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Amit Gandhi

Orchestrated PaaS: A Product-Centric Approach

974 608 Parkar Consulting & Labs

“We must indeed, all hang together, or most assuredly we shall all hang separately.” – Benjamin Franklin

Franklin reportedly issued this as a warning during the Revolutionary War of 1776.

Fast forward to the technological war room of the 21st Century, unity is still the key to victory, especially when it comes to the ‘Battle of the Clouds’!

Cloud technology is at chaotic crossroads. At the time when cloud adoption soared across industries, a plethora of companies jumped on the bandwagon with a short-sighted strategic approach. They quickly realized that the future of cloud computing does not lie in the implementation of multiple cloud resources but in the holistic adoption of cloud in all its forms. In other words, the value of cloud services is increasing exponentially as it functions as a single, cohesive, and orchestrating unit.

What is Cloud Orchestration?

Let’s take the example of a mechanical watch. How does it work? It involves the functioning of numerous interconnected gears that work in perfect harmony to measure the passage of time. The end result looks something like this:

Source

The number of gears (or jewels) is directly proportional to the price of the watch. Why? Simply because every additional gear substantially improves the accuracy of the result. Fascinating stuff, isn’t it?

This is how cloud orchestration works. If you consider every independent cloud deployment and functionality as a gear, then orchestration is the process of bringing together every moving cloud part and tying them up in a single and monolithic workflow.

Benefits of disparate cloud resources working in tandem include the likes of high availability, scaling, failure recovery, and dependency management. DevOps can boost the speed of service delivery while reducing costs and eliminating potential errors in provisioning, scaling, and other processes.

In this case, the end result looks like this:

Source

And this is the groundwork on which our story is based on.

PaaS – The Realm Beyond Cloud Orchestration

Platform as a Service or PaaS is a type of service deployment in the cloud that takes a product-centric approach and goes beyond orchestration. It aims to meet the basic infrastructure and platform needs of developers to deploy applications. They do not need to handle mundane tasks. Instead, they can use APIs to develop, test, and deploy industry solutions. For this purpose, they are generally hosted and deployed as web-based application-development platforms. This gives developers the flexibility to provide end-to-end or partial online development environments.

While it does help to orchestrate containers, the main function of Orchestrated PaaS lies in setting up choreographed workflows. This makes it relevant for software solutions that want to primarily focus on the development cycle of the software and the monetization of new applications. By deploying agile tools and techniques, companies can accelerate application development, reduce compartmentalization, increase collaboration, and boost scalability.

Apart from these, the primary reasons to implement an orchestrated PaaS strategy are:

  • Accelerated application development
  • Quicker deployment
  • Faster go to market
  • Organization-wide collaboration
  • Hybrid cloud flexibility
  • Enterprise-grade security

There are two basic types of Platform as a service deployment – ‘Service Orchestration’, and ‘Container Orchestration’.

Service Orchestration

These include public PaaS solutions and functions as a bundled package for individuals, startups, and small teams. Being public in nature, it does come with certain limitations in terms of the depth of integration it offers. Hence, it poses to be a difficult choice for organizations that are looking for company-wide standardization.

But, in situations where quick prototyping and deployment is needed with an ability to go past compliances, public PaaS solutions can come to the rescue.

Container Orchestration

Container orchestration includes private SaaS solutions that function as a closed system. It does not focus on where the product or application is running, rather concentrates on simply the running of the resulting service. For instance, the end result can be loading certain web pages without any latency.

But modern Enterprise IT has gradually brought about a change where they are concerned with the scale of application and not just the underlying system.

The Coveted PaaS Model

To better understand how a PaaS framework can serve certain business scenarios, here are certain cases of this model.

  • A single vendor owns every platform or application that is contained in the PaaS model.
  • Applications need to be developed from scratch and leverage a formalized programming model.
  • The services involved in the solution are common and stable.
  • All the roles of containers used in the business model are stable.
  • No industry-specific service or application is being used in the platform, and it is simple and easy to design and manage.

The whole idea of PaaS is to empower developers by helping them deliver value through their product without worrying about building a dedicated IT infrastructure.

Best Practices and Patterns of Orchestrated PaaS

The manner in which a PaaS system can be fundamentally orchestrated depends on its solution-specific application scenario, business model, and enterprise architecture. Based on this, integration patterns with other leading industry solutions can also vary. Various patterns in which PaaS can be implemented include:

  • Embedded PaaS

This is implemented within an industry solution and becomes a part of it. Examples of this include; a cloud-enabled integrated information framework. In such a system, only certain parts or functions of the whole system are deployed as PaaS solutions. The rest of the solution is not hosted on the cloud.

  • Value-added PaaS

Functions as ‘PaaS on an industry’ and includes industries that host value-added PaaS solutions that can be used by customers in tandem with their core industry offerings. Primary functions and infrastructures are maintained outside the cloud environment. Examples here include; a self-service telecommunications service delivery platform that is based on the cloud that empowers customers to quickly deploy value-added PaaS functionalities from the ground-up.

  • Bundled PaaS

The core function or solution of the industry is bundled together in the same PaaS environment. The end result is an industry-specific PaaS solution that empowers the entire business model of the company to function as an independent node in the ecosystem.

The World of Containers: Building Blocks of PaaS

In the elementary sense, containers are what have made PaaS possible in the first place. All the necessary code of a function can be bundled into a container, and the PaaS accordingly builds on to run and manage the application.

 

Although PaaS boosts the productivity of developers, they have little wiggle room. But, now, further technological development has made an autonomous existence of containers possible with leading software solutions, such as Docker, Kubernetes, and Red Hat OpenShift.

With these applications, developers can now easily define their app components and build container images. Apps can now run independently from platforms, paving the way for more flexible orchestration.

Software-Driven Container Orchestration

Here’s a close look at the various software that is making PaaS orchestration possible by functioning at the container level.

1. Docker

Docker is an open platform that is meant for developing, running, and delivering applications. It enables users to treat their infrastructure like a managed application. As a result, developers can quickly ship codes, test apps, and deploy them by reducing the time gap between writing and running codes.

Benefits of Docker for Paas Orchestration include:

  • Faster delivery of applications.
  • Easy application deployment and scaling.
  • Achieving higher density and running more workloads.
  • Eliminating environmental inconsistencies.
  • Empowering developer creativity.
  • Accelerating developer onboarding.

2. Kubernetes

Kubernetes is another popular container orchestration tool that works in tandem with additional tool sets for functions, such as container registry, discovery, networking, monitoring services, and storage management. Multiple containers can be grouped together and managed with a single entity to co-locate the main application.

Features of Kubernetes include:

  • Algorithmic container placement that selects a specific host for a specific container.
  • Container replication that makes sure that a specific number of container replicas are running simultaneously.
  • An auto-scaling feature that can autonomously tweak the number of running containers based on certain KPIs.
  • Resource utilization and system memory monitoring (CPU and RAM).

3. Red Hat OpenShift

This is a unique platform that is a combination of Dev and Ops tools that functions on top of Kubernetes. Its aim is to streamline application development and manage functions like deployment, scaling, and long-term lifecycle maintenance.

Various features of the tool include:

  • Single-step installation for Kubernetes applications.
  • Centralized admin control and performance optimization for Kubernetes operators.
  • Contains functions, such as built-in authentication and authorization, secrets management, auditing, logging, and integrated container registry.
  • Smart workflows, such as automated container build, built-in CI/CD, and application deployment.
  • Built-in service mesh for microservices.

In fact, Openshift has become the go-to platform to implement PaaS orchestration.

At Parkar, we recently came across a project where the client was looking to develop a next-gen platform that increased speed and incorporated

innovation in their existing technological ecosystem. Our developers used Openshift as the orchestrated container platform and significantly reduced the time to market.

