AWS Step Functions Integrations

Around 25 million engineers work across dozens of distinct functions. Engineers are using New Relic as every company is becoming a software company to gather real-time insight and trending data on the performance of their software. This allows them to be more resilient and provide exceptional customer experiences. New Relic is the only platform that offers an all-in one solution. New Relic offers customers a secure cloud for all metrics and events, powerful full-stack analytics tools, and simple, transparent pricing based on usage. New Relic also has curated the largest open source ecosystem in the industry, making it simple for engineers to get started using observability.

Amazon Elastic Container Service (ECS) is a comprehensive container orchestration platform that is fully managed. Notable clients like Duolingo, Samsung, GE, and Cook Pad rely on ECS to operate their critical applications due to its robust security, dependability, and ability to scale. There are multiple advantages to utilizing ECS for container management. For one, users can deploy their ECS clusters using AWS Fargate, which provides serverless computing specifically designed for containerized applications. By leveraging Fargate, customers eliminate the need for server provisioning and management, allowing them to allocate costs based on their application's resource needs while enhancing security through inherent application isolation. Additionally, ECS plays a vital role in Amazon’s own infrastructure, powering essential services such as Amazon SageMaker, AWS Batch, Amazon Lex, and the recommendation system for Amazon.com, which demonstrates ECS’s extensive testing and reliability in terms of security and availability. This makes ECS not only a practical option but a proven choice for organizations looking to optimize their container operations efficiently.

If you're in need of computing power, database solutions, content distribution, or various other functionalities, AWS offers a wide array of services designed to assist you in developing advanced applications with enhanced flexibility, scalability, and reliability. Amazon Web Services (AWS) stands as the most extensive and widely utilized cloud platform globally, boasting over 175 fully functional services spread across data centers worldwide. A diverse range of customers, from rapidly expanding startups to major corporations and prominent government bodies, are leveraging AWS to reduce expenses, enhance agility, and accelerate innovation. AWS provides a larger selection of services, along with more features within those services, compared to any other cloud provider—covering everything from fundamental infrastructure technologies like computing, storage, and databases to cutting-edge innovations such as machine learning, artificial intelligence, data lakes, analytics, and the Internet of Things. This breadth of offerings facilitates a quicker, simpler, and more cost-effective transition of your current applications to the cloud, ensuring that you can stay ahead in a competitive landscape while taking advantage of the latest technological advancements.

Amazon Athena serves as an interactive query service that simplifies the process of analyzing data stored in Amazon S3 through the use of standard SQL. As a serverless service, it eliminates the need for infrastructure management, allowing users to pay solely for the queries they execute. The user-friendly interface enables you to simply point to your data in Amazon S3, establish the schema, and begin querying with standard SQL commands, with most results returning in mere seconds. Athena negates the requirement for intricate ETL processes to prepare data for analysis, making it accessible for anyone possessing SQL skills to swiftly examine large datasets. Additionally, Athena integrates seamlessly with AWS Glue Data Catalog, which facilitates the creation of a consolidated metadata repository across multiple services. This integration allows users to crawl data sources to identify schemas, update the Catalog with new and modified table and partition definitions, and manage schema versioning effectively. Not only does this streamline data management, but it also enhances the overall efficiency of data analysis within the AWS ecosystem.

AWS Cloud9 serves as a cloud-oriented integrated development environment (IDE) that allows you to create, execute, and troubleshoot your coding projects directly from your web browser. It features a built-in code editor, a debugger, and a terminal for ease of use. This IDE is equipped with necessary tools for various popular programming languages such as JavaScript, Python, and PHP, eliminating the need for manual installation or configuration on your local development setup to launch new projects. Being cloud-based means that you can access your Cloud9 environment from anywhere—be it your workplace, home, or any location with internet access. Additionally, Cloud9 streamlines the development of serverless applications, allowing for the effortless definition of resources, debugging, and toggling between local and remote application execution. Moreover, it facilitates collaboration by enabling you to share your development workspace with colleagues, which enhances pair programming and allows for real-time tracking of each other's contributions. Overall, Cloud9 significantly improves productivity and collaboration among development teams.

