Amazon Elastic Container Service (Amazon ECS) Integrations

An all-encompassing data virtualization solution for enterprises allows for effortless access to diverse data sources while creating a solid base of datasets and IT-managed data services that cater to nearly any application. The TIBCO® Data Virtualization platform acts as a modern data layer, adeptly addressing the fluctuating needs of organizations that are constantly adapting their architectures. By removing obstacles, it promotes uniformity and encourages the reuse of information by offering on-demand access to all data through a cohesive logical layer that is secure, well-governed, and available to a broad spectrum of users. With immediate access to essential data, organizations can extract actionable insights and react promptly in real-time. Users can conveniently search for and select from a self-service directory of virtualized business data, leveraging their preferred analytics tools to achieve their objectives. This transformation allows them to focus more on analyzing data instead of the labor-intensive process of data extraction. Additionally, this efficient process not only boosts productivity but also empowers teams to make swift and well-informed decisions, ultimately enhancing overall operational effectiveness. The integration of such a system can lead to a more agile and responsive organization, ready to tackle the challenges of a fast-paced business environment.

Tanzu Observability, powered by Broadcom, is a comprehensive observability solution designed to help businesses monitor and optimize cloud-native applications and infrastructure. The platform provides real-time visibility into applications, services, and infrastructure by aggregating metrics, logs, and traces, which allows businesses to identify performance bottlenecks, troubleshoot issues, and ensure seamless operations. Utilizing advanced AI and machine learning, Tanzu Observability automatically detects anomalies, enables automated root cause analysis, and provides actionable insights for proactive system management. Its scalable architecture supports large-scale deployments, making it an ideal solution for businesses seeking to enhance application performance, improve uptime, and drive data-driven decision-making across their cloud-native environments.

AWS Fargate is a serverless compute engine specifically designed for containerized applications and is fully compatible with Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS). This service empowers developers to focus on building their applications rather than dealing with server management hassles. With Fargate, there is no need to provision or manage servers, as users can specify and pay for resources tailored to their application needs, while also benefiting from enhanced security due to its built-in application isolation features. Fargate automatically allocates the necessary compute resources, alleviating the stress of instance selection and cluster scaling management. Users are charged only for the resources consumed by their containers, which helps to avoid unnecessary costs linked to over-provisioning or maintaining excess servers. Each task or pod operates in its own dedicated kernel, providing isolated computing environments that ensure secure workload separation and bolster overall security, which is crucial for maintaining application integrity. By embracing Fargate, developers can not only streamline their development processes but also enhance operational efficiency and implement strong security protocols, ultimately resulting in a more effective and agile application lifecycle. Additionally, this flexibility allows teams to adapt quickly to changing requirements and scale their applications seamlessly.

Amazon Elastic Kubernetes Service (EKS) provides an all-encompassing solution for Kubernetes management, fully managed by AWS. Esteemed companies such as Intel, Snap, Intuit, GoDaddy, and Autodesk trust EKS for hosting their essential applications, taking advantage of its strong security features, reliability, and efficient scaling capabilities. EKS is recognized as the leading choice for running Kubernetes due to several compelling factors. A significant benefit is the capability to launch EKS clusters with AWS Fargate, which facilitates serverless computing specifically designed for containerized applications. This functionality removes the necessity of server provisioning and management, allows users to distribute and pay for resources based on each application's needs, and boosts security through built-in application isolation. Moreover, EKS integrates flawlessly with a range of Amazon services, such as CloudWatch, Auto Scaling Groups, IAM, and VPC, ensuring that users can monitor, scale, and balance loads with ease. This deep level of integration streamlines operations, empowering developers to concentrate more on application development instead of the complexities of infrastructure management. Ultimately, the combination of these features positions EKS as a highly effective solution for organizations seeking to optimize their Kubernetes deployments.

Effx provides a seamless solution for managing and traversing your microservices architecture effectively. Regardless of whether you operate a small number of microservices or a large-scale environment, effx will continuously monitor and support you, regardless of using a public cloud, an orchestration platform, or a local deployment. Navigating incidents within a network of microservices can frequently become intricate and challenging. With effx, you receive essential context that enables you to accurately identify possible outage causes as they happen. Your organization has invested heavily to stay informed about any production issues. Our platform boosts your readiness by assessing services based on vital characteristics that guarantee their functionality, ultimately equipping your team to act quickly and effectively. In addition, effx's user-friendly interface simplifies the management process, making it easier for teams to collaborate and maintain a high level of service reliability.