The decision paid off with significant metrics and the following project results were realized:

Conclusion

It is safe to assume that successful cloud orchestration opens the door for a number of benefits for the entire cloud ecosystem. These include forced best practices, simplified optimization, unified automation, improved visibility and control, and business agility. The PaaS construct functions as a layered model to deliver specific applications and services. It also improves the end-result with abilities like rapid time-to-market, future-proofing, and investment protection to support all-round cloud-based digital transformation.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Application Containerization Assessment

974 608 Parkar Consulting & Labs

Containerization seems like the buzzword these days and I&O (Infrastructure and Operations) leaders globally are eagerly adopting container technology. As per Gartner, the containerization wave will sweep across organizations worldwide with 75% of them running containerized applications in production by 2022 as opposed to the current 30%. Having said that, the fact cannot be denied that the present container ecosystem is still in its nascent stage. There’s a lot to get out of containerized environments provided containerization is a good fit for your organization. Detailed assessment therefore becomes a mandate to ensure that you have a solid business case that makes the additional layer of complexity and cost incurred in deploying containers absolutely worth your efforts. It wouldn’t be wrong to say that running them in production, by far, seems like a steep learning curve that many are trying to comprehend.

The dilemma 

To containerize or not to containerize is one question that continues to plague the minds of many. While moving traditional monolithic workloads to the cloud seems like a great idea, organizations need to seriously ponder over whether moving the workload is indeed the right thing to do. Many are going by the ‘lift and shift’ the application into a virtual machine (VM) approach, but the pertinent question here is ‘does containerization help your case?’ When applied correctly, it will not only modernize legacy applications but also create new cloud-native ones that run consistently across the entire software development life cycle. What’s even better is that these new applications are both agile and scalable. While deploying containers in production environments, the I&O teams need to mitigate operational concerns pertaining to their availability, performance and integrity. At Parkar, we look at all your deployment challenges critically. We’ve identified the key elements that can help you decide how eligible your applications are for containerization.

Here’s a quick lowdown on the assessment. Take a look.

Now lets deep-dive into more details.

Is your platform a containerized version?

This should not be so difficult considering the fact that vendors have already taken care of that. Commonly used platforms such as Node.js, Drupal, Tomcat and Joomla have taken care of the nitty-gritties to ensure that the app you use offers scope for digital transformation and gets converted to be adapted effortlessly into a containerized environment. For starters, begin with an inventory of all internally-developed applications. Check if the software being used allows containerization. If yes, you can extract the application configuration, download its containerized version and Voila; you are good to go. The same configuration can be fine-tuned to run in that version and can be subsequently deployed in a shared cluster in a configuration that is even cheaper than its predecessor.

Do you have containerized versions of 3rd party apps?

With the vast majority of legacy apps being converted into containerized versions, third party vendors are also realizing the benefits of jumping onto the containerization bandwagon. When you choose to containerize instead of choosing VMs, you also eliminate the need of having OS and bearing its license fee. This leads to better cost management as you avoid paying for unnecessary stuff. As a result, vendors too are now offering containerized versions of their products. Commercial software is one of them. Classic case in point- Hitachi offering SAN management software on containers. This is a value-add to their existing full-server versions. Infrastructure servers deployed at data centers are good examples of application containerization. Talk to your vendors and they will tell you if they offer any. For all you know, the road to application containerization may be smoother than you think.

Do you have a stateless app?

When an application program does not save client data that gets generated in one session to be used for a future session even if it is for the same client, it is called as a stateless app. The configuration data so stored is often in the form of temporary cache instead of permanent data on the server. Typically, Tomcat tiers and many other web front ends are good examples of stateless apps where the role of tiers is merely to do processing. As you take away the stateless tires of any application, they automatically become eligible for containerization due to the flexibility they attain. Rather than being run at high density, they can now be containerized to facilitate simpler backups and configure changes to the app. While these are good targets, storage tools such as ceph, Portworx and Rex-Ray also make good candidates, except that they will require a lengthier process to containerize. Post the makeover, they become great targets.

Is your app part of a DevOps and CI/CD process?

If the answer is yes, then migrating to containers would be a cakewalk for you. All you need to do is package them in containers and integrate them with your servers. As you gradually gain confidence that the deployment has been well received and the app is working as desired, you can bring container orchestration platforms into the picture and enjoy a host of advantages, top on the list being resilience and efficiency. Companies have started realizing the benefits of app containerization and have started modifying their existing CI/CD pipeline so as to create a more efficient and robust infrastructure. Apart from the obvious benefits, containerization goes a long way in testing and deployment of new code and even retrieves the ones that are not performing well. For those who thrive on agile development, this feature is definitely a huge savior.

Are you using a pre-packaged app?

It’s easy to containerize an application if it is already packaged as a single binary or a JAR file since both are fairly flexible. What’s common about Java apps and JAR files is that both can be easily changed to their containerized versions apart from the fact that they carry their typical JRE environment into the container during the process. This ensures faster and simpler deployment and also gives users the freedom to run various versions of Java runtimes alongside on the same servers. This is possible purely because of the isolation that containers offer.

How secure is the environment?

A container-based application architecture comes with its own set of security requirements. Container security is a broad term and includes everything from the apps they hold to the infrastructure they depend on. The fact that containers share a kernel and don’t work in isolation makes it important to secure the entire container environment. The Linux 3.19 kernel for instance, has about 397 system calls for containers clearly indicating the size of the attack surface. A small breach in the security of a single one would in turn jeopardize the security of the entire kernel. Also, containers such as Docker containers have a symbiotic arrangement and designed to build upon each other. Security should be continuous and must gel well with enterprise security tools. It should also be in line with existing security policies that balance the networking and governance needs of containers. It is important to secure the containerized environment across the entire life cycle that includes but is not limited to development, deployment and the run phase. As a rule of thumb, products that offer whitelisting, behavioral monitoring and anomaly detection must be used to build security in the container pipeline. What you get is a container environment that can be scaled as required and completely trusted.

Resource Requirements

As opposed to running VMs that require more resources, containers occupy just a miniscule portion of the operating system and are therefore less resource-intensive. Several containers can be easily accommodated on a single server with ease. However, there will be edge cases where scaling of multiple containers may be necessary in order to replace a single VM which would also mean you could be saying goodbye to potential savings on resources. One VM would be equivalent to an entire computer, and if you were to divide its functions into 50 distinct services, you would actually be investing in not one but 50 partial copies of the operating system. Now that’s something you definitely need to consider before deciding if containerization is for you. You get it? Or we could go on and on with the number crunching.

Other considerations

There are several other considerations that determine if your apps are containerization-worthy. You need to take into account several factors such as storage, networking, monitoring, governance and life cycle management. Each has a vital role to play and can be a critical component in the decision-making process.

Ask the experts

Parkar recently undertook an application modernization project for a prominent healthcare company where it was tasked with the evaluation of multiple applications to check their readiness for containerization. We worked on one of the critical business applications and chalked out a roadmap to modernize and containerize it without compromising security and availability. We migrated the application to OpenShift platform with multiple containers for their frontend and backend layers. The application was scaled both horizontally and vertically.

Here’s what we achieved:

 

 

Summing up

Containerization comes with a world of benefits. From allowing you the convenience of putting several applications on a single server to support a unified DevOps culture, they give you the agility and power to perform better in a competitive environment. Since they run on an operating system that’s already booted up, they are fast and ideal for apps that require spun up and down every now and then. Being self-contained they are portable and can be easily moved between machines.

The modern organizations rely on DC/OS to host containerized apps. The reason being that this method consolidates your infrastructure into a single logical cluster that offers incredible benefits that include fast hosting, efficient handling of load balancing, and automatic networking of containers. It allows teams to estimate the resources required and help reduce the operating costs for existing applications.

If you wish to know if containerization is right for you and want to unleash greater efficiencies, contact us today.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Container orchestration with Red Hat OpenShift and Kubernetes

974 608 Parkar Consulting & Labs

 

“The difficulty lies not so much in developing new ideas as in escaping from old ones”

John Maynard Keynes.

Containers have taken the world by storm! Many companies have begun to show a fervent interest in this next evolution on Virtual Machines. With a plethora of container definitions out there, let me just attempt to give a layman understanding of the term. Container, in simple terms, is something that helps your software run consistently irrespective of the underlying host environment. It ensures the predictability and compatibility of your application across the diverse landscape of infrastructure.

To exemplify this, let us suppose you borrow a movie from your friend but it is not compatible with your PC because you don’t have the video player to display it. Here comes the role of a container, which will fill in the deficit caused by the PC.

We, at Parkar, have seen how container orchestration using Kubernetes and OpenShift  have immensely helped companies get on the fast-paced delivery and digital transformation journey. Let me attempt to break it down.

What really happens in production using containers?

Have you ever been to a symphony? An orchestra ? or a music concert? You must have seen many artists doing what they do best and in the center would be the maestro managing the entire show. Now imagine in a production environment, you need to manage the containers that run the applications and ensure that there is no downtime. Modern applications, especially the ones based on microservice architecture, usually contain multiple services (run in separate processes) and each is run in a separate container. Thus a single application contains multiple container-services and their multiple instances.

Think of a scenario where a container/service instance goes down. Ideally, another instance would need to start and serve in its place. How would you manually be able to keep track and handle this lag?  Wouldn’t it be easier if this behavior was automated without any human intervention? This is exactly where container orchestration comes in and helps solve this problem.

Container orchestration is an automated process of managing, scheduling and balancing the workload on the container or containers for the applications. Container orchestration tools help in managing the containerized workloads and services.

The most widely used container orchestration platforms are Open Source Kubernetes, Docker Swarm, and Enterprise Red Hat OpenShift. Kubernetes is the most popular orchestration tool out there and has kind of become a name in itself !

Our recent project involved us working on a PAAS platform where we built a Next Generation application mobilization platform involving OpenShift and Kubernetes. Using this platform, an API marketplace was created which facilitated access to data from legacy applications and enabled the creation of new applications in a rapid and scalable manner.

But Is Kubernetes enough in itself?

Kubernetes had become a brand name, and perhaps is the most efficient of them all. However,  Kubernetes is just a container orchestration tool, which also needs to be supplemented with an additional toolset for container registry, discovery, networking, monitoring services, and storage management. Kubernetes also needs to depend on other service mesh tools like Istio for service to service communication. There are several architectural and integration considerations that have to be met to make all of this work together. Building container-based applications require even more integration work with middleware, frameworks, databases, and CI/CD tools. To augment the base Kubernetes, Red Hat OpenShift combines all these auxiliaries into a single platform, and thus presents a more complete solution to DevOps.

Let us understand what Red Hat Openshift Platform is, shall we?

Red Hat OpenShift platform is a combination of Dev and Ops tools on top of Kubernetes to streamline application development, deployment, scaling, and long-term lifecycle maintenance for small and large teams in a consistent manner. In other words, it is a ‘Kubernetes Platforms as a Service’.

What’s the big advantage of it?  Red Hat OpenShift brings over the bare Kubernetes, meaning it lets teams start building, developing, deploying easily and quickly, in infrastructure agnostic way, i.e. whether in the cloud or on-premises.

Image Source: https://www.openshift.com/learn/what-is-openshift

Parkar’s clients have seen tremendous benefits with containers. The benefits have been across application deployment frequency, time to deploy and number of deployments overall. And with these benefits the customers could roll-out features much faster.

Fig : Benefits realized by Parkar’s Clients using Containers

What more does Red Hat OpenShift offer?

A lot, I would say! Here are some of the top benefits:

Full-stack automated operations

Red Hat OpenShift offers automated installation, upgrades, and life cycle management for every part of your container stack.

  • It provides a single-step installation for Kubernetes applications.
  • It gives centralized administrative control for over-the-air updates and performance tuning with Kubernetes operators.
  • It offers continuous security through built-in authentication and authorization, secrets management, auditing, logging, and integrated container registry.

Developer Productivity

Red Hat OpenShift supports well-known developer tools and helps streamline your build and delivery workflows.

  • It has code in production-like environments with developer self-service and Red Hat Code Ready Workspaces.
  • It extends support for your choice of languages, databases, frameworks, and tools.
  • It offers streamlined workflows like automated container builds, built-in CI/CD, and application deployment.

Built-in service mesh for microservices

Microservices architectures can cause communication between the services to become complex to encode and manage. The Red Hat OpenShift service mesh abstracts the logic of interservice communication into a dedicated infrastructure layer, so communication is more efficient and distributed applications are more resilient.

  • It’s installed and updated via Kubernetes operators.
  • It incorporates Istio service mesh, Jaeger (for tracing), and Kiali (for visibility) on a security-focused, enterprise platform.
  • It frees developers from being responsible for service-to-service communication.

A unified Kubernetes experience on any infrastructure !

Red Hat OpenShift is a consistent Kubernetes experience for on-premises and cloud-based deployments. Assisted by unified operations and admin controls, all the workload is decoupled from infrastructure, thereby consuming less time spent on system maintenance and more on building critical services.

In conclusion, the Red Hat OpenShift platform does simplify quite a lot of things for IT teams in terms of abstraction, efficiency, automation, and overall productivity. Albert Einstein had once quoted: ‘Necessity is the mother of invention’; and that quote rings true throughout this article, which is to say at every step there are tools to ease operations, and to handle it most efficiently.

Parkar can help your organization in reaching your business goals. Contact us for more information about how we can make a difference.

References:

  1. https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/
  2. https://www.openshift.com/learn/what-is-openshift

 

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Top 5 Things to remember when defining a Microservices Architecture

1280 800 Parkar Consulting & Labs

“Nothing exists except atoms and empty space; everything else is opinion.” 

– Democritus

In other words, what’s the lowest unit of work that programmers can deal with easily without making the whole application one big inseparable monolith?

In order to stay ahead of the curve, organizations must transform themselves at different levels. Leadership transformation and digital transformation are examples of some of those initiatives.  Our focus here will be around Digital Transformation and how to measure its effectiveness.

CIOs now have a critical KRA around ‘Time to Market’, ‘Faster Delivery’ and ‘Business Availability’. Can a Microservices architecture underpin these metrics to create a foundation that helps CIOs better them? Most of the CIOs have a legacy platform or monolithic application to deal with. Among many, we need to look at transforming the monolithic application into independent, easily manageable services.

While there is an urgency, it is important to firstly understand the what, the why and how of Microservices Architecture.

Having worked in diverse industries helping midsize to Fortune 500 companies build their empires, at Parkar, we have seen how well-designed microservices have helped companies get on the fast-paced delivery and digital transformation journey.

In his book, “Business @the Speed of Thought: Succeeding in the digital economy”, Gates stresses the need for leaders to view technology not as overhead but as a strategic asset. If you haven’t yet jumped on to the Microservices journey, you will be lagging way behind your competitors and before you know it would be too late.

Today’s world is all about focusing on managing the core business. Your IT services and applications should scale to the need of speed of the business. With legacy applications, it’s going to be tougher. You need someone who has done it before to navigate through the complexities.

Microservices architecture is a methodology where the application is divided into set of smaller services, known as Microservices. It is based on proven software engineering concepts around agility, APIs, and Continuous integration (CI) continuous deployment (CD).

In practical sense, Microservices are SOA done right.

In various Parkar engagements, the Microservices have been implemented using different languages or technologies. During implementation, we interface the back-end services through an API gateway which provides a unified interface to front-end client applications.

For one of Parker’s customer engagements, even advanced analytics was incorporated to gain better insight into data and predictive models were deployed using machine learning algorithms.

For scaling (instancing up and down), life-cycle management, and load balancing, we at Parkar, have done through orchestration and load balancing services such as Kubernetes.

Digital Patient Care sub-application built with Microservices

Parkar Consulting Lab had a recent engagement with a healthcare organization, where we implemented the Microservices stack using the right architectural principles.

In the services and applications that we enabled, there was a patient care application that queried patient history, verified any drug allergies and family medical history. The application consisted of several components including the Web Client, which implements the user interface, along with some backend services for checking patient records, maintaining history and related analytics & visual graphs.

The application consisted of a set of services. A cross-section of the application is as below:

Fig 1: Example of Microservice

Benefits of Microservices

Much has been said about the advantages of Microservices based architecture over monolithic architecture. In short, we can list the following as a set of deciding parameters which make the Microservices architecture attractive.

Fig2: Microservices value realization in some Parkar implementations

Doing it Right is Critical for the Digital Transformation Journey!

Choosing the right partner in such transformation project involving Microservices could be challenging. Understanding the business nuances and then architecting the solution with future in perspective needs a deeper understanding.

We’ve worked with scores of customers till now. And what we’ve learnt is that they all are poles apart from one another. Each will have its own parameters, and the level of urgency and degree of dependence on the services will differ too. There is a right architecture for every use case, scale and target consumer (B2B v/s B2C).

No matter which one you choose, it should help you reduce development and operating costs while giving you added functionality and power.

Without much ado, we’ll proceed towards the key recommendation from our success stories and learnings.

Top 5 things to remember while adopting Microservices architecture

 1. Defining logical domain boundaries clearly

This includes –

  • Domain data management (separate or shared Databases between one or more Microservices)

Fig 3:  Domain Data Model

  • Well defined interfaces for communication means that each Microservice does exactly what it is supposed to do and nothing else, so there is no overlapping of purpose or functionality across Microservices.

  • Each Bounded Context of the domain maps to exactly one business Microservice, which in other words means the Microservice is cohesive within itself but not across other Microservices.
  • Events propagation and communication methods within application and from outside application (HTTP/HTTPS or AMQP).
  • API Gateway for single point of interfacing for the clients. (API Gateway or Backend for Frontend pattern)

2. Security Architecture:

Security design and implementation at multiple levels: authentication, authorization, secrets management, secure communication.

3. Orchestration:

Achieving scalability sounds simple but is by no means a simple task. Given the complex set of activities going on in parallel, orchestrating them at scale needs to be a well thought out plan. Using the Microservices tools is also important. Our experience at Parkar shows that Kubernetes and Helm are common technologies that have performed well for achieving scalability. It may look complex, but here are key aspects one should look into:

  • Service registry and service discovery
  • IP whitelisting
  • Authentication and authorization
  • Response caching
  • Retry policies, circuit breaker
  • Quality of service management
  • Rate limiting and throttling
  • Load balancing
  • Logging and tracing
  • Managed orchestration

4. Monitoring and health checks

Each service needs to be healthy to process requests. And hence monitoring service health is a key consideration. In one of the instances at Parkar, we have seen that sometimes a service instance is incapable of handling requests, yet it was running. And when we debugged further, we found that the specific service had run out of database connections. The key learning for Parkar from this, is that when this occurs, the monitoring system should generate an alert. The load balancer or service registry should also be designed intelligently so as to not route requests to the failed service instance. And ideally, the request should be routed to working service instances after checking the necessary pre-conditions.

5. Deployment (DevOps and CI/CD practices and infrastructure)

Faster release cycles are one of the major advantages of Microservices architectures. But without a good CI/CD process, you won’t achieve the agility that Microservices promise.

For example, assume, in an organization, there are sub-teams working on different features of an application. In case of monolithic application, something goes wrong for, say sub-team B, the release candidate of the application gets broken and there is no production rollout.

Fig 4: Deployment in case of monolithic v/s Microservices

On the current application modernization project, Parkar team was tasked with reducing the application deployment time while improving the quality and ability to add new features rapidly. With Microservices, we were able to develop and deploy services rapidly and decouple the business functionalities to enable rapid deployment.

The key for us, at Parkar, has been following the Microservices philosophy, where there is not a long release train where every team has to get in line. The team that builds service “A” can release an update at any time, without waiting for changes in service “B” to be merged, tested, and deployed.

Now that we have looked at the key things to consider before you zero down on the Microservices architecture, there are certain things you must ask your team or your IT service implementation partner before you invest your money and time.

What are these questions?

  1. Where to start: Start point often becomes a major question for many seniors. Are you developing an application from scratch or are you transforming your legacy application into Microservices based architecture?
  2. How to Segregate: What are the logical domains you could segregate your application into? Methods such as strangulation strategy can often help to migrate in phases.
  3. How do you want to separate front-end and back-end services?
  4. What is your plan for deployment? Do we have the right environments develop, QA, staging and production? Is the deployment pipeline ready?
  5. Where to deploy: Whether on-premises, hybrid infrastructure, single public cloud or multi-cloud?

Whenever Parkar gets involved with any customer, answers to these questions has helped in a big way to crystalize the outlines of microservices architecture. It also helped plan for incremental migration from monolithic to microservices avatar for the customer application.

Conclusion

Every enterprise has certain roadmaps when it comes to charting its journey. Clearly, the Digital Transformation journey is a step of multi-step evolution and one of the key steps is moving to Microservices.

At Parkar, we have experienced that right Microservices architecture helped our customer organization in their roadmap of Legacy to Microservices migration or building a new service scalable, secure and quick to deploy.

It’s critical to ask the right questions (as mentioned above) and then follow through the key 5 things at the minimum, before you embark on the Microservices journey enroute to your digital transformation journey.

On this note, we shall let you ponder over the benefits and principles of Microservices architecture. Meanwhile, we at Parkar Consulting Lab, will gear up to bring you something more engaging from the world of Application Modernization and Digital Transformation. Stay tuned.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

serverless-cloud-computing

Serverless Cloud Computing: current trends, implementation and architectures

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It seems like everyone’s talking about serverless computing these days. But wait…when did we all stop using servers? In case you were wondering, servers are still necessary — serverless just means the average cloud user doesn’t have to worry about them anymore. You can now code freely without having to write an endless number of scripts to manage server provisioning.

 

serverless-trends

The interest over time for Serverless Computing

Also called FaaS (Functions as a Service), serverless computing can bring nearly limitless, provision-less computing power to your applications. Here’s how it can make a difference for you.

Introducing Serverless Cloud Computing

Serverless computing, of course, still uses computers—serverless doesn’t mean we’re running applications without using computing resources at all. It does mean that users of the serverless computing service don’t have to provision virtual machines or directly manage the computers. This frees up developers to focus on their application development instead. For development teams, this makes development a lot easier.

For companies offering SaaS (Software as a Service) or running their own computing tasks, it makes it a great deal easier to get the right computing resources and have applications run.

Users taking advantage of serverless computing for their applications find that it has a lot of practical value. In a business setting, serverless computing essentially extends what organizations are able to accomplish with their applications and enables them to provide greater value to their customers.

In fact, serverless computing is valuable for many reasons. For instance:

  • Scalability: Serverless computing makes it much easier to scale computing resources to meet the needs of your application.
  • Access on-demand computing: Computing resources are available immediately, whenever the application needs them or whenever users initiate the system to start. There’s no waiting around for computing time to become available, because it’s already waiting and can be quickly deployed or used on schedule.
  • Unlimited resources: Truthfully, serverless computing resources can seem almost unlimited. Your application can use whatever it needs to run, even if you suddenly have additional demand you didn’t plan for. While there’s no such thing yet as completely unlimited computing resources, serverless computing can get really close.
  • Time-to-market: If you’re a developer, being able to quickly have the right resources you need to get your software ready is a really big deal.
  • Security: Whenever human error is possible, it’s bound to happen eventually that someone will make a mistake. Serverless computing helps to protect against the inevitable. This makes it easier for you to focus on your work instead of preventing every possible security problem.

For these and other reasons, serverless computing is now more popular than ever before. It helps companies achieve their computing needs without having to spend so much time on computing resource management.

Switching from traditional servers to serverless computing can generate really mind-blowing savings, like cutting your monthly costs from $10k to just $370. Wow.

Before and After the Serverless Era

Doing anything without serverless computing can be fairly limiting once you’ve experienced the benefits. Getting here to the point where this technology really became available did take a while.

Yesterday’s Cloud, Today’s Cloud & Tomorrow’s Cloud

Just like A Christmas Carol’s three ghosts, the cloud has three personalities, too—let’s talk about yesterday’s cloud, today’s cloud, and the cloud of the future.

Originally, just the idea of outsourcing your computing to another network was a big deal. That other network’s servers could augment your existing computing resources and enable you to tackle much bigger projects than you could before. This was the very beginning of the cloud. With the Internet in its early days and basic server networks available to help you get a little extra computing help, it had a lot of promise for early software development and operations.

That’s yesterday’s cloud. It had severe limitations, like very limited overall resources. It was about the beginning of SaaS, and data analytics was on the horizon but not quite yet a big If you needed to scale, that might’ve required a discussion with your vendor and some changes onsite at their facilities.

At the end of the day, you were running virtual machines, but you still had to worry about the machines—not their hardware, because someone else was doing the maintenance—but you did have to manage your computing resources closely.

Today, there’s another cloud in town. It’s trying to free us from this close management. Cloud 2.0 has often been described in terms of data. Big data, analytics, and information. With fewer data constraints, companies are free to make the most of data in new ways.

And tomorrow, the cloud’s continued growth will bring us even more possibilities, making data use more practical for a variety of different industry applications.

Serverless Implementation Examples

In recent times, many organizations have successfully transitioned their applications over to serverless computing.

For instance:

  • Trello
  • Soundcloud
  • Spotify

For event-driven applications, the move or a partial move to serverless makes sense. These applications rely a lot on input from users that triggers the need for computing resources. Until specific events are triggered, the applications may need very little at all—but once a function triggers, the computing power needs increase almost asymptotically very rapidly. In many cases it’s tough to scale these applications without readily-accessible and affordable computing power.

Why Should You Move to Serverless?

Serverless is ideal for applications that have a lot of function-driven events, such as events driven by a mouse click. It’s great for systems that rely on user engagement—and require big bursts of computing power at key moments. It would be hard to have on premises infrastructure to meet these needs. It also doesn’t make sense to have to recreate resource management processes and micromanage machine use when you’re creating or operating software that works this way.

From a technical standpoint, it offers benefits such as:

  • Supports all major server side languages/frameworks like Node.js, Python, Java, Scala and Kotlin.
  • Software lifecycle management from a single platform i.e. you can build, deploy, update and delete.
  • Safety function for smooth deployment and resource manager
  • Minimal configuration required
  • Functions optimized for CI/CD workflows
  • Supports automation, optimization and best practices of enterprise computing
  • 100% extensible functions and frameworks

Key Steps in Migrating Existing Structure to Serverless

Making the transition to serverless computing doesn’t have to be too difficult. As long as you start with a viable plan and a willingness to adapt, you shouldn’t have too much trouble.

 

 

Here’s a few steps to get you started. You’ll be setting up an account with a provider and testing it with your own function. From there, you can quickly start tailoring the service to your needs.

Adapt this test process to your own applications and business needs, but choose something simple so you can play around with your new account:

  1. Begin with an application, or an idea.
  2. Create an account with a serverless computing provider, such as AWS Lambda, Google Cloud, or Microsoft Azure.
  3. Prepare to test your new account. To do so, you’ll want to create two buckets. For one of the buckets, you’ll upload photos or another file type you’ll be transforming. The other will receive these files once you’re done.
  4. In your management console, you’ll now create a new function using the two buckets you just set up. Specify how the buckets will be used.
  5. Name your function and set it aside for later.
  6. Create a directory and set up your workspace on your local machine.
  7. Write a Javascript file or other code to use files in your new account(Here’s an example using AWS)
  8. Upload.
  9. Test your function.

Once you’ve tested the process, you can start looking at how existing code (and new, from-scratch code, too) can leverage serverless computing capabilities.

 

Is Serverless the Future of Cloud Computing?

With so many uses and promises for the future, serverless is likely to continue playing a prominent role in the future of cloud computing. It’s not for every application and company, but for event-driven functions you need a little (or a lot) of on-demand computing power for, it makes sense.

Your business may benefit tremendously from making a move to the serverless cloud. Parkar can help your organization make sense of the cloud and how it can help you reach your business goals. Contact us for more information about how we can make a difference.

 

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Machine-learning-for-enterprises

Introduction to Machine Learning for Enterprises

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Machine Learning, a sub-field of Artificial Intelligence, is playing a key role in a wide range of industry critical applications such as data mining, natural language processing, image recognition, and many other predictive systems.

The goal of Machine Learning is to understand the structure of different data sets and build models that can be understood and utilized by industry systems and people.

It provides automated study and extraction of insights from data by the means of different ML approaches, algorithms, and tools.

In this competitive age, how you use the data to better understand your systems and their behavior will determine the level of success in the market. Machine Learning goes beyond traditional Business Intelligence and accelerates data-driven insights and knowledge acquisition.

Although, it has been around for decades; due to the pervasiveness of data that is being generated and infinite scalability of computing power, it has taken the center stage now.

In this comprehensive guide, we will discuss methods, ML frameworks, predictive models and a wide range of machine learning applications for major industries.

Common approaches to Machine Learning

If you want to predict the traffic of a busy street for a smart city initiative, you can run it through ML algorithms and feed the past traffic data to accurately predict future traffic patterns. Industrial problems are complex in nature which means we must invent new but very specialized algorithms that can solve impractical problems.

Hence, there are different approaches to which ML models can be applied to train a software system.

Supervised Learning

It takes place when a developer provides the learning agent with a precise measure of its error which can be directly compared with specified outputs. In generic terms, supervised learning is ideal when you have what you want the machine to learn.

The algorithm is mostly trained on a pre-defined set of training examples which enable the program to reach an accurate conclusion when given new data.

supervised-learning

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classfication-vs-regression

Unsupervised Learning

In unsupervised learning, data is unlabelled, so the learning algorithm is left to find commonalities among its input data. As unlabelled data are more abundant than labeled data, machine learning methods that facilitate unsupervised learning are particularly valuable.

The goal of unsupervised learning may be as straightforward as discovering hidden patterns within a dataset, but it may also have a goal of feature learning, which allows the computational machine to automatically discover the representations that are needed to classify raw data.

Unsupervised learning is commonly used for transactional data. You may have a large dataset of customers and their purchases, but as a human, you will likely not be able to make sense of what similar attributes can be drawn from customer profiles and their types of purchases. With this data fed into an unsupervised learning algorithm, it may be determined that women of a certain age range who buy unscented soaps are likely to be pregnant, and therefore a marketing campaign related to pregnancy and baby products can be targeted to this audience to increase their number of purchases.

Without being told a “correct” answer, unsupervised learning methods can look at complex data that is more expansive and seemingly unrelated to organize it in potentially meaningful ways. Unsupervised learning is often used for anomaly detection including for fraudulent credit card purchases, and recommender systems that recommend what products to buy next. In unsupervised learning, untagged photos of dogs can be used as input data for the algorithm to find likenesses and classify dog photos together.

Machine Learning frameworks

machine-learning-framework

In this section, we will talk about some of the best frameworks and libraries available for Machine Learning. Each of these Frameworks is different from each other and takes much time to learn. During the time of making this list, we took care of features other than the basic ones: user base and community & support was one of the most important parameters. Some frameworks are more mathematically oriented, and hence geared more towards statistical than neural networks. Some of them provide a rich set of linear algebra tools; some are mainly focused only on deep learning.

 

TensorFlow

Tensor Flow is developed by Google to write and run high-performance numerical computation algorithm. It is an open source ML library for data-based programming which uses data flow graphs. Tensor Flow offers an extensive amount of functions and classes that we can use to build various training models from scratch.

Earlier, we talked about different machine learning methods, Tensor Flow is capable to handle all kinds of regressions, classifications algorithms and neural networks on both CPUs & GPUs.  However, most of the functions are complex so it’s difficult to implement at the early stages.

What makes Tensor Flow the perfect library for enterprises:

  • Based on Python API
  • Truly portable as it can be deployed on one or more CPUs or GPUs and can be served simultaneously on mobile, computer with a single API.
  • It’s flexible enough to run it on Android, Windows, iOS, Linux and even Raspberry Pi.
  • Visualization
  • It has checkpoints to manage all your experiments
  • The community is large to help with any issues.
  • Acceptability across the Industries as tons of innovation projects are using TensorFlow.
  • It lets you handle the derivatives automatically.
  • Performance

Tensor Flow is being used by the top most companies in the world including:

  • Google
  • OpenAI
  • DeepMind
  • Snapchat
  • Uber
  • eBay
  • Dropbox
  • Home61
  • Airbus
  • And Tons of new-age startups

Spark

Spark is an analytics engine based on a cluster-computing framework built for large-scale data processing. The initial development was done at Berkeley’s lab but later was donated to Apache Software Foundation.

With some advanced features, it creates spark label vectors for you thus carrying away much complexity to feed to ML algorithms.

Advantages of Spark ML:

  • Simplicity: Simple APIs familiar to data scientists coming from tools like R and Python
  • Scalability: Ability to run same ML code on small as well as big machines
  • Streamlined end to end
  • Compatibility

CAFFE

Caffe is an open source framework under a BSD license. CAFFE (Convolutional Architecture for Fast Feature Embedding) is a deep learning tool which is mainly written in CPP.

It supports many different types of architectures for deep learning focusing mainly on image classification and segmentation. It also supports Graphic and CPU based acceleration for neural based engines

CAFFE is mainly used in the academic research projects and to design startups Prototypes. Even Yahoo has integrated caffe with Apache Spark to create CaffeOnSpark, another great deep learning framework.

Advantages of Caffe Framework:

  • Caffe is one of the fastest ways to apply deep neural networks to the problem
  • Supports out of box GPU training
  • Well organized Mat lab and python interface
  • Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
  • Speed makes Caffe perfect for research experiments and industry deployment.
  • Caffe can process over 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still. We believe that Caffe is among the fastest convent implementations available.

 

TORCH

Torch is also a machine learning open source library, a proper scientific computing framework. Its complexity is relatively simple which comes from its scripting language interface from Lua programming language interface. There are just numbers (no int, short or double) in it which are not categorized further like in any other language. So, it eases many operations and functions.

Torch is used by Facebook AI Research Group, IBM, Yandex, and the Idiap Research Institute, it has recently extended its use for Android and iOS.

Advantages of torch framework include:

  • Flexible to use
  • High level of speed and efficiency
  • Availability of tons of pre-trained ML models

SCIKIT-LEARN

Scikit-Learn is a very powerful free to use Python library for ML that is widely used in Building models. It is founded and built on foundations of many other libraries namely SciPy, Numpy, and matplotlib, it is also one of the most efficient tools for statistical modeling techniques.

Advantages of Sci-Kit Learn:

  • Availability of many of the main algorithms
  • Quite efficient for data mining
  • Widely used for complex tasks

 

Business/Operational Challenges while implementing Machine Learning

To better understand how ML may benefit your organization — and to weigh this against the potential costs and downsides of using it — we need to understand the major strengths and challenges of ML when applied to the business domain.

High performance, efficient, and intelligence

ML can deliver valuable business insights more quickly and efficiently than traditional data analysis techniques because there’s no need to program every possible scenario or require a human to be part of the process — taking people out of the process. ML can process higher volumes of data, it also has the potential to perform much more powerful analytics. ML’s intelligence, provided by its ability to learn autonomously, can be used to uncover latent insights.

Pervasive Nature

Due to higher volumes of data collected by increasingly computing devices and software systems, ML can now be applied to a variety of data sources. It can also solve problems under a variety of contexts.

For Instance, it can be used to add unique functionalities to enterprise systems that may otherwise be too difficult to program. We’re already using to solve large-scale process improvement initiatives to support business objectives for many industry-leading organizations. Programs like Six Sigma is already being replaced by many corporations, and they’re leaning towards training ML algorithms to enhance their business process.

Uncover hidden insights

It can handle nonspecific and unexpected situations. When organizations are uncertain about the value or insights inherent in their data — or are confronted with new information they don’t know how to interpret — ML can help discover business value where they may not have been able to before.

With all the benefits and capabilities, there are some challenges that become a roadblock for organizations in adopting ML in their Industry such as:

It requires considerable data and computing power as it applies analytics to such large amounts of data and runs such sophisticated algorithms, it typically requires high levels of computing performance and advanced data management capabilities. Organizations will need to invest in infrastructure to handle it or gain access to it through the on-demand services of external providers, such as big data analytics cloud providers.

It adds complexity to the organization’s data integration strategy. ML feeds off of large amounts of raw data, which often come from various sources. This brings a demand for advanced data integration tools and infrastructure, which must be addressed in a thorough data integration strategy.

 

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

BI-for-Enterprise

A comprehensive guide on smart Business Intelligence for Enterprises

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The best way to understand Business Intelligence, is probably an overview of how it works in action: taking an organization from data-rich but UN-optimized in data usage, to truly data-driven and able to fully reap the benefits of their information and technology.

Place yourself in the shoes of an established organization – hypothetical, of course – from a data-rich industry like healthcare, IT, or retail. By nature of the tools necessary just to conduct your business, you already have massive amounts of raw data on hand, and you probably made a not-insignificant investment to acquire it. This vast store of data might include, as examples – depending on your industry:

For a medical corporation:

  • Patient records
  • Diagnosis reports
  • Patient surveys

For a retail organization:

  • PoS data
  • In-store sensors
  • Footage from security cameras
  • Demand data based on customer footprint

This is where business intelligence at the enterprise level comes in. Previously, ETL design and implementation for the data warehouse was ignored by various organizations but slowly they started adopting a modern approach to handle the data mining, analytics, optimization and reporting.

Let’s have a look at the difference between is a look at traditional BI approach vs Modern approach:

Modern BI Approach

By taking a holistic view of your entire organization and the data gathered in every function, enterprise BI knocks down silos and provides advanced solutions, with a deep understanding of how disparate data from across the enterprise can, when used the right way, provide maximum benefit to the entire enterprise.

While the examples above are broad and hypothetical, many major companies have already benefited from enterprise BI, ranging from giants like Amazon and Netflix, to relatively smaller, niche organizations like music analytics platform Next Big Sound, and digital game developer Miniclip. Read on to discover more about how enterprise BI works.

 

Major Components of Business Intelligence

Although Business intelligence and analytics implementation differs in the details for every organization, there are a several underlying principles that are relatively constant. These include:

Data Optimization

A primary component of BI is making data more efficient to access and analyze. This is achieved in several ways:

  • Data aggregation (a centralized data warehouse enabling faster querying);
  • Data cleaning (standardizing data so that it’s better able to “communicate” with other data);
  • OLAP (online analytical processing, a way of quickly analyzing data via multiple dimensions)
  • Denormalization (optimizing data query times in several ways, including allowing redundant data storage)

Real time analytics

True BI is an “always-on” process, enabling agile, nimble responses to inefficiencies or problems as soon as they’re detected. In practice, this means access to real-time metrics and dashboards, as well as an alert system that ensures that no time is wasted in producing a response.

The data from sources like sensors, markets, logs, social interactions, purchase/spends can be processed for Real time analytics.

real-time analytics

Predictive elements

Your raw data is a record of history: how a process has been performing, how customers have been making purchase decisions.

With BI, the vast amount of historical data on hand is put to use in vast simulations, drawing on statistical inference and making use of ML and AI tools to provide probabilities for future events and behaviors, which can then be put to use to make more informed decisions in a broad range of areas, including development, marketing, sales, budgeting, and even hiring and promotion.

predictive analytics

Credit:Dataschool

KPI insights

KPIs (key performance indicators) are often seen as an intractable, “conventional wisdom”-driven metric. Enterprise BI can detect surprising data patterns that may change the way you look at, choose, and assess your KPIs – ultimately resulting in improved performance and results.

KPI Insights

 

Unstructured and unconventional data sources

The typical image of big data, and databases in general, is row after row of numbers, along with simple text data like names. A key differentiator in BI is the ability to draw on unstructured data, which is typically in an unconventional, “un-quantifiable” format like long-form text, such as customer reviews or comments.

The benefits of the advanced technology behind BI mean that this data can be analyzed on its own and in relation to more traditional data, providing even more easily accessible, digestible, actionable information.

Structured-unstructured

Comparison of Top BI tools

Choosing the right BI tool means asking a few questions of your organization and your stakeholders: Which tool is right for analyzing the data you have on hand, and the market data needed to make decisions? Which tool makes sense for the people who will be using it? And which one can produce the output and results that you’ll need?

Below, we take an in-depth look at several of the solutions available:

Cognos

Developed by IBM, Cognos is widely used and delivers one of the most user-focused, intuitive end user interfaces available. It’s ideal for users who frequently make use of data in presentations, such as business cases to upper management.

Key features include real-time analysis, ready-to-present data visualization tools, and the ability to quickly share information with colleagues.

Domo

Another broadly-used tool, Domo is designed for relevance to the entire organization for businesses in almost any industry. Domo’s BI reporting tool is especially multifaceted and powerful.

DomoKey features include real-time alerting and a robust mobile app for data access and management from anywhere.

Qlik

We’re now looking at tools with more specialized benefits. Qlik is ideal for organizations where data might be more limited, difficult to clean, or just considered “incomplete.” It’s ideal for organizing and analyzing even these types of “difficult” data, providing insights which otherwise may not have been possible.

Key features include an associative engine which connects all available data in such a way that makes it possible to infer the conclusions described above, even in sub-optimal data sets.

Pentaho

Yet another tool which is ideal for pulling in data from all areas of the company and enabling it to “talk” to each other to generate useful analytics and reports. Pentaho is ideal for companies involved in production or manufacturing, as it specializes in integrating data from connected, IoT (Internet of Things) devices.

Pentaho

Key features include the above-mentioned IoT focus and advanced visual report- and analytics-generation tools.

Spotfire

Taking a more predictive, probability-driven approach, Spotfire draws heavily on artificial intelligence for organizations in competitive industries where which trend forecasting is a critical need.

Spotfire

Key features include real-time analytics, identification of potential data inconsistencies, and location-based analytics.

How Is Business intelligence being used?

We’ve discussed several business intelligence use cases already, both in the introduction (with our hypothetical companies), as well as some of the optimal uses for the BI tools above. Now, let’s take a closer look at some of the practical benefits of BI across various industries.

BI in Banking & Finance

  • Via the data warehouse and BI tool, centralize access to disparate internal KPIs like lead time, cycle time, sales, and more, to analyze and make decisions regarding overall employee performance
  • Analyze customer satisfaction in key areas like service and performance, identifying areas for improvement to increase value to customers
  • Zero in on process improvements and service offerings that can help court targeted, high-value clients
  • Track and process data on internal processes and company culture, using the results to identify process efficiencies and optimize the environment for growth
  • Harness the potential of the data warehouse and BI tool to generate reports and other personalized content to improve customer relations

BI in Retail

  • Use PoS & beacon data to offers discounts to customers when they enter the store
  • Optimize inventory and stock management by drawing on RFID, PoS, and/or beacon data to order, fulfil, and stock merchandise
  • Draw on continuously processed data to drive real-time merchandising updates, optimizing the customer path at a granular level and increasing spend
  • Tailor inventory orders with demand and fulfillment forecasting informed by real-time supply chain analytics like seasonality, shipping distance, economic and market factors, and more.
  • Turn personnel scheduling into an efficient, fast process by utilizing data based on promotions, season, historical sales, and competitor

 

BI in Healthcare

  • Predict specific efficacy scenarios for different medications and treatments on a patient-by-patient basis using data drawn from testing, wearable fitness devices, and the wealth of historic data.
  • Centralize and provide easy access, via BI tools, to the entire contents of a patient’s medical history, collected over years from numerous sources.
  • Create easily understandable charts and graphs with intricate, visualized details on patient health, treatment plans, test results, medication regimens, and more.
  • Identify disease risks with increased accuracy based on both personal medical data and a broad range of environmental factors and data
  • Improve and streamline communication between medical care providers when a patient is visiting different facilities and specialists.

BI in Manufacturing

  • Store and optimize data pulled from existing connected, IoT devices which previously had separate, isolated data management and storage systems, increasing the ROI for these installations.
  • Analyze and classify production alarms and errors in real-time, allowing for faster diagnostics and remedies.
  • Install and optimize predictive maintenance protocol based on machine and process data, creating a more effective practice than simply following set schedules.
  • More efficient automation through continuous limit monitoring, reducing or eliminatingmissed alarms and allowing for closer limit tolerances
  • Data warehouse and real-time analytics are key to the innovative cyber physical systems that will define Industry 4.0 – improvements in efficiency, quality, safety, and more.

BI in Supply Chain Management

BI-in-supply-chain

  • Fully integrate, normalize, and analyze data from all steps of the supply chain, from raw material through to the facility and/or retail location itself, to identify efficiencies and improve forecasting.
  • Evaluate current suppliers and identify potential new partners through historical data: on-time delivery, damage rate, customer satisfaction, and more.
  • Anticipate, respond to, and neutralize cost fluctuations with historical and real-time global data
  • Optimize ordering and delivery schedules more quickly and efficiently
  • In real-time, identify and respond to abnormal fluctuations in commodity pricing, adjusting ordering and inventory accordingly and immediately.

The Business Intelligence Process

With an understanding of what business intelligence tools look like, and some examples of how they can be put into use – and what the benefits are – let’s close with a look at the details of the process itself. These steps are the underpinning of what ultimately leads to better processes and results for your organization.

Pulling up the data

In this initial step, all aspects of the available raw data are assessed: scope, type, source, state/suitability, and more. This initial audit defines the methodology and, potentially, the tool or tools that will be used to clean, standardize, aggregate, and optimize data for centralized use.

Tool deployment and installation

Once the most suitable tool or tools have been identified, the service provider will install and deploy the tool, either as a managed solution, a SaaS solution, an on-site installation, or some combination of the above. Client and customer concerns and requirements, such as security and auditing, must be considered here. At this point, adoption and training efforts within the organization may begin.

Big data integration

With the BI tool installed, all existing data can be analyzed to provide the desired analytics and insights. More importantly, new data is continuously and efficiently being integrated alongside existing data, providing the basis of the real-time analysis and alerts that are a key component of BI.

Cloud integration

A cloud solution is most often the right choice for the scale of data included and generated in a BI implementation. The right service provider will combine the storage benefits and efficiencies of the cloud with performance-maximizing practices like de-formalizing data for optimal results.

Visualizations

BI dashboards take all the data being constantly analyzed and provide a broad range of visualization and summary choices to make it presentable, usable, and actionable. Visualizations can range from the easily digestible, like charts and graphs (which may appear simple but are driven by advanced BI analytics), to more intricate formats like heat maps, candlestick charts, and beyond.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

3 Emerging Technologies Transforming the Health Care Landscape

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The rate at which technology is changing our everyday lives is truly remarkable, and one especially transformative area is health care. Emerging technologies are primed to disrupt many aspects of patient care in increasingly advanced ways, and they’re making their way into health care on a minor scale in mobile and wearable technologies.

These devices started as popular non-medical fitness monitors but are beginning to expand into medical-grade wearables, home health monitoring tools, and mobile care apps. Where else will technology take health care — and what do these advances mean for IT professionals?

1. Wearables, mobile apps, and big data

Researchers predict the wearable medical devices market will reach $14.41 billion by 2022, up from $6.22 billion in 2017. Until now, these devices had been limited to individual fitness trackers that connected to smartphone apps, but they are poised to offer real-time access to medical records as well as diagnostic and treatment functionalities. This could help empower patients to take control of their health, improving patient outcomes and saving health care providers and patients time.

To make this transition, medical device manufacturers, health care records system providers, IT developers, and health information regulators will need to learn how to integrate patient-generated data into their workflows and products. Privacy and security concerns, data relevancy to clinical situations, and big data handling are the biggest challenges facing the widespread adoption of clinically relevant personal medical-grade devices. Health IT managers and developers may need to look at Internet of Things (IoT) application programming interfaces (APIs) and standardization techniques to help handle this unstandardized user-generated data.

2. Machine learning and artificial intelligence (AI)

AI and cognitive computing technologies are able to integrate patient-generated and IoT big data. These technologies use algorithms to mine large datasets, recognize patterns, and make connections between disparate items in ways that mimic the human mind — but much faster and more comprehensively than any medical professional can. Savvy developers can tie these cognitive computing platforms to electronic health records to spot trends not only within a single patient’s records but also across patients to assist doctors in recognizing anomalies as well as diagnosing and treating patients with similar conditions.