Amazon DynamoDB is a versatile key-value and document database that provides exceptional single-digit millisecond performance, regardless of scale. As a fully managed service, it offers multi-region, multimaster durability along with integrated security features, backup and restore capabilities, and in-memory caching designed for internet-scale applications. With the ability to handle over 10 trillion requests daily and support peak loads exceeding 20 million requests per second, it serves a wide range of businesses. Prominent companies like Lyft, Airbnb, and Redfin, alongside major enterprises such as Samsung, Toyota, and Capital One, rely on DynamoDB for their critical operations, leveraging its scalability and performance. This allows organizations to concentrate on fostering innovation without the burden of operational management. You can create an immersive gaming platform that manages player data, session histories, and leaderboards for millions of users simultaneously. Additionally, it facilitates the implementation of design patterns for various applications like shopping carts, workflow engines, inventory management, and customer profiles. DynamoDB is well-equipped to handle high-traffic, large-scale events seamlessly, making it an ideal choice for modern applications.

There are countless devices operating in various environments such as residences, industrial sites, oil extraction facilities, medical centers, vehicles, and numerous other locations. As the number of these devices continues to rise, there is a growing demand for effective solutions that can connect them, as well as gather, store, and analyze the data they generate. AWS provides a comprehensive suite of IoT services that span from edge computing to cloud-based solutions. Unique among cloud providers, AWS IoT integrates data management with advanced analytics capabilities tailored to handle the complexities of IoT data seamlessly. The platform includes robust security features at every level, offering preventive measures like encryption and access control to safeguard device data, along with ongoing monitoring and auditing of configurations. By merging AI with IoT, AWS enhances the intelligence of devices, allowing users to build models in the cloud and deploy them to devices where they operate twice as efficiently as comparable solutions. Additionally, you can streamline operations by easily creating digital twins that mirror real-world systems and conduct analytics on large volumes of IoT data without the need to construct a dedicated analytics infrastructure. This means businesses can focus more on leveraging insights rather than getting bogged down in technical complexities.

AWS Batch provides a streamlined platform for developers, scientists, and engineers to efficiently execute vast numbers of batch computing jobs on the AWS cloud infrastructure. It automatically allocates the ideal quantity and types of compute resources, such as CPU or memory-optimized instances, tailored to the demands and specifications of the submitted batch jobs. By utilizing AWS Batch, users are spared from the hassle of installing and managing batch computing software or server clusters, enabling them to concentrate on result analysis and problem-solving. The service organizes, schedules, and manages batch workloads across a comprehensive suite of AWS compute offerings, including AWS Fargate, Amazon EC2, and Spot Instances. Importantly, there are no extra fees associated with AWS Batch itself; users only incur costs for the AWS resources, such as EC2 instances or Fargate jobs, that they deploy for executing and storing their batch jobs. This makes AWS Batch not only efficient but also cost-effective for handling large-scale computing tasks. As a result, organizations can optimize their workflows and improve productivity without being burdened by complex infrastructure management.

Amazon SageMaker is a comprehensive machine learning platform that integrates powerful tools for model building, training, and deployment in one cohesive environment. It combines data processing, AI model development, and collaboration features, allowing teams to streamline the development of custom AI applications. With SageMaker, users can easily access data stored across Amazon S3 data lakes and Amazon Redshift data warehouses, facilitating faster insights and AI model development. It also supports generative AI use cases, enabling users to develop and scale applications with cutting-edge AI technologies. The platform’s governance and security features ensure that data and models are handled with precision and compliance throughout the entire ML lifecycle. Furthermore, SageMaker provides a unified development studio for real-time collaboration, speeding up data discovery and model deployment.

AWS App Mesh is a service mesh designed to enhance application-level networking, enabling seamless communication among your services across diverse computing environments. It provides excellent visibility and ensures high availability for your applications. Typically, modern applications comprise several services, each capable of being developed on various compute platforms, including Amazon EC2, Amazon ECS, Amazon EKS, and AWS Fargate. As the complexity increases with more services being added, identifying error sources and managing traffic rerouting after issues become challenging, along with safely implementing code modifications. In the past, developers had to embed monitoring and control mechanisms within their code, necessitating a redeployment of services with each update. This reliance on manual intervention can lead to longer downtimes and increased potential for human error, but App Mesh alleviates these concerns by streamlining the process.