Amazon GameLift is a dedicated hosting service designed for managing game servers, facilitating the deployment, operation, and scaling of cloud servers tailored for multiplayer gaming. Whether you're in need of a fully managed experience or specific functionalities, GameLift leverages AWS's infrastructure to deliver optimal latency, reduce player wait times, and enhance cost-effectiveness. By utilizing AWS's extensive global network, Amazon GameLift efficiently oversees game servers and seamlessly integrates player matchmaking into game sessions. This allows for automatic scaling of various instances—ranging from one to thousands—while adjusting to fluctuating player demands without requiring manual oversight. Additionally, the platform includes vital support services for multiplayer gaming, such as matchmaking, session directories, player data management, and in-depth analytics. Complementary services like AWS Lambda offer serverless computing solutions that can easily scale, while features from Amazon GameLift FlexMatch further improve the matchmaking process. Overall, GameLift streamlines the complexities involved in running multiplayer games, serving as an essential asset for developers aiming to create engaging gaming experiences. By automating many functions, it allows developers to focus more on game design rather than server management.

With over a decade of expertise in the development of media technology, we have designed a variety of solutions tailored to enhance your media and content creation processes. You can retain control while leveraging your existing tools and workflows, thereby engaging your audience in the way you envision. Production companies, editorial facilities, visual effects studios, audio houses, and finishing houses can work alongside a range of vendors, securely exchanging files across teams and multiple locations. Envoi, which employs IBM Aspera on Cloud, Amazon S3, and Equinix, will automate file processing and delivery through streamlined workflows. This allows for effortless file uploads, metadata requests, and collaboration among team members, storage options, and distribution partners. By establishing efficient ingest and distribution workflows, you can guarantee that files are processed and transformed as they arrive, significantly boosting productivity and collaboration in your projects. This innovative strategy not only enhances operational efficiency but also encourages greater creativity and productivity in media production, ultimately leading to superior results. Furthermore, these advancements position your projects for success in an increasingly competitive landscape.

Effortlessly manage the storage, sharing, and deployment of your containerized software solutions in any desired location. You can upload container images to Amazon ECR without the need for tedious infrastructure management, and conveniently retrieve those images using your preferred management tools. Images can be securely shared and downloaded through Hypertext Transfer Protocol Secure (HTTPS), which ensures automatic encryption and access controls to maintain data security. By taking advantage of a robust and scalable architecture, you can access and distribute your images more efficiently, which significantly cuts down on download times while improving overall availability. Amazon ECR acts as a fully managed container registry, offering high-performance hosting that facilitates the reliable deployment of application images and artifacts across multiple platforms. To help your organization adhere to image compliance and security standards, you can utilize insights from common vulnerabilities and exposures (CVEs) as well as the Common Vulnerability Scoring System (CVSS). With just a single command, you can publish your containerized applications and smoothly integrate them into your self-managed environments, making your deployment process more streamlined and efficient. This increased efficiency empowers developers to direct their attention towards innovation, rather than getting bogged down by operational challenges, thereby fostering a more productive development environment.

Engineered with a focus on the needs of engineers, our Prometheus monitoring solution is remarkably easy to implement, set up, and begin relaying metrics. We handle the scaling aspects of your Prometheus infrastructure, allowing you to dedicate your attention solely to your projects without any worries. Our service ensures that your data is preserved long-term with triple redundancy, enabling you to gain insights without the hassle of managing databases. You will benefit from automatic updates and plugins, keeping your Prometheus and Grafana stack up-to-date without requiring extra effort from you. All the tools for effective management of your Prometheus metrics are readily available, and we emphasize your independence by avoiding vendor lock-in, providing you with complete data export options at any time. This model merges the strengths of an open-source solution with the dependability and security offered by a SaaS platform. We guarantee your data is well-secured with threefold redundancy and stored for a full year, allowing you to scale seamlessly as we manage all the complexities on your behalf. Furthermore, it gives you peace of mind knowing that Prometheus experts are available to provide assistance around the clock, ensuring that you can always count on specialized support when required. Thus, you're not only equipped with effective monitoring tools, but you also have the backing of professionals dedicated to your success.

