Aws greengrass example

1. What is AWS Greengrass used for?
2. How does greengrass work?
3. How do you deploy lambda function to greengrass?
4. How many cores can a greengrass group have?
5. What is Greengrass deployment?
6. Do I have to use AWS IoT Greengrass OTA updates?
7. What is Amazon IoT device?
8. What is the AWS IoT rule engine?
9. How do I trigger Lambda function automatically?
10. How do you automate a Lambda function?
11. What is AWS SageMaker used for?
12. Where is Amazon SageMaker used?
13. Why do we need AWS monitoring?
14. Which SIEM tool does AWS use?
15. What problem does SageMaker solve?
16. Why do I need SageMaker?
17. Is SageMaker SaaS or PaaS?
18. Can SageMaker handle big data?
19. Can SageMaker be used for ETL?

What is AWS Greengrass used for?

AWS IoT Greengrass is an Internet of Things (IoT) open source edge runtime and cloud service that helps you build, deploy, and manage device software. Customers use AWS IoT Greengrass for their IoT applications on millions of devices in homes, factories, vehicles, and businesses.

How does greengrass work?

Greengrass secures data with authentication and authorization at both the network- and device-level. IoT devices in a Greengrass deployment communicate with each other through local networks. A business can filter and transmit only the data it wants to the cloud, which reduces data migration and storage costs.

How do you deploy lambda function to greengrass?

To deploy a Lambda function to a core, you add the function to a Greengrass group (by referencing the existing Lambda function), configure group-specific settings for the function, and then deploy the group. If the function accesses AWS services, you also must add any required permissions to the Greengrass group role.

How many cores can a greengrass group have?

For more information, see Configure the AWS IoT Greengrass core. A Greengrass group must contain exactly one core. Client devices (also called connected devices, Greengrass devices, or devices) are devices that connect to a Greengrass core over MQTT.

What is Greengrass deployment?

Deployment is the process to send components and apply the desired component configuration to a destination target device (at the edge), which can be a single Greengrass core device or a group of Greengrass core devices.

Do I have to use AWS IoT Greengrass OTA updates?

You can use OTA updates to install the latest version of the AWS IoT Greengrass Core software or OTA update agent software on one or more cores. With OTA updates, your core devices don't have to be physically present. We recommend that you use OTA updates when possible.

What is Amazon IoT device?

AWS IoT Core is a managed cloud service that lets connected devices easily and securely interact with cloud applications and other devices. AWS IoT Core can support billions of devices and trillions of messages, and can process and route those messages to AWS endpoints and to other devices reliably and securely.

What is the AWS IoT rule engine?

The AWS IoT Rules Engine enables you to define how messages sent to AWS IoT Core can interact with AWS services. An AWS IoT rule consists of a SQL SELECT statement, a topic filter, and a rule action. The SQL SELECT statement can extract data from incoming MQTT messages.

How do I trigger Lambda function automatically?

To create a trigger, open the functions page of the Lambda console and choose the function you want to add a trigger to. In the Function overview pane, choose add trigger, select the AWS service you want to invoke your function, and follow the instructions to create a trigger.

How do you automate a Lambda function?

To automate tasks with specific timing and without any input, follow the instructions in Creating an EventBridge rule that triggers on a schedule. Make sure that you specify a schedule for when you want your automated task to run. Add the Lambda function that you created as a target to trigger in response to the event.

What is AWS SageMaker used for?

Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.

Where is Amazon SageMaker used?

Amazon SageMaker is a managed service in the Amazon Web Services (AWS) public cloud. It provides the tools to build, train and deploy machine learning (ML) models for predictive analytics applications. The platform automates the tedious work of building a production-ready artificial intelligence (AI) pipeline.

Why do we need AWS monitoring?

AWS environments require continuous monitoring, for example, to determine which changes to make to reduce costs, improve performance, and secure your systems.

Which SIEM tool does AWS use?

IBM Security QRadar SIEM provides centralized visibility and insights to quickly detect and prioritize threats across networks, users, and cloud.

What problem does SageMaker solve?

Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale.

Why do I need SageMaker?

Amazon SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps. SageMaker Studio gives you complete access, control, and visibility into each step required to prepare data and build, train, and deploy models.

Is SageMaker SaaS or PaaS?

SageMaker Studio is embedded inside the SaaS as the data science workbench—you can launch it by choosing a link inside the SaaS and get access to the various capabilities of SageMaker. You can use SageMaker Studio to process and analyze your own data stored in the SaaS and extract insights.

Can SageMaker handle big data?

Amazon SageMaker is designed for such scales and it is possible to use it to train on very large datasets. To take advantage of the scalability of the service you should consider a few modifications to your current practices, mainly around distributed training.

Can SageMaker be used for ETL?

AWS Glue development endpoint and SageMaker notebook

In AWS Glue, you can create a development endpoint and then create a SageMaker notebook to help develop your ETL and machine learning scripts.