Explore the SAS IoT analytics reference library of technical architecture examples, outlining industry best practices and use cases.
- Integration with In-memory Databases for State and Data Persistence
- Analytics For IoT Logical Architecture
- Azure IoT Reference Architecture
- SAS ESP Reference Architecture
- ESP Kubernetes Architecture Guide
- ESP HA system using Kafka and Viya 4
- SAS ESP High Availability using Kafka
- ESP and Omnio Integration
SAS ESP integration with In-memory Databases for State and Data Persistence
Use the SAS Event Stream Processing (ESP) StateDB windows StateDB Reader and StateDB Writer to integrate with Singelstore and Redis in-memory databases.
Key takeaways from the example:
• understand SAS Event Stream Processing in Kubernetes
• introduction to ESP StateDB Windows
• understand when to use external in-memory databases with ESP
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Overview
Have you ever been in a situation where the internal memory of SAS Event Stream Processing (ESP) was not sufficient to store all the data? Have you faced a time-consuming recovery of ESP from a failure due to the recreation of the internal state? Have you thought of having an external unlimited in-memory database to handle the limited internal memory issues? If yes is your answer to all these questions, then you are at the right place.
In this project, we present two new ESP StateDB windows StateDB Reader and StateDB Writer to integrate with Singelstore and Redis in-memory databases. We will present how we can solve the above-mentioned issues. You will also learn about the various benefits of this new architecture for persisting state and data using in-memory databases.
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| Category | Database Integration |
Analytics For IoT Logical Architecture
The project provides details on the Analytics for IoT reference architecture.
Key takeaways from the example:
• introduce the components of Analytics for IoT
• demo video covering scalable results from IoT data
• key features of SAS Analytics for IoT
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Overview
SAS Analytics for IoT is a complete AI-embedded IoT analytics solution that covers the entire IoT analytics life cycle. It was created for all types of users --business users, engineers, data scientist and IT professionals--who want to accelerate time to value (new insight, fast & accurate decision making, desirable outcomes) from their use cases.
The solution enables them to organize their diverse IoT data, create data selections, visually explore massive volumes of high frequency data, and launch data sets that can be leveraged in SAS, third party and open source applications. With this capability, organizations can extend the use of IoT analytics and collaboration across the enterprise, while optimizing their ecosystem of IoT investments –SAS, third party and open source.
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| Category | Analytics for IoT |
Azure IoT Reference Architecture
The project provides details on developing and refining reference architecture(s) for SAS integration with Azure IoT, as well as the full set of Azure cloud services.
Key takeaways from the example:
• introduce architects to the component Azure services
• understand common integration patterns
• authentication considerations
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Overview
This architecture attempts to cover well-known integration points between ESP and ESP Edge and the Azure services (as shown in the below architecture diagram). It does not necessarily cover every service that may be required to develop a solution, nor can it cover every use case.
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| Category | Cloud |
SAS ESP Reference Architecture
This project covers the reference architectures for SAS Event Stream Processing (ESP), including the best practices based on project requirements.
Key takeaways from the example:
• explore different reference architectures of ESP
• understand and evaluate which architectures is the best fit for your use-case
• use a flowchart to helps determinine the right ESP reference architecture that fulfils the business needs
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Overview
This architecture attempts to cover well-known integration points between ESP and ESP Edge and the Azure services (as shown in the below architecture diagram). It does not necessarily cover every service that may be required to develop a solution, nor can it cover every use case.
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| Category | Streaming Analytics |
SAS ESP Kubernetes Guide
The complete guide to SAS Event Stream Processing (ESP) with Kubernetes provides all the necessary information and guidelines with commands to set up your own Kubernetes cluster and its peripherals.
Key takeaways from the example:
• introduction to container and Kubernetes architecture
• Kubernetes cluster environment setup
• end-to-end ESP Kubernetes (ESP-Kube) architecture
• demo examples using ESP Operator
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Overview
SAS Event Stream Processing 6.2 with Kubernetes is a framework for controlled and automatic deployment, management, and scaling of SAS Event Stream Processing Servers running in Docker containers across the Kubernetes cluster in a cloud environment. It provides high guarantees on availability, reliability and fault tolerance. The framework leverages features of Kubernetes for resource allocation, storage orchestration, self-monitoring and healing, and secret and configuration management.
The framework comprises of SAS Event Stream Processing Operator which allows starting, stopping, updating, deleting, and monitoring of the ESP Servers docker containers running in the Kubernetes cluster.
SAS Web Clients (SAS Event Stream Processing Studio, SAS Event Stream Processing Streamviewer and SAS Event Stream Processing Manager) also runs in the docker containers in the same cluster and allows seamless access to all the ESP Servers.
This repository provides a thorough deep dive into the SAS Event Stream Processing 6.2 Kubernetes Architecture and guidelines for users to set up their own ESP Operator framework with web clients in a Kubernetes cluster.
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| Category | Cloud |
| Sub Category | Kubernetes |
Create and deploy an ESP HA system using Kafka and SAS Viya
Learn how to create and deploy a SAS Event Stream Processing (ESP) high availability (HA) system using Kafka and SAS Viya.
Key takeaways from the example:
• define high availability
• learn the basics of the Kafka message bus
• learn how to deploy an ESP project in failover mode
• how an end to end example
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Overview
There are three message buses supported for ESP failover: Solace, RabbitMQ and Kafka. This repository will show you how to use SAS Event Stream Processing (ESP) in conjunction with a Kafka message bus to build a real time fault-tolerant application which provides analytic services.
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| Category | High Availability |
SAS ESP High Availability using Kafka
Learn how to create and deploy a SAS Event Stream Processing (ESP) high availability system using Kafka as the message broker.
Key takeaways from the example:
• examine high availability concepts and the basics of the Kafka message bus
• deploy ESP in failover models
• walk through an end to end example
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Overview
There are four message buses supported for ESP failover: Tervela, Solace, RabbitMQ and Kafka. This repository will show you how to use SAS Event Stream Processing (ESP) in conjunction with a Kafka message bus to build a real time fault-tolerant application which provides analytic services.
ESP and Kafka work together to create a great high availability system with redundancy built into each part of the system. When combining these two technologies, real-time data pipelines and advanced analytics come together into a fault-tolerant, fast and scalable system.
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| Category | High availability |