Choose the tools that align perfectly with your requirements, and we will take care of the rest. Design a customized CI/CD stack that meets your organization's goals without concerns about vendor lock-in. By removing the necessity for manual scripts and intricate toolchain automation, your engineers can focus on core business functions. Our pipeline workflows embrace a declarative methodology, which allows you to emphasize critical tasks rather than the techniques needed to complete them, addressing components such as software builds, security evaluations, unit tests, and deployment procedures. With the integration of Blueprints, you can easily diagnose issues directly within Opsera, aided by comprehensive console output for each stage of your pipeline's performance. Obtain a complete perspective on your CI/CD process with in-depth software delivery analytics that monitor metrics like Lead Time, Change Failure Rate, Deployment Frequency, and Time to Restore. Moreover, enjoy the advantages of contextualized logs that enable faster problem-solving while improving auditing and compliance practices, ensuring your operations stay effective and transparent. This efficient strategy not only fosters enhanced productivity but also encourages teams to explore innovative solutions without constraints, ultimately driving greater success for your organization.

A robust continuous delivery platform crafted for diverse application types across various cloud ecosystems, empowering engineers to deploy with enhanced speed and reliability. This GitOps solution streamlines deployment tasks via pull requests on Git, while its deployment pipeline interface provides a clear visualization of active processes. Each deployment is accompanied by a specialized log viewer, ensuring transparency regarding individual deployment activities. Users benefit from real-time application status updates and receive deployment notifications through Slack and webhook integrations. Performance insights related to delivery are easily accessible, further enriched by automated deployment evaluations that leverage metrics, logs, and outgoing requests. In case of any failure during an analysis or within a pipeline stage, the system automatically reverts to the most recent stable configuration. Furthermore, it quickly detects configuration drift, alerting users to any changes that have occurred. A new deployment is triggered automatically when specific events take place, such as a fresh container image being uploaded or a Helm chart being released. The platform also features single sign-on and role-based access control, ensuring that credentials are safeguarded and remain secure within the cluster without exposure in the control plane. By offering such comprehensive capabilities, this solution not only facilitates the deployment process but also significantly boosts overall operational efficiency and productivity.

QueryPie functions as an all-encompassing platform that integrates a variety of data sources and security measures into one seamless interface. Propel your organization’s progress towards success without needing to modify your existing data framework. In a landscape increasingly influenced by data, robust data governance has risen to paramount importance. Ensure compliance with data governance standards while simultaneously providing numerous users with access to a growing array of critical information. Develop data access protocols that take into account essential elements such as IP addresses and access times. You are able to establish different levels of access based on SQL commands divided into categories such as DML, DCL, and DDL, which facilitates secure data analysis and alterations. Effortlessly keep track of SQL event specifics and scrutinize user activities along with possible security risks by reviewing logs filtered through permissions. Additionally, all past data can be exported for detailed reporting, guaranteeing a comprehensive understanding of your data utilization and security measures. This integrated strategy not only improves oversight but also fosters informed decision-making for upcoming initiatives, thereby paving the way for a more secure and efficient data environment. Ultimately, QueryPie empowers organizations to harness their data responsibly while driving innovation and growth.

The system facilitates high-performance training on Amazon Elastic Compute Cloud (Amazon EC2) Trn1 instances, which utilize AWS Trainium technology. For model deployment, it provides efficient and low-latency inference on Amazon EC2 Inf1 instances that leverage AWS Inferentia, as well as Inf2 instances which are based on AWS Inferentia2. Through the Neuron software development kit, users can effectively use well-known machine learning frameworks such as TensorFlow and PyTorch, which allows them to optimally train and deploy their machine learning models on EC2 instances without the need for extensive code alterations or reliance on specific vendor solutions. The AWS Neuron SDK, tailored for both Inferentia and Trainium accelerators, integrates seamlessly with PyTorch and TensorFlow, enabling users to preserve their existing workflows with minimal changes. Moreover, for collaborative model training, the Neuron SDK is compatible with libraries like Megatron-LM and PyTorch Fully Sharded Data Parallel (FSDP), which boosts its adaptability and efficiency across various machine learning projects. This extensive support framework simplifies the management of machine learning tasks for developers, allowing for a more streamlined and productive development process overall.

DROPS is an advanced release management tool designed to improve, secure, and unify the application deployment process across various infrastructures such as data centers, hybrid models, and multi-cloud systems. It supports multiple platforms, seamlessly integrates with various CI/CD pipelines, and offers both agent-based and agentless operation modes. With features that include thorough release management, automated infrastructure provisioning, and 24/7 availability, DROPS aims to streamline deployment processes while maintaining consistent and reliable delivery. This flexible solution is capable of managing both legacy and modern applications, catering to the diverse needs of businesses. Users can opt for agent-based or agentless methods without the burden of agent installation or maintenance. If agents are needed, DROPS will automatically handle their provisioning to fit your specific setup. The intuitive web console allows for planning and managing application deployments without requiring extensive scripting knowledge, enhancing collaboration among stakeholders and technical teams, which ultimately boosts overall project efficiency. Consequently, DROPS not only simplifies the deployment process but also significantly improves communication and coordination throughout the software development lifecycle, leading to more successful project outcomes.