Amazon Simple Queue Service (SQS) is a fully managed message queuing platform designed to help you decouple and scale microservices, distributed systems, and serverless applications. By removing the complexity and overhead typically associated with message-oriented middleware, SQS allows developers to concentrate on more impactful tasks. With SQS, you can effortlessly send, store, and receive messages between various software components at any scale, ensuring message integrity and independence from other services. You can quickly begin using SQS in just minutes through the AWS console, Command Line Interface, or your preferred SDK, executing three straightforward commands. This service enables the transmission of any data volume at any throughput level while maintaining message reliability and service independence. Additionally, SQS facilitates the decoupling of application components, which allows them to operate and fail independently, ultimately enhancing the fault tolerance of the overall system. By leveraging SQS, organizations can achieve greater resilience and adaptability in their application architecture.

Amazon Simple Notification Service (SNS) is a comprehensive messaging platform designed for both system-to-system and app-to-person (A2P) communications. It facilitates interaction between systems through a publish/subscribe (pub/sub) model, allowing messages to flow seamlessly between independent microservice applications or directly to users via SMS, mobile push notifications, and email. The pub/sub capabilities for system-to-system interactions support topics that enable high-throughput, push-based, many-to-many messaging. By leveraging Amazon SNS topics, your publishing systems can efficiently distribute messages to a wide array of subscriber systems or customer endpoints, including Amazon SQS queues, AWS Lambda functions, and HTTP/S, thus allowing for concurrent processing. Additionally, the A2P messaging feature empowers you to send messages to users on a large scale, utilizing either a pub/sub model or direct-publish messages through a unified API. This flexibility enhances communication strategies for businesses aiming to engage their users effectively.

AWS Glue is a fully managed data integration solution that simplifies the process of discovering, preparing, and merging data for purposes such as analytics, machine learning, and application development. By offering all the necessary tools for data integration, AWS Glue enables users to begin analyzing their data and leveraging it for insights within minutes rather than taking months. The concept of data integration encompasses various activities like identifying and extracting data from multiple sources, enhancing, cleaning, normalizing, and consolidating that data, as well as organizing and loading it into databases, data warehouses, and data lakes. Different users, each utilizing various tools, often manage these tasks. Operating within a serverless environment, AWS Glue eliminates the need for infrastructure management, automatically provisioning, configuring, and scaling the resources essential for executing data integration jobs. This efficiency allows organizations to focus more on data-driven decision-making without the overhead of manual resource management.

Easily design contemporary applications utilizing over 1,000 AWS resources with minimal uncertainty. Swiftly produce deployable infrastructure as code to optimize resource management. You can choose to write code or construct it visually, ensuring you have the ideal development experience for any given task. Simplify the assembly of modern applications by leveraging various AWS services. Select the appropriate tool for your project needs, whether it's visual building, coding, or generative AI-assisted code suggestions within your integrated development environment. Gain a clear understanding of your application's architecture and maintain its accuracy as you work collaboratively with your team. Seamlessly integrate with Workflow Studio to visually coordinate more than 220 AWS services or public HTTP endpoints through Step Functions workflows. You can start by diagramming a model of your application, create a new architecture from the ground up, import an existing infrastructure as code template, or utilize commonly used patterns. Furthermore, expand your visual composition capabilities to Visual Studio Code, allowing for the generation of AI-driven code suggestions for any AWS CloudFormation resource. This flexibility ensures that you can optimize your development process and adapt to evolving project requirements.

AWS Fargate serves as a serverless compute engine tailored for containerization, compatible with both Amazon Elastic Container Service (ECS) and Amazon Elastic Kubernetes Service (EKS). By utilizing Fargate, developers can concentrate on crafting their applications without the hassle of server management. This service eliminates the necessity to provision and oversee servers, allowing users to define and pay for resources specific to their applications while enhancing security through built-in application isolation. Fargate intelligently allocates the appropriate amount of compute resources, removing the burden of selecting instances and managing cluster scalability. Users are billed solely for the resources their containers utilize, thus avoiding costs associated with over-provisioning or extra servers. Each task or pod runs in its own kernel, ensuring that they have dedicated isolated computing environments. This architecture not only fosters workload separation but also reinforces overall security, greatly benefiting application integrity. By leveraging Fargate, developers can achieve operational efficiency alongside robust security measures, leading to a more streamlined development process.

