AWS Timestream includes time series analytics functions that allow us to identify trends and patterns in our data in near real-time.

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Take a look at how our Support team explained AWS Timestream in detail.

AWS Timestream

Amazon Timestream is a fast, scalable, and serverless time series database service for IoT and operational applications that enables the storage and analysis of trillions of events per day up to 1,000 times faster and at a fraction of the cost of relational databases. Equally, by keeping recent data in memory and moving historical data to a cost optimised storage tier based on user defined policies, Amazon Timestream saves us time and money in managing the lifecycle of time series data.

Its purpose-built query engine allows us to access and analyse both recent and historical data without explicitly specifying in the query whether the data is in-memory or cost-optimized. Amazon Timestream is serverless and scales up and down automatically to adjust capacity and performance, so we don’t need to manage the underlying infrastructure, allowing us to focus on building our applications.

Benefits of AWS Timestream

1. High performance at a low cost.
2. Serverless computing with auto-scaling.
3. The complex process of data lifecycle management is easier with Amazon Timestream.
4. We no longer need to use multiple tools to access recent and historical data thanks to Amazon Timestream.
5. Advanced aggregates, window functions, and complex data types like arrays and rows are also supported by Amazon Timestream.
6. Finally, it ensures that we can always encrypt our time series data.

The Timestream rule action saves MQTT message attributes (measures) to an Amazon Timestream table. The attributes that this rule stores in the Timestream database are those that are returned by the query statement