication technologies which are used in legacy systems. In such systems, applications are connected over multiple separate locations in real time.

For example, a business which runs a legacy system in New York can also operate through international branches in Stockholm and Berlin while connected through microservices with Amazon Web Services, all at the back of the same event-driven approach. A more complex use can include the connection of various shops across regional waters.

Some renowned companies who have adopted this approach include Audi, Netflix, Salesforce, ING, and others.

2.   Central Event Store

Events can be cached by streaming platforms for a specific or undefined time period to generate an event store, a type of organizational ledger.

This approach is used by companies for retrospective analysis. For instance, it can be used to train models in ML (machine learning) for detecting frauds of an e-commerce website.

This approach facilitates developers to create new applications without needing the republishing of previous events by the source systems. As a result, it becomes easier to replay datasets from their actual source which can be a legacy, external, or mainframe system.

Some companies store their complete data in Apache Kafka. This approach comes under event sourcing, forward event cache, or event streaming.

Event storage is necessary for stream processing with states to generate self-sufficient and enriched events through several distinct data sources. For example, it can be used to enrich orders from a customer module.

FaaS implementations or microservices are easily able to consume enriched events as these events carry all the required data for the service. Additionally, they are also used for the provision of a database’s denormalized input. These enrichments are executed by stream processors which require event storage to store data which works with tabular operations.

3.   Event-First and Event Streaming Applications

Usually, in conventional setups, applications gather data from several locations and import datasets in the database after which it can be filtered, joined, aggregated, and cleaned. This is a particularly effective strategy for applications which require creating dashboards, reports or operate via online services. However, in business processing, efficiency can be attained by ignoring the DB step and instead of sending real-time events into a serverless function or microservices.

For such approaches, stream processors like KSQL and Kafka Streams execute the operations like joining, filtering, and aggregating event streams to manipulate data.

For instance, suppose there is a service for limit checking which joins payments and orders through KSQL or a similar stream processing. It extracts the required fields and records and then sends it to a FaaS (function as a service) or a microservice where a check is executed for the limit while no DB is used.

This event-driven approach offers a greater degree of responsiveness to systems because they can be quickly and conveniently built due to the lesser data and infrastructural requirements.

4.   Automated Data Provisioning

This approach mixes the above-mentioned practices while combining serverless/PaaS implementations with the aim of making data provisioning as self-service.

In automate data provisioning, a user specifies the type of data which they require including its form or where it should land like a microservice, FaaS, database, or a distributed cache.

The infrastructure is provisioned by the system which also pre-populates it accordingly and supervises the flow of events. The buffering and manipulation of data streams is done by stream processing. What this means is that for instance, a retail business may have to join payments, customer, and real-time orders after which they send it into a FaaS or a microservice.

The migration of businesses to both private and public clouds has overseen an adoption of this pattern. It helps to initiate new project environments. Since datasets are stored or cached in the messaging system; therefore users only have to use data which they require at a specific time. Meanwhile, earlier in traditional messaging, it was a common practice to hoard and consume complete datasets so they can be used later.


Shedding Light on the Evolution

Over the last few years, there has been considerable evolution in event-driven architectures. In the beginning, they were only used to pass messages—use of state transfer and notification for standard messaging systems.

Afterward, these architectures were enhanced with the help of improved centralized control and better out-of-the-box connectivity. However, centralized control was a tricky part; due to standardization, teams found it difficult to progress. In recent memory, storage patterns like CQRS and event sourcing have gained popularity. The above-mentioned approaches are based on it.

Nowadays modern event streaming systems have taken the next step to unify storage, processing, and events into the same platform. This amalgamation is necessary as it shows that these systems are different from databases which store data in one place. Moreover, they do not even fall into the category of messaging systems in which data is transitory. Thus, they are a combination of both databases and messaging systems.

The correct use of these standard categories has allowed organizations to target several regions and clouds for global connectivity and data has become one of their most prized commodities used in provisioned as a service. This means that it can be pushed into a cache, database, machine learning model, serverless function, or a microservice.






Fundamentals to Create a Scalable Cloud Application

Developing cloud-based applications needs a modern mindset along with the implementation of newer rules. When the cloud is used effectively, it can help both small and large enterprises with a wide range of activities. Consider the following factors to create a scalable cloud application.

Reflecting on the Topology

One of the leading reasons behind the use of cloud systems is that it helps businesses scale their applications at will. Often, virtual applications are implemented to attain this type of scaling.