Amazon Elastic Kubernetes Service (EKS) is a comprehensive Kubernetes management solution that operates entirely under AWS's management. High-profile clients like Intel, Snap, Intuit, GoDaddy, and Autodesk rely on EKS to host their most critical applications, benefiting from its robust security, dependability, and ability to scale efficiently. EKS stands out as the premier platform for running Kubernetes for multiple reasons. One key advantage is the option to deploy EKS clusters using AWS Fargate, which offers serverless computing tailored for containers. This feature eliminates the need to handle server provisioning and management, allows users to allocate and pay for resources on an application-by-application basis, and enhances security through inherent application isolation. Furthermore, EKS seamlessly integrates with various Amazon services, including CloudWatch, Auto Scaling Groups, IAM, and VPC, ensuring an effortless experience for monitoring, scaling, and load balancing applications. This level of integration simplifies operations, enabling developers to focus more on building their applications rather than managing infrastructure.

Execute your code without having to worry about server management, paying solely for the computational resources you actually use. AWS Lambda allows you to run your code without the need for provisioning or overseeing servers, charging you exclusively for the time your code is active. With Lambda, you can deploy code for nearly any kind of application or backend service while enjoying complete freedom from administrative tasks. Simply upload your code, and AWS Lambda handles everything necessary for running and scaling it with exceptional availability. You have the flexibility to set your code to automatically respond to triggers from other AWS services or invoke it directly from any web or mobile application. Furthermore, AWS Lambda efficiently runs your code without the need for you to manage server infrastructure. Just write your code and upload it, and AWS Lambda will take care of the rest. It also automatically scales your application by executing your code in response to each individual trigger, processing them in parallel and adapting precisely to the workload's demands. This level of automation and scalability makes AWS Lambda a powerful tool for developers seeking to optimize their application's performance.

Amazon EMR stands as the leading cloud-based big data solution for handling extensive datasets through popular open-source frameworks like Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. This platform enables you to conduct Petabyte-scale analyses at a cost that is less than half of traditional on-premises systems and delivers performance more than three times faster than typical Apache Spark operations. For short-duration tasks, you have the flexibility to quickly launch and terminate clusters, incurring charges only for the seconds the instances are active. In contrast, for extended workloads, you can establish highly available clusters that automatically adapt to fluctuating demand. Additionally, if you already utilize open-source technologies like Apache Spark and Apache Hive on-premises, you can seamlessly operate EMR clusters on AWS Outposts. Furthermore, you can leverage open-source machine learning libraries such as Apache Spark MLlib, TensorFlow, and Apache MXNet for data analysis. Integrating with Amazon SageMaker Studio allows for efficient large-scale model training, comprehensive analysis, and detailed reporting, enhancing your data processing capabilities even further. This robust infrastructure is ideal for organizations seeking to maximize efficiency while minimizing costs in their data operations.

Amazon EventBridge serves as a serverless event bus that simplifies the integration of applications by utilizing data from your own systems, various Software-as-a-Service (SaaS) offerings, and AWS services. It provides a continuous flow of real-time data from event sources like Zendesk, Datadog, and PagerDuty, efficiently directing that information to targets such as AWS Lambda. By establishing routing rules, you can dictate the destination of your data, enabling the creation of application architectures that respond instantaneously to all incoming data sources. EventBridge facilitates the development of event-driven applications by managing essential aspects like event ingestion, delivery, security, authorization, and error handling on your behalf. As your applications grow increasingly interconnected through events, you may find that greater effort is required to discover and comprehend the structure of these events in order to effectively code responses to them. This can enhance the overall efficiency and responsiveness of your application ecosystem.

AWS Identity and Access Management (IAM) provides a secure way to oversee access to AWS services and resources. With IAM, you have the ability to create and manage users and groups within AWS, while setting permissions to either grant or restrict their access to various resources. This valuable service comes at no extra cost beyond what you may incur from the usage of other AWS services by your users. IAM allows users to manage access to AWS service APIs and specific resources, ensuring that control is maintained. Moreover, IAM lets you implement specific conditions to further refine user access, such as time of day restrictions, the user's IP address, the use of SSL, or the requirement for multi-factor authentication (MFA). To enhance the security of your AWS environment, you can utilize AWS MFA, which is an added security layer that works alongside standard username and password credentials. MFA necessitates that users demonstrate physical possession of either a hardware MFA token or a mobile device equipped for MFA by entering a valid code. By implementing these measures, you can significantly increase the security posture of your AWS resources, safeguarding them against unauthorized access.