Amazon Web Services is a subordinate of Amazon which provides on-demand cloud computing platforms and APIs to individuals, companies, and governments with a mixture of infrastructure like service (IaaS), platform as a service (PaaS), and packed software as a service (SaaS).
Today’s customers want to create IoT, edge, and operational applications that collect, synthesize and derive insights from large amounts of data that change over time. This type of time-series data can be generated from multiple sources with extremely high volumes, cost-effective, collected in near real-time, and requires efficient storage that helps customers organize and analyze the data. For this customers can either use existing relational databases or self-managed time-series databases which is costly and time-consuming.
So Recently, AWS announced the general availability of Amazon Timestream, a new time-series database for IoT and operational applications that helps o process trillions of time series events per day up to 1,000 times faster than relational databases with low cost. Amazon Timestream helps to reduce customers’ effort in storing recent data in-memory and moving historical data to a cost-optimized storage tier based upon user-defined policies and while processing, it helps customers to combine and access recent and historical data transparently across tiers with a single query.
Amazon Timestream solves the challenges by proving customers with a purpose-built, serverless time-series database for collecting, storing, and processing time-series data. Amazon Timestream can automatically detect the attributes of the data, so customers no longer need to predefine its schema.
Amazon Timestream provides an inbuilt time-series analytics, for smoothing, approximation, and interpolation, so customers don’t have to extract raw data from their databases and then perform their time-series analytics with external tools and libraries or write complex procedures that not all databases support. In addition, Amazon Timestream combines with popular data collection, visualization, and machine learning tools, including services like AWS IoT, Amazon Kinesis and Amazon MSK, Amazon QuickSight, and Amazon SageMaker as well as open-source, third-party tools like Grafana and Telegraf.
Amazon Timestream is currently accessible in US East (N. Virginia), US East (Ohio), US West (Oregon), and EU (Ireland) and it is going to expand in other regions in the coming months.
Therefore, serverless Amazon Timestream analytics features provide time series-specific functions that help customers to identify trends and patterns in data in near real-time and it automatically scales up or down to adjust capacity based on load, without customers’ touch. There are no upfront costs or commitments required to use Amazon Timestream, customers should focus only on the data they write, store, query processing.