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Timestream (Amazon AWS)

Time series database designed for collecting, storing, and processing time-series data in IoT and operational applications

Amazon Timestream, part of Amazon Web Services (AWS), is a purpose-built time series database designed for collecting, storing, and processing time-series data in IoT and operational applications. Amazon Timestream is serverless and is able to automatically scale up or down to adjust to required capacity and performance. 

Thanks to our flexible input APIs, Clarify can be easily connected to Amazon Timestream, providing a cost-efficient and simple way of getting value from the time series data collected by this service.

Clarify makes it easy to visualize time series data from your IoT network, access it on web and mobile devices, as well as sharing data to other people. With Clarify, your organization can focus on key business goals instead of configuring dashboards or developing expensive custom solutions.

About Amazon Web Services (AWS)

Amazon Web Services is a suite of cloud computing platforms and APIs by Amazon. AWS comprises more than 200 different products and services including storage, networking, database, analytics, computing, deployment, management, machine learning, mobile, developer tools, IoT tools, and solutions in other fields. 

AWS traces its origins to the early 2000s, when online retailer Amazon started to explore the idea of providing cloud-based web services to other companies and individuals. In 2002, Web Services launched its first set of web services. 

In 2006, AWS launched Amazon S3 (Amazon Simple Storage Service that provides object storage through a web service interface) and EC2 (Elastic Compute Cloud, service that allows users to rent virtual computers on which to run their own computer applications). Over the next several years, Amazon launched a number of new services as part of AWS, including SimpleDB, Mechanical Turk, Elastic Block Store, Elastic Beanstalk, Relational Database Service, DynamoDB, CloudWatch, Simple Workflow, CloudFront, and Availability Zones.

In 2010, it was reported that all of's retail sites had migrated to AWS. In 2012, AWS hosted its first major annual conference (named e:Invent), focused on AWS' partners and ecosystem. In 2014, AWS launched its AWS Partner Network, focused on helping AWS-based companies grow and scale their businesses with collaboration and best practices.

Most services that are part of AWS don’t get direct exposure to end-users. Instead, the functionality is offered through APIs, which developers can use in their applications. AWS services are accessible over HTTP, using the REST architectural style and SOAP protocol for older APIs, and exclusively JSON for newer ones.

It is estimated that AWS is the largest cloud services platform in the world, having a market share of 33%. Microsoft Azure and Google Cloud, two Amazon’s main competitors in this field, have 18% and 9% of the market respectively. 

About Amazon Timestream

Amazon Timestream is a purpose-built time series database solution designed for collecting, storing and processing various time-series data across IoT networks and system monitoring solutions. 

Fast time series data processing for real-time analytics is one distinctive feature of Amazon Timestream. The service was designed to provide 1,000 times faster query performance compared to relational databases, and includes features such as scheduled queries, multi-measure records, and data storage tiering.

Automating scaling is another benefit of Amazon Timestream deriving from the cloud nature of this service. The solution scales automatically in accordance with the needs of user applications. 

Amazon Timestream has SQL support with built-in time series functions for smoothing, approximation, and interpolation. It also supports advanced aggregates, window functions, and complex data types such as arrays and rows.The scheduled queries feature offers a serverless solution for calculating and storing aggregates, rollups, and other real-time analytics. 

Timestream queries are expressed in a SQL grammar with extensions for time series-specific data types and functions. Queries are then processed by an adaptive and distributed query engine that uses metadata from the tile tracking and indexing service to seamlessly access and combine data across data stores at the time the query is issued. Queries are run by a dedicated fleet of workers, the number of which is determined by query complexity and data size. Performance for complex queries over large data sets is achieved through massive parallelism, both on the query execution fleet and the storage fleets of the system. 

Amazon Timestream’s data storage 

Amazon Timestream is designed to automatically manage the lifecycle of time series data and offers a memory store for recent data and a cost-effective magnetic store for historical data. The service supports configuring table level policies to automatically transfer data across stores. Incoming writes land in the memory store where data is optimized for writes, with reads performed at current time for powering dashboards and alerting type queries. When the main time-frame for writes, alerting and dashboarding needs has passed, the data automatically flows from the memory store to the magnetic store for cost optimization.

Data moved to the magnetic store are reorganized into a format that is highly optimized for large volume data reads. The magnetic store also has a data retention policy that may be configured if there is a time threshold where the data outlive its usefulness. When the data exceed the time range defined for the magnetic store retention policy, it is automatically removed.

Amazon Timestream integrates with commonly used services for data collection, visualization, and machine learning. Data can be sent to Amazon Timestream using AWS IoT Core, Amazon Kinesis, Amazon MSK, and open source Telegraf metrics collector. The service also supports Amazon SageMaker with Amazon Timestream for machine learning.

Why integrate with Clarify?

  • Search - let people on your team easily find data across siloed systems - always available on their phone or in the browser
  • Visualize - See trends and combine data to visually explore
  • Explore - See statistics and seamlessly move from millisecond detail to years worth of data
  • Export - Combine data sources and easily export to Excel for further data work
  • Annotate/label - Add meaning and context to your data by labeling both points and periods in time, to build training sets for your AI/ML efforts
  • Contextualize - Ensure naming, labeling, and metadata is of high quality
  • Cloud Storage - one safe space for your raw time series data built for cost-efficient speed and performance at scale
  • Integrate - break down your data silos and build one source of truth
  • Mobile apps for iOS and Android - make data easily available for your team, whenever and wherever they are. Fuel a remote work culture with live data.
  • Collaboration - share visualizations and discuss data with comments, threads and the activity feed. Add files for even more context.



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