Instead of limiting yourself to a certain topology, you should consider how to protect your cloud applications from the dynamic scaling impact. If you can design a generic application, you can prevent your application from struggling with negative effects during the cloud migration. In case your application is using a singleton state, developing a backup through a shared repository can assist you before the cloud migration.

Pondering Over Design

Designing the appropriate scalable cloud architecture which also aligns well for business risk requires the right combination of security policies and design principles. For a cloud system, you should have the tools for designing, implementing, and refining your policies for enforcement, and controls in a centralized approach.

These tools allow developers to equip the network layer with security through solutions like host-level firewalls, VPNs, access management, and security group. For the operating system layer, they can take advantage of strict privilege separation, encryption storage, and hardened system. While dealing with the application layer, they can benefit from carefully enforced rules and the latest updates. The idea is to implement these solutions in conjunction for the design and development approach, instead of thinking it as being part of operational maintenance.

In the cloud, when you deploy services, it gives you an edge to plan your network and security solutions from scratch.


Sharding and Segmenting

You have to ensure that you have properly separated concerns regarding your components. For instance, while working with an RDBMS, a common question is that where should the developer place the DB? According to a conventional approach, the DB server has to a large metallic structure with 16TB RAM and a 64-CPU box.

However, a large and singular DB server for hosting is significantly less powerful in comparison to multiple small DB servers where each of them hosts one schema. This means that if you have a 16 CPU, 16 GB server, then you are better off with 2 CPU, 2GB PostgreSQL servers to achieve greater performance. Similarly, you also have the choice to vertically scale all your instances by adding one schema for each server. In this way, you can have additional RAM, CPUs, and can also perform migration for better storage to use that schema.

This recommendation is equally effective for in-house IT teams. For instance, if all the organization’s department wants to use a portal—let’s say it is built on Drupal—then all departments have to manage documents themselves. Therefore, the use of a stand-alone server is an effective option. All your instances can be maintained with a similar strategy; they will be connected to a primary authentication server while scaling will be performed independently.

Choosing Enterprise Integration Patterns

While designing cloud applications, choosing the right design patterns is necessary. Enterprise integration patterns are a good fit for service-oriented architectures. You can use enterprise integration patterns which have gained relevance for enterprise-level systems. It helps to scale cloud systems on demand while incorporating an extensive list of third-party tools.

The use of development which is based on patterns helps developers and architects to code applications which are convenient to connect, describe, and maintain through utilizing recurrent elements.

In cloud systems, patterns are particularly used for routing messages. Sometimes in service-oriented architectures, a common pattern known as message translator is required where the adoption of shared best practices is effective.

Similarly, there are options like Apache Camel which developers use to get their hands on the enterprise integration framework. It allows them to avoid writing glue code. Instead, they can focus on coding business logic, thanks to the reusable integration patterns of Camel.

Enhancing QoS

At times, a strong wave of traffic comes at once where systems are simply struggling to manage the workload.  If a service is non-operational in these times, then it can be quite tricky to handle a large number of messages which must be processed. These are times when the inbound traffic is more than the limits of the system and it is not possible to create a response for all requests.

You may think about the shutdown as a solution but there are better options. If you can enforce QoS (quality of service) to produce and consume your messages, it is possible to achieve diminished availability rather than going completely unavailable.

To stop services from getting overloaded, you can decrease the message number which is being managed; specify a time-to-live for them. By doing this, messages which are older and not relevant can expire and thus discard after a time limit.  You can also try restricting the message number which is interpreted simultaneously through the use of concurrent message consumption; restrict each service to handle no more than 10 messages at a single time.

Going Stateless

For scalable cloud applications, the maintenance of a state is always tough. Persistence is another name for maintenance of state. Persistence is to store data in a location that is central, due to which it is always hard to scale in such instances. Instead of using multiple transactional or stateful endpoints for your cloud application, you can ensure that your application possesses a RESTful capability; however, make sure that it is not restricted by HTTP.

If you must use state and you cannot ignore state for your cloud applications, then you can govern your state through the use of the above-mentioned enterprise integration patterns. For instance, read about the claim check pattern.

The Best Practices for Hybrid Cloud Development

Hybrid cloud has changed the way applications run. It has affected the expectations of the consumers which in turn has a strong impact on the business revenue streams. In order to adapt quickly, consider the following best practices for cloud development.

Selection of IDE

There is an extensive list of IDEs (Integrated Development Environment) that can be used in hybrid cloud development. While creating hybrid cloud applications, you should target IDEs with the following features.

