Introduction to Amazon Timestream and its use cases

In this recipe, we will learn about Amazon Timestream. We will also learn about the use cases of Amazon Timestream.

Recipe Objective - Introduction to Amazon Timestream and its use cases?

The Amazon Timestream is widely used and is defined as a fast, scalable, and serverless time-series database solution for IoT and operational applications that allows users to store and analyse trillions of events each day 1,000 times quicker than relational databases and at a fraction of the cost. By retaining recent data in memory and shifting previous data to a cost-optimized storage tier based on user-defined criteria, Amazon Timestream saves users time and money when managing the lifetime of time series data. The purpose-built query engine in Amazon Timestream allows you to access and analyse both recent and historical data without having to indicate whether the data is in memory or in the cost-optimized tier directly in the query. Built-in time-series analytics tools in Amazon Timestream enable users to find trends and patterns in user's data in near real-time. Because Amazon Timestream is serverless and scales up and down dynamically to adapt capacity and performance, you don't have to worry about managing the underlying infrastructure, allowing users to focus on developing their apps. User's time series data is always secured with Amazon Timestream, whether at rest or in transit. For encrypting data in the magnetic storage, Amazon Timestream now lets you select an AWS KMS customer-managed key (CMK).

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Benefits of Amazon Timestream

  • The Amazon Timestream is meant to provide interactive and economical real-time analytics, with query speed up to 1,000 times quicker than relational databases and costs as little as a tenth of the price. Users can process, store, and analyse their time-series data for a fraction of the expense of traditional time-series solutions thanks to product features like scheduled queries, multi-measure records, and data storage tiers. Amazon Timestream can assist users in gaining quicker and more cost-effective insights from their data, allowing users to make better data-driven business choices and thus giving high performance at low cost. Amazon Timestream is serverless, which means users don't have to worry about managing servers or provisioning capacity, allowing users to focus on developing their apps and also users can handle billions of events and millions of queries every day using Amazon Timestream. It automatically scales to adapt capacity as their application's demands vary and thus provides serverless with auto-scaling. The complicated process of data lifecycle management is made easier with Amazon Timestream. Storage tiering is available, with a memory store for recent data and a magnetic store for historical data. Also, based on user-configurable rules, Amazon Timestream automates the transfer of data from the memory store to the magnetic storage and provides data lifecycle management.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Timestream and Use cases of Amazon Timestream.

Use cases of Amazon Timestream

    • It provides IoT applications

Using built-in analytic tools like smoothing, approximation, and interpolation, Amazon Timestream allows users to easily evaluate time-series data generated by IoT applications. A smart home device maker, for example, users can use Amazon Timestream to gather motion or temperature data from device sensors, interpolate to detect time spans without motion, and advise users to take actions like turning down the heat to conserve energy.

    • It supports DevOps applications

Amazon Timestream is perfect for DevOps systems that track health and usage indicators in real-time and analyse data to optimise performance and availability. To monitor health and optimise instance usage, users may use Amazon Timestream to gather and analyse operational metrics including CPU/memory utilisation, network data, and IOPS.

    • It supports Analytics applications

Amazon Timestream makes it simple to store and analyse large amounts of data. For example, users may use Amazon Timestream to store and handle incoming and outgoing web traffic for their apps using clickstream data. Amazon Timestream also features aggregate services for analysing data and gaining insights like the path-to-purchase and shopping cart abandonment rate.

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