Amazon's EC2 P5 instances, equipped with NVIDIA H100 Tensor Core GPUs, alongside the P5e and P5en variants utilizing NVIDIA H200 Tensor Core GPUs, deliver exceptional capabilities for deep learning and high-performance computing endeavors. These instances can boost your solution development speed by up to four times compared to earlier GPU-based EC2 offerings, while also reducing the costs linked to machine learning model training by as much as 40%. This remarkable efficiency accelerates solution iterations, leading to a quicker time-to-market. Specifically designed for training and deploying cutting-edge large language models and diffusion models, the P5 series is indispensable for tackling the most complex generative AI challenges. Such applications span a diverse array of functionalities, including question-answering, code generation, image and video synthesis, and speech recognition. In addition, these instances are adept at scaling to accommodate demanding high-performance computing tasks, such as those found in pharmaceutical research and discovery, thereby broadening their applicability across numerous industries. Ultimately, Amazon EC2's P5 series not only amplifies computational capabilities but also fosters innovation across a variety of sectors, enabling businesses to stay ahead of the curve in technological advancements. The integration of these advanced instances can transform how organizations approach their most critical computational challenges.

Amazon EC2 Capacity Blocks are designed for machine learning, allowing users to secure accelerated compute instances within Amazon EC2 UltraClusters that are specifically optimized for their ML tasks. This service encompasses a variety of instance types, including P5en, P5e, P5, and P4d, which leverage NVIDIA's H200, H100, and A100 Tensor Core GPUs, along with Trn2 and Trn1 instances that utilize AWS Trainium. Users can reserve these instances for periods of up to six months, with flexible cluster sizes ranging from a single instance to as many as 64 instances, accommodating a maximum of 512 GPUs or 1,024 Trainium chips to meet a wide array of machine learning needs. Reservations can be conveniently made as much as eight weeks in advance. By employing Amazon EC2 UltraClusters, Capacity Blocks deliver a low-latency and high-throughput network, significantly improving the efficiency of distributed training processes. This setup ensures dependable access to superior computing resources, empowering you to plan your machine learning projects strategically, run experiments, develop prototypes, and manage anticipated surges in demand for machine learning applications. Ultimately, this service is crafted to enhance the machine learning workflow while promoting both scalability and performance, thereby allowing users to focus more on innovation and less on infrastructure. It stands as a pivotal tool for organizations looking to advance their machine learning initiatives effectively.

Amazon EC2 UltraClusters provide the ability to scale up to thousands of GPUs or specialized machine learning accelerators such as AWS Trainium, offering immediate access to performance comparable to supercomputing. They democratize advanced computing for developers working in machine learning, generative AI, and high-performance computing through a straightforward pay-as-you-go model, which removes the burden of setup and maintenance costs. These UltraClusters consist of numerous accelerated EC2 instances that are optimally organized within a particular AWS Availability Zone and interconnected through Elastic Fabric Adapter (EFA) networking over a petabit-scale nonblocking network. This cutting-edge arrangement ensures enhanced networking performance and includes access to Amazon FSx for Lustre, a fully managed shared storage system that is based on a high-performance parallel file system, enabling the efficient processing of large datasets with latencies in the sub-millisecond range. Additionally, EC2 UltraClusters support greater scalability for distributed machine learning training and seamlessly integrated high-performance computing tasks, thereby significantly reducing the time required for training. This infrastructure not only meets but exceeds the requirements for the most demanding computational applications, making it an essential tool for modern developers. With such capabilities, organizations can tackle complex challenges with confidence and efficiency.