  • An IDE must be compatible with several programming languages to power the requirements of enterprise development.
  • Since the dev community uses IDEs, it is best to use only those IDEs which are open-source and come with strong community support. Similarly, there should be only a few IDEs to deal with so patching, upgrading, and maintenance tasks are convenient.
  • Since, we are talking about the cloud; hence it is almost necessary that IDEs can connect with cloud solutions like Oracle, Salesforce, Azure, and AWS.
  • IDEs must have seamless integration for popular source code repositories like Gitlab, Bitbucket, Subversion, etc.
  • The chosen IDE must help developers with the import of common libraries, automatic code generation, generation of template projects, provision of shortcuts keys for generation of code constructs, auto-complete, assistance for compilation errors, and code recommendations.
  • If API management tools like Akana, Mule, and Apigee are compatible with the IDE and developers can use them for publishing and pulling content directly, then it can save a lot of time.

There are some of the features that are must-have if you wish to breeze through your hybrid cloud development for enterprise level. AWS Cloud9 and Eclipse are some of the IDEs which check of all the above-mentioned boxes.


Integrated governance and security, an efficient sequence of procedures, planned configuration, and cloud-agnostic tools are necessary to discover powerful tool chains. Developers should try to limit the number of steps for the toolchain so they speed up with quicker builds while maintaining quality assurance for the written code.

Design Patterns

The addition of design patterns ensures that applications conform to industry standards. For hybrid cloud development, you have to check which design patterns fit your use case. If you select the wrong design pattern, then it can create confusion and can potentially disrupt the robustness of the application.

Applications replicas are spun up and tore down in hybrid cloud environments. Therefore, it is necessary to conduct a thorough detail analysis to identify the creation and destruction of objects. In some use cases, you might require certain handling for the objects which is caused by rapid scaling.


Agile and Lean practices allow development teams to offer higher value for the stakeholders. VersionOne, Jira, and similar tools can help you to not only monitor, analyze, and prioritize your work but also help the provision of transparency for the stakeholders. Tools like XP can have a positive impact on the code for hybrid cloud development. Similarly, with the use of pair review and pair programming, you can ensure that your code is written and verified effectively.

Server Selection

Developers can pick from many types of servers to work for hybrid cloud applications. Majority of the IDEs are powered by their own default servers. Cloud experts recommend using the nearest version and server type for the code which is expected to be deployed. Try to choose a cloud-agnostic and light-weight server for the job. The introduction of light-weight servers can save a great amount of time for the starting and deploying the code.

Service Discovery

Regardless of the fact that your IT architecture is based on a cloud or on-premises architecture, services are always bound to grow. There comes a point where it absolutely necessary to target strong discovery process. Depending upon the tools and use case, you can go for server-side discovery, gateway moderation, and self- registration. To do this, you can use multiple tools like Mule Service Registry, Istio, and API Connect. Before choosing these tools, assess their impact on the performance of your hybrid cloud application.


As the cloud development gained traction, it became easy for instances to rebuild and teardown regularly. This helps in frequent patching and enhancements for the OS, 3rd party libraries, middleware, and the existing code base. Developers should patch and upgrade whenever it is possible. This can help to not only limit defects and vulnerabilities but also improve the cloud application’s upgrade cycle. Good practice for your provisioned servers is to use a regular upgrade cycle where it can serve as a portfolio activity for your IT infrastructure.


For IT artifacts, versioning is one of the most important aspects. Perhaps, the most popular among them is the Semantic Versioning. However, depending upon your use case, you can use others as well. With tools like Git and Bitbucket, you cannot only apply versioning for your artifacts but also improve their functionality to merge, branch, revert accordingly. Therefore, versioning is implemented for all of your artifacts.

Third-Party Libraries

When the complexity of your hybrid cloud application reaches higher levels, you can take advantage of 3rd party libraries. They can help you with vulnerabilities, dependencies, and vendor lock-in. For best results, use these libraries sparingly. Additionally, pick those which have an open source and strong community support to back them. Choosing those with a proven track record can help you with better results.


XML and JSON are the most common mediums for the exchange of information between heterogeneous systems. You have to think about a standard solution which can convert or translate these messages as objects. If you are working with C#, then you can use XML to C# and JSON to C# while Java programmers can use JAXB, Jackson, and Java API for JSON. This can assist developers in the creation of incoming messages. Moreover, RAML and Swagger are getting traction, therefore their tools for Java and .NET can prove helpful for developers.