AI is also likely to play an important role in researching and developing treatments for many health conditions. Using large centralized data repositories, these AI systems can store vast amounts of data generated through health care systems, the IoT, wearable medical devices, and more to gain deeper insights into some of the most impactful health issues such as heart disease, diabetes, Alzheimer’s, and autism. Health care providers, developers, and IT decision-makers alike will need to work together to develop big data gathering methods and analytical tools to best take advantage of the tremendous benefits machine learning and AI can offer health care industry insiders and their patients.

3. Blockchain

The third technology trend transforming health care today is blockchain. Blockchain is the technology behind Bitcoin, the cryptocurrency that’s shaking up the financial world. Blockchain is a massive distributed network of replicated databases containing records stored on an encrypted ledger. No central administrator exists, users can change only the blocks of records they have access to, and software time-stamps any entries or updates and syncs them across the other networked databases. Because of the massive amounts of data surrounding the health care industry as well as the need for security and adherence to privacy regulations, blockchain offers tremendous potential for many areas of the industry, including secure patient medical record storage, clinical trial data privacy, drug development, supply chain integrity, as well as medical billing and insurance claims. Although still in its infancy, blockchain will likely have a significant impact on the health care industry going forward.

As technology continues to disrupt the health care field, both patients and providers will likely benefit from improved diagnostic techniques, treatments, record keeping, research, security, and so much more. Only by staying abreast of these technological advancements will software developers and IT decision-makers find opportunities to optimize their health care software projects to integrate with and take advantage of blockchain, AI, and wearables, allowing them to offer the medical advantages this new technology enables to their patients and stay ahead of the competition.

Our experts at Parkar Consulting & Labs have the knowledge and expertise to help you make the most of emerging technologies so you can pass them and the value they bring to your customers. Contact us today to learn how we can help.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

3 Big Ways Artificial Intelligence Is Changing Software Development

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Agile technologies such as DevOps and continuous integration, continuous delivery (CI/CD) practices have brought about positive changes for software developers over the past decade. From faster time to market and greater collaboration between development and operations teams to fewer end-user issues and improved testing, DevOps has changed the way developers work. In a similar vein, artificial intelligence (AI) is poised to change how organizations and their engineering teams approach software development.

AI’s promising potential

Software engineers are tasked with creating solutions for the myriad problems, challenges, and everyday organizational tasks in most every industry imaginable. Ironically, they even develop the tools that make their development processes easier. AI is well-suited to helping software engineers develop these intelligent software tools because it can learn from and replicate human behaviors. Because of this, AI and machine learning algorithms can impact nearly all areas of software development.

Best uses for AI in software development

AI and machine learning have already made big impacts in software development. Here are three of the most important ways it is changing the development landscape and the evolving role of software engineers.

  1. Estimating delivery schedules — When development teams work together for long periods of time, they become fairly adept at estimating delivery times, although they may still encounter challenges due to a variety of influencing factors, including flawed code and changing user demands. AI can help development teams make more accurate estimates, even with the numerous and diverse factors that come into play. And as the AI programs gather more data and learn from other development projects, the accuracy of those estimates is likely to continue to improve.
  2. Project management — AI systems can take over daily project management tasks without the need for human input, according to The Next Web article. Over time, they can understand project performance and use that knowledge to form insights, complete complex tasks, and help human project managers make improved decisions.
  3. Testing and quality assurance — Developers are creating tools that use AI to detect flaws in code and automatically fix them, according to the Forbes article. This is the logical next step after testing automation and will likely lead to higher-quality software and improved time to market. Software engineers could have less involvement in testing mechanics but would shift their roles to approving and acting on test findings. In other words, AI could streamline software testing by providing the right data to the human engineers who can then make better decisions.

evolution of the programmer

Best practices and AI’s future

Based on these changes alone, it seems AI and machine-based learning are primed to disrupt the software development field. What does that mean for development company leaders, software engineers, and software development in general?

Overall, AI will likely help software development become better, faster, and less expensive. However, for this to happen, engineers would have to learn a different skill set so they could build AI into their development toolboxes. They’d need more data science skills and a better understanding of deep learning principles to reap the full benefits of machine-based learning. Also, instead of turning to logging and debugging tools to find and fix bugs, engineers would need tools that allow them to question the AI to find out how and why it reached a particular conclusion. In addition, AI could allow more tasks to run autonomously and require fewer daily management tasks. Finally, developers could use AI for routine tasks so humans can focus on what makes them human: thinking creatively according to the problem’s context, something that AI has not yet mastered.

Will AI eventually replace the human element in software engineering? Not likely, but it certainly has the potential to make development faster, more efficient, more effective, and less costly all while letting engineers and other development personnel focus on honing their skills to make better use of AI in their processes.

For the best in thought leadership on emerging technologies, look to the software development experts at Parkar Consulting & Labs. Contact the Parkar professionals today.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

Rapid Test Automation

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In today’s competitive landscape, enterprises and organizations need to assess the best way to execute test automation for their different projects. This has developed an awareness regarding the value that automated software testing can bring.

A well-established test automation methodology brings in predictability, repeatability, and agility, and thereby drives software engineering to a higher degree of quality. Test Automation Assessment helps understand whether or not an application requires to be automated. Based on certain criteria, recommendations are made that help decide whether an application really needs to be automated, and the advantages that may thus be achieved. Test automation assessment is usually performed either for customers with an existing test automation framework, or for clients with a want for a new test automation framework.

However, to continuously evolve best quality applications, organizations need to consistently test the automation process. Rapid Test Automation Assessment (RTAA) is a commonly used approach that enables organizations test the process.

What is a Rapid Test Automation Assessment?

If the test automation assessment is to be achieved within shorter timelines than the normal time frames, RTAA becomes a necessity. RTAA refers to a fast analyses and execution of a TAF that fits in a small environment, specifically created based on the criticality of the test cases.

4 Steps for a Rapid Test Automation Assessment

  • Understand the Existing System: This involves analysing the current state of quality assurance and testing methodologies being followed. An inceptive understanding of the system, their technology, processes and testing information will be taken up as part of the assessment. An understanding of the system is known through knowledge of the objectives, a know-how of the technology stack is taken up, user flows is identified, and analysis of the manual test cases if any.
  • Assessment: Utilization of the tools and the extent of their automation readiness approach will be identified in this step. A requirement traceability matrix is prepared that details the extent of test cases, business and details of the functional requirements and areas of quality enhancement. Tool feasibility and confirmation in addition with automation ROI analysis is also taken into account as part of the assessment approach. But foremost, the top few of the most business-critical test cases are recognized.
  • Proof of Concept (POC) to Demonstrate Feasibility: This phase comprises of implementing a TAF for the environment and executing only the identified critical test cases for conducting a POC. The POC will help identify financial and operational benefits and provide suggestions regarding the actual need for complete automation.
  • Recommendations & Implementation: Specific test automation tools, automation feasibility, and automation approach will be clearly defined in this phase.

Primary assessment focus areas are automation framework, automation integration and its fitment in the SDLC. In automation framework areas, reusable function libraries, test object maps, exception, error management etc. will be detailed.

In the automation integration focus area, test management, source code repository, and defect management, continuous build management etc. will be defined. In the fitment in SDLC focus area, details like existing /target automation coverage, metrics, test prioritization etc. will be listed.

Outcome of the Rapid Test Automation Assessment

The output of this rapid test automation recommends appropriate automation strategies and executes them to improve testing quality, minimize testing effort, schedule and ensure return on investments. A comprehensive report of the process, tools and people will be provided. Predictions for effective project management, simple details on the response and demand for continuous communication with business teams and the need to absorb changes recommended by business will be defined. Execution of tools to effectively track defects and a well-defined test strategy document covering all aspects of testing requirements will be provided.

Innovative Director of Software Engineering. Entrepreneurial, methodical senior software development executive with extensive software product management and development experience within highly competitive markets. I am an analytical professional skilled in successfully navigating corporations large and small through periods of accelerated growth.

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