Amazon EC2 Trn2 instances, equipped with AWS Trainium2 chips, are purpose-built for the effective training of generative AI models, including large language and diffusion models, and offer remarkable performance. These instances can provide cost reductions of as much as 50% when compared to other Amazon EC2 options. Supporting up to 16 Trainium2 accelerators, Trn2 instances deliver impressive computational power of up to 3 petaflops utilizing FP16/BF16 precision and come with 512 GB of high-bandwidth memory. They also include NeuronLink, a high-speed, nonblocking interconnect that enhances data and model parallelism, along with a network bandwidth capability of up to 1600 Gbps through the second-generation Elastic Fabric Adapter (EFAv2). When deployed in EC2 UltraClusters, these instances can scale extensively, accommodating as many as 30,000 interconnected Trainium2 chips linked by a nonblocking petabit-scale network, resulting in an astonishing 6 exaflops of compute performance. Furthermore, the AWS Neuron SDK integrates effortlessly with popular machine learning frameworks like PyTorch and TensorFlow, facilitating a smooth development process. This powerful combination of advanced hardware and robust software support makes Trn2 instances an outstanding option for organizations aiming to enhance their artificial intelligence capabilities, ultimately driving innovation and efficiency in AI projects.

Amazon Elastic File System (Amazon EFS) automatically adjusts its size as files are added or removed, removing the necessity for manual management or provisioning. This service facilitates secure and organized sharing of code and files, significantly boosting DevOps efficiency and enabling teams to react more rapidly to customer input. With no management overhead to contend with, users can easily persist and share data from their AWS containers and serverless applications. Built for user-friendliness and scalability, Amazon EFS provides the performance and reliability crucial for machine learning and large-scale data analytics tasks. It simplifies persistent storage for modern content management system operations, enabling businesses to launch products and services more efficiently, reliably, and economically. Users can quickly set up and configure shared file systems for AWS compute services, with no concerns regarding provisioning, deployment, patching, or maintenance. This system is also capable of handling workloads that require on-demand scaling, offering petabytes of storage and gigabytes per second of throughput right from the start, making it a fundamental component of contemporary technology infrastructures. Furthermore, with its robust architecture, Amazon EFS allows organizations to effortlessly adapt to changing demands while ensuring performance and security remain at the forefront. It truly represents an invaluable tool for businesses aiming to streamline their operations in a dynamic environment.

Presenting the ultimate solution for multicloud monitoring that delivers real-time analytics across a variety of environments, formerly recognized as SignalFx. This advanced platform supports monitoring in any setting thanks to its highly scalable streaming architecture. It boasts flexible and open data collection methods, allowing for rapid service visualizations in just seconds. Tailored for the fast-paced and transient nature of cloud-native environments, it is compatible with diverse scales including Kubernetes, containers, and serverless architectures. Users can quickly identify, visualize, and resolve issues as they arise, ensuring they maintain seamless operations. The system enhances real-time infrastructure performance monitoring at cloud scale through cutting-edge predictive streaming analytics. With over 200 pre-built integrations for various cloud services and readily available dashboards, it streamlines the visualization of your complete operational stack. Furthermore, the platform is equipped to autodiscover, categorize, group, and analyze different clouds, services, and systems with ease. This all-encompassing solution not only clarifies how your infrastructure interacts across multiple services, availability zones, and Kubernetes clusters but also significantly boosts operational efficiency and response times, making it an indispensable tool for modern IT environments. Ultimately, it empowers organizations to maintain optimal performance and adaptability in an ever-evolving cloud landscape.

StackState’s observability platform, which is centered around topology and relationships, enhances the management of your ever-evolving IT landscape. By consolidating performance metrics from various monitoring solutions, it establishes a cohesive topology. This innovative platform provides the following benefits: 1. An 80% reduction in Mean Time to Repair (MTTR) by pinpointing the underlying issues and notifying the relevant teams with precise information. 2. A 65% decrease in outages through real-time integrated monitoring and improved strategic planning. 3. A threefold increase in the speed of software releases, allowing developers more time to focus on implementation. Discover the advantages for yourself by signing up for a free guided demo today: https://www.stackstate.com/schedule-a-demo, and take the first step toward transforming your IT operations.

Deep Learning Containers are specialized Docker images that come pre-loaded and validated with the latest versions of popular deep learning frameworks. These containers enable the swift establishment of customized machine learning environments, thus removing the necessity to build and refine environments from scratch. By leveraging these pre-configured and rigorously tested Docker images, users can set up deep learning environments in a matter of minutes. In addition, they allow for the seamless development of tailored machine learning workflows for various tasks such as training, validation, and deployment, integrating effortlessly with platforms like Amazon SageMaker, Amazon EKS, and Amazon ECS. This simplification of the process significantly boosts both productivity and efficiency for data scientists and developers, ultimately fostering a more innovative atmosphere in the field of machine learning. As a result, teams can focus more on research and development instead of getting bogged down by environment setup.