Mock sample for your project: Amazon Timestream Query API

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Amazon Timestream Query

amazonaws.com

Version: 2018-11-01


Use this API in your project

Integrate third-party APIs faster by using "Amazon Timestream Query API" ready-to-use mock sample. Mocking this API will help you accelerate your development lifecycles and improves your integration tests' quality and reliability by accounting for random failures, slow response time, etc.
It also helps reduce your dependency on third-party APIs: no more accounts to create, API keys to provision, accesses to configure, unplanned downtime, etc.

Description

Other APIs by amazonaws.com

Amazon Lightsail

Amazon Lightsail is the easiest way to get started with Amazon Web Services (AWS) for developers who need to build websites or web applications. It includes everything you need to launch your project quickly - instances (virtual private servers), container services, storage buckets, managed databases, SSD-based block storage, static IP addresses, load balancers, content delivery network (CDN) distributions, DNS management of registered domains, and resource snapshots (backups) - for a low, predictable monthly price. You can manage your Lightsail resources using the Lightsail console, Lightsail API, AWS Command Line Interface (AWS CLI), or SDKs. For more information about Lightsail concepts and tasks, see the Amazon Lightsail Developer Guide. This API Reference provides detailed information about the actions, data types, parameters, and errors of the Lightsail service. For more information about the supported AWS Regions, endpoints, and service quotas of the Lightsail service, see Amazon Lightsail Endpoints and Quotas in the AWS General Reference.

AWS Data Pipeline

AWS Data Pipeline configures and manages a data-driven workflow called a pipeline. AWS Data Pipeline handles the details of scheduling and ensuring that data dependencies are met so that your application can focus on processing the data. AWS Data Pipeline provides a JAR implementation of a task runner called AWS Data Pipeline Task Runner. AWS Data Pipeline Task Runner provides logic for common data management scenarios, such as performing database queries and running data analysis using Amazon Elastic MapReduce (Amazon EMR). You can use AWS Data Pipeline Task Runner as your task runner, or you can write your own task runner to provide custom data management. AWS Data Pipeline implements two main sets of functionality. Use the first set to create a pipeline and define data sources, schedules, dependencies, and the transforms to be performed on the data. Use the second set in your task runner application to receive the next task ready for processing. The logic for performing the task, such as querying the data, running data analysis, or converting the data from one format to another, is contained within the task runner. The task runner performs the task assigned to it by the web service, reporting progress to the web service as it does so. When the task is done, the task runner reports the final success or failure of the task to the web service.

Application Auto Scaling

With Application Auto Scaling, you can configure automatic scaling for the following resources: Amazon AppStream 2.0 fleets Amazon Aurora Replicas Amazon Comprehend document classification and entity recognizer endpoints Amazon DynamoDB tables and global secondary indexes throughput capacity Amazon ECS services Amazon ElastiCache for Redis clusters (replication groups) Amazon EMR clusters Amazon Keyspaces (for Apache Cassandra) tables Lambda function provisioned concurrency Amazon Managed Streaming for Apache Kafka broker storage Amazon SageMaker endpoint variants Spot Fleet (Amazon EC2) requests Custom resources provided by your own applications or services API Summary The Application Auto Scaling service API includes three key sets of actions: Register and manage scalable targets - Register Amazon Web Services or custom resources as scalable targets (a resource that Application Auto Scaling can scale), set minimum and maximum capacity limits, and retrieve information on existing scalable targets. Configure and manage automatic scaling - Define scaling policies to dynamically scale your resources in response to CloudWatch alarms, schedule one-time or recurring scaling actions, and retrieve your recent scaling activity history. Suspend and resume scaling - Temporarily suspend and later resume automatic scaling by calling the RegisterScalableTarget API action for any Application Auto Scaling scalable target. You can suspend and resume (individually or in combination) scale-out activities that are triggered by a scaling policy, scale-in activities that are triggered by a scaling policy, and scheduled scaling. To learn more about Application Auto Scaling, including information about granting IAM users required permissions for Application Auto Scaling actions, see the Application Auto Scaling User Guide.

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Alexa for Business helps you use Alexa in your organization. Alexa for Business provides you with the tools to manage Alexa devices, enroll your users, and assign skills, at scale. You can build your own context-aware voice skills using the Alexa Skills Kit and the Alexa for Business API operations. You can also make these available as private skills for your organization. Alexa for Business makes it efficient to voice-enable your products and services, thus providing context-aware voice experiences for your customers. Device makers building with the Alexa Voice Service (AVS) can create fully integrated solutions, register their products with Alexa for Business, and manage them as shared devices in their organization.

Amazon CloudDirectory

Amazon Cloud Directory Amazon Cloud Directory is a component of the AWS Directory Service that simplifies the development and management of cloud-scale web, mobile, and IoT applications. This guide describes the Cloud Directory operations that you can call programmatically and includes detailed information on data types and errors. For information about Cloud Directory features, see AWS Directory Service and the Amazon Cloud Directory Developer Guide.

Amazon Athena

Amazon Athena is an interactive query service that lets you use standard SQL to analyze data directly in Amazon S3. You can point Athena at your data in Amazon S3 and run ad-hoc queries and get results in seconds. Athena is serverless, so there is no infrastructure to set up or manage. You pay only for the queries you run. Athena scales automatically—executing queries in parallel—so results are fast, even with large datasets and complex queries. For more information, see What is Amazon Athena in the Amazon Athena User Guide. If you connect to Athena using the JDBC driver, use version 1.1.0 of the driver or later with the Amazon Athena API. Earlier version drivers do not support the API. For more information and to download the driver, see Accessing Amazon Athena with JDBC. For code samples using the Amazon Web Services SDK for Java, see Examples and Code Samples in the Amazon Athena User Guide.

AmplifyBackend

AWS Amplify Admin API

Amazon CloudWatch Application Insights

Amazon CloudWatch Application Insights Amazon CloudWatch Application Insights is a service that helps you detect common problems with your applications. It enables you to pinpoint the source of issues in your applications (built with technologies such as Microsoft IIS, .NET, and Microsoft SQL Server), by providing key insights into detected problems. After you onboard your application, CloudWatch Application Insights identifies, recommends, and sets up metrics and logs. It continuously analyzes and correlates your metrics and logs for unusual behavior to surface actionable problems with your application. For example, if your application is slow and unresponsive and leading to HTTP 500 errors in your Application Load Balancer (ALB), Application Insights informs you that a memory pressure problem with your SQL Server database is occurring. It bases this analysis on impactful metrics and log errors.

AWS IoT Things Graph

AWS IoT Things Graph AWS IoT Things Graph provides an integrated set of tools that enable developers to connect devices and services that use different standards, such as units of measure and communication protocols. AWS IoT Things Graph makes it possible to build IoT applications with little to no code by connecting devices and services and defining how they interact at an abstract level. For more information about how AWS IoT Things Graph works, see the User Guide.

AWS Migration Hub

The AWS Migration Hub API methods help to obtain server and application migration status and integrate your resource-specific migration tool by providing a programmatic interface to Migration Hub. Remember that you must set your AWS Migration Hub home region before you call any of these APIs, or a HomeRegionNotSetException error will be returned. Also, you must make the API calls while in your home region.

AWS CloudHSM V2

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Elastic Load Balancing

Elastic Load Balancing A load balancer distributes incoming traffic across targets, such as your EC2 instances. This enables you to increase the availability of your application. The load balancer also monitors the health of its registered targets and ensures that it routes traffic only to healthy targets. You configure your load balancer to accept incoming traffic by specifying one or more listeners, which are configured with a protocol and port number for connections from clients to the load balancer. You configure a target group with a protocol and port number for connections from the load balancer to the targets, and with health check settings to be used when checking the health status of the targets. Elastic Load Balancing supports the following types of load balancers: Application Load Balancers, Network Load Balancers, Gateway Load Balancers, and Classic Load Balancers. This reference covers the following load balancer types: Application Load Balancer - Operates at the application layer (layer 7) and supports HTTP and HTTPS. Network Load Balancer - Operates at the transport layer (layer 4) and supports TCP, TLS, and UDP. Gateway Load Balancer - Operates at the network layer (layer 3). For more information, see the Elastic Load Balancing User Guide. All Elastic Load Balancing operations are idempotent, which means that they complete at most one time. If you repeat an operation, it succeeds.

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