WE BELIEVE, Google Cloud Platform differs from other cloud providers in a variety of ways through the best of the technology services/products it offers. Google Cloud is the forerunner whether it is technology innovations like BigQuery, Bigtable, AI Platform or adoption of open source products like Kubernetes Engine, Cloud Run, Dataflow, Dataproc to support businesses to adopt multi-cloud and hybrid architectures.
We are proud to be one of the very few who are endorsed by Google Cloud for our excellence in our works. Our technical expertise has been thoroughly validated and recognized.
We ensure quality by testing our knowledge by going through the industry-standard certifications. This helps us evolve as a better team to solve the problems of the current age.
We adopt standardized processes and frameworks to arrive at the optimum results for your business needs. This ensures transparency and validation.
Delivered projects globally and proven the value we can bring in to the table. It is part of our process to take-up a work only if we believe we can add value to it. We make sure it is always a Win-Win.
The recommended way to start when you have multiple inter-dependent services and components. Our processes help you assess and plan it well to get started well.
TransCloud’s application migration framework is one of the best and proven to be efficient in planning and taking it to finish line. It adds efficiency to our quality.
TransCloud ensures the data warehouse/platforms migration opens up newer possibilities, scalable and highly optimized for the scale of data that they are built for.
API Endpoints have specific needs when it comes to security, availability, scalability, management and analytics. TransCloud follows a 360* approach to get API Endpoints functional.
TransCloud handles the performance, scale and cost aspects in a highly optimized way for Content delivery systems. It ensures the best practices are followed for highly specific needs.
TransCloud ensures every single issue occurs at various stages during and after the migration is addressed effectively through root-cause analysis. We ensure the system is reliable.
The resources are provisioned purely based on the data we capture from the current environment and following Google Cloud’s best practices. You start to save money from day 1 after the migration.
Through regular account management meetings, we will be able to oversee your overall roadmap and become your trusted advisor. We are your extended team!
As your business grows, its support structure needs to grow, too. When you use our services you can scale your business up or down with nothing more than a simple discussion.
You’ll have your cloud needs outsourced to a team of cloud experts, and during times where you need additional support, we can extend your team’s strength to accomplish.
We love open-source tools and frameworks. We come with the expertise of using a wide array of proven tools and we know the art of leveraging the right ones for you.
Common patterns for modernizing applications include:
Sometimes called rehosting, the phrase “lift and shift” has become software development lingo for taking an existing application and moving it from a legacy environment (such as an on-premises server) to newer infrastructure, such as a public cloud platform. With this pattern, you are essentially moving the application “as is,” with little to no changes to its underlying code or architecture. This means it is usually the least intensive approach, but not always the optimal approach depending on the application in question.
Refactoring is essentially another way of saying “rewriting” or “restructuring.” This approach to application modernization entails taking a legacy application and retooling significant chunks of its underlying code to better run in a new environment, usually cloud infrastructure. In addition to major restructuring of the existing codebase, this approach often requires rewriting code. A development team may choose this approach if it wants to break up a monolithic application into smaller, decoupled pieces—an architectural choice commonly called microservices. They may use microservices to maximize the benefits of cloud-native infrastructure and tools, including containers and container orchestration.
This pattern can be viewed as a middle ground or compromise between the lift-and-shift and refactoring approaches. It does not require major changes in code or architecture, as with refactoring, but entails complementary updates that enable the legacy app to take advantage of a modern cloud platform, such as modifying or replacing the application’s backend database.
The foundational strategy for successful legacy app modernization is to conduct a thorough assessment of candidate applications. This analysis should include an evaluation of the app’s technical characteristics, its suitability for a cloud migration or similar shift, the ROI of such a modernization, the application’s interdependencies with other systems and other criteria.
Another key strategy for application modernization is for businesses to develop a long-term application modernization roadmap to effectively managing their resources. Most organizations are better served by approaching application modernization in a piece-by-piece manner rather than all at once. This can help teams to properly manage the performance and availability of their existing applications while also undertaking the work of modernization.
There are several intersecting technologies fundamental to application modernization.
When people discuss application modernization, they are typically referring to the process of migrating traditional applications to run in modern cloud environments. These include public cloud platforms, private clouds and hybrid clouds (which usually refer to public and/or private clouds integrated with on-premises environments.)
Containers are a cloud-centric method for packaging, deploying and operating applications and workloads. The big-picture benefits associated with containerization include greater scalability, portability and operational efficiency that is well-suited for cloud infrastructure, and especially multi-cloud and hybrid cloud environments.
This is not so much a technology as an architectural choice. Instead of building and operating an application as a single, complete codebase—usually called a monolith, or monolithic development—you decouple different components into smaller, discrete pieces that can be deployed, updated and operated independently.
Orchestration in software development refers to the automation of many of the operational tasks associated with containers, including deployment, scaling and networking. Automation in general is an important principle and technology, as it is increasingly necessary to ensure that development, operations and security teams can sustainably manage their modern apps at scale.
ML Ops is a set of practices that enables to deploy and maintain ML systems functional in production reliably. It combines Machine Learning, DevOps and Data Engineering together. It helps to standardize the processes involved across the lifecycle of the ML systems.
We are proud to be one of the very few who are endorsed by Google Cloud for our excellence in our works. Our technical expertise has been thoroughly validated and recognized.
We ensure quality by testing our knowledge by going through the industry-standard certifications. This helps us evolve as a better team to solve the problems of the current age.
We adopt standardized processes and frameworks to arrive at the optimum results for your business needs. This ensures transparency and validation.
Delivered projects globally and proven the value we can bring in to the table. It is part of our process to take-up a work only if we believe we can add value to it. We make sure it is always a Win-Win.
Continuous Training is something unique to ML/AI model development and deployment. It helps businesses to retrain the model automatically and get it ready for serving.
You need a robust automated CI/CD system for ML pipelines in production. The automated CI/CD system lets your data science team rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment.
The resources are provisioned purely based on the data we capture from the current environment and following Google Cloud’s best practices. You start to save money from day 1 after the migration.
Through regular account management meetings, we will be able to oversee your overall roadmap and become your trusted advisor. We are your extended team!
As your business grows, its support structure needs to grow, too. When you use our services you can scale your business up or down with nothing more than a simple discussion.
You’ll have your cloud needs outsourced to a team of cloud experts, and during times where you need additional support, we can extend your team’s strength to accomplish.
We love open-source tools and frameworks. We come with the expertise of using a wide array of proven tools and we know the art of leveraging the right ones for you.