Digitizing, Transforming, and Refactoring for Efficiency.
From monolith to microservices transformations, kubernetes, cloud native, devsecops, and data analytics on Amazon AWS, CCT is your partner for success!
As the principle form of application modernization that allows you to finally organize all of your solutions around your business capabilities. In addition to making it far easier to both build and maintain apps, Monolith to Microservices Transformation improves productivity and speed, creates an essential flexibility for your organization in terms of using technologies, and increase scalability.
This is a technology used to develop applications built with services that are then packaged in containers. At that point, they can be deployed as microservices and can easily be managed on an incredibly elastic infrastructure via all of the most agile DevOps processes and continuous delivery workflows that you’ve been searching for.
This is a DoD best practice for not only rapid software development, but secure development on Kubernetes as well. Not only does this bring with it the benefit of reduced mean-time to production, but it also also allows you to leverage advantages like fully automated risk characterization, increased deployment frequency and much, much more.
Cloud Computing has taken the world by storm. Many organizations that thought they can build their applications with their know-how and tools have now started realizing the benefits of Amazon AWS Cloud Computing with Kubernetes and Terraform. This realization has resulted in a rapid increase in the adoption of cloud technologies.
Cloud Computing on Amazon AWS including data analytics and Kubernetes, refers to a variety of services ranging from virtual computing to storing and accessing data and other computing services over the internet. Many on-demand and scalable services such as processing, storage, networking, and applications are part of Cloud Computing.
One of the most common uses of Cloud Computing is to run heavy processing workloads on powerful data centers, which are virtually infinite in terms of capacity. Scalability and economies of scale make such solutions highly cost-effective. High availability is another benefit that you can depend on for mission-critical applications such as data analytics.
Cloud Computing offers services like AI, machine learning, data analytics, image processing, and authentication, which would otherwise require companies to incur substantial costs to develop them on their own. These services are available as SaaS solutions and empower applications that use Micro-services architecture.
The preferred architecture for Cloud Computing is Microservices on Kubernetes. Converting legacy applications to Microservices or components that can be migrated to the Cloud can be both costly and time-consuming.
Many organizations choose to completely replace their applications when migrating to the Cloud due to this reason. However, there are many ways of moving applications to the Cloud. The most common of these is the “Lift and Shift” method, where applications are simply moved to cloud-based infrastructure without any change. While this method can be cost-effective, it restricts organizations from fully leveraging the benefits of Cloud Computing on Amazon AWS.
Microservices allow organizations to decentralize their applications. So components are platform and language independent, meaning that they can be gradually refactored and migrated to the Cloud one component at a time. This strategy is the most recommended approach as it allows organizations to integrate business changes to their applications during the refactoring process.
Once the strategy has been decided, the next step is to select the deployment model. This refers to the type of cloud solution which could range from a single cloud provider to a multi-cloud solution. The Microservice architecture allows you to deploy each component in the Cloud service that is most suited for it.
You will also need to determine whether you are comfortable with the public Cloud or whether you prefer a private or hybrid cloud, which offers additional security and features on Amazon AWS.
Lastly, you will need to determine the service model on Amazon AWS. The available options are SaaS, PasS, and Infrastructure-as-a-Service (IaaS). These decisions can make or break your Cloud Computing success. So don’t take a chance, talk to the experts at Cloud Computing Technologies.
Data Analytics is the study of raw data in view of identifying trends and converting it into meaningful information that can facilitate decision making. It is highly dependent on processes and algorithms. This has lead to a convergence between data analysis and cloud computing.
The popularity of Kubernetes has made the term almost synonymous with Containers. Kubernetes are an open-source system for deploying, scaling and managing containerized applications. The open-sourced nature of Kubernetes makes it compatible with many different types of infrastructure, such as on-premise, hybrid, or public clouds.
Data Science is the superset of Data Analysis. In contrast to Analytics, Data Science focuses on analyzing large data sets for insights through techniques such as predictive analysis and machine learning. It is the broader study of data to arrive at insights regarding the unknown facets of data sets.
Our clients and CCT celebrate two decades of superior client outcomes from multi-year contract engagements. We are honored, inspired, and grateful for these long-term relationships.
Planning and implementing the latest strategies in ALM for best outcomes and high value delivery
Implementing Infrastructure as Code (IaC) Terraform automation as work output multiplier to increase speed, volume, and reduce errors in CI/CD pipelines
Developing secure coding solutions leveraging leading cloud platforms for compliance
Designing and implementing compelling digital transformation experience through superior design, proven strategy, and microservices technology stack to exceed expectations
Creating innovative, rapid iteration, simple to deploy, optimized time-to-market, microservices and API solutions
Implementing GRC best practices and security frameworks to consistenly identify and address risk
It has been known for some time that AWS CDK offers an extremely rich class of constructs in its library, and one of the best examples of a construct are AWS resources. In addition, it is extremely rare that you find a resource existing without context and with no relation to other resources. So, when you’re looking to connect the resources between the proper graphs for infrastructure, we would have
Amazon AWS Lambda Implementation Service is a cloud-based service that allows you to run code without provisioning or managing servers. You can use Lambda to build applications that respond to events and invoke functions that process data. With Lambda, you can implement solutions quickly and easily without having to worry about provisioning or managing server infrastructure. Lambda is a compute service that runs your code in response to events and
Cloud Infrastructure includes all the software and hardware elements required for cloud computing and it consists of the following: There are various types of cloud infrastructure that come with a user interface (UI) to manage all these virtual resources. IaaS or Infrastructure as a Service is this model’s accessible and prominent example. When you use IaaS, an enterprise or a team can acquire the infrastructure for computing necessary for use
Each organization is unique. This is so true that it would be an understatement to say that not all organizations are created equally. Diversity can be seen even within the same industry, even with businesses that compete with each other for the same market segment.
The software that drives your business is one of your most important assets. It should be designed in a way that allows it to rapidly adapt to changes in the business environment. The speed at which you are able to adapt to these changes depends not only on the business logic integrated into your software, but also the technology that is used, and even the methodology followed.
Gone are the days when monolithic applications ruled the world of software development. Decoupled, self-healing, cloud-enabled apps are the way forward. This new type of environment requires apps to be dynamic and component-based, but work together as a single unit. They are expected to share the same memory space, making all software built with this approach essentially self-contained.
If you’ve ever wondered how platforms like Google and Amazon show you targeted product advertisements, how Netflix and Spotify recommend content, how virtual assistants like Alexa and Siri work, or how cars drive themselves, the answer lies in some form of Artificial Intelligence (AI).
According to one recent cloud security study, the average global cost of a data breach hit $3.86 million in 2019 – up a massive 6.4% from the previous year. A lot of this has to do with how disparate and complicated computing environments have become in recent memory.
Data analysis refers to the process of analyzing data and producing insights for decision making. It has become vital for modern businesses due to the sheer amount of user and transaction data they encounter on a daily basis. Storing and processing this data becomes impossible without the right tools.
Cloud Computing Technologies contributors are passionate in creating the next generation of innovative cloud-native microservices with Amazon AWS landing zones and Kubernetes for agility, efficiency, cost avoidance, and high security regulatory compliance. We truly believe in the transformative power of excellent architecture, data analytics, and agile collaboration to bring applications elevated beyond expectations. Strong client relationships and creative collaborations result in superior application design for data analytics. We’re excited to start a dialog, learn about your Telework objectives, and develop the Kubernetes platform and microservices applications for your strategic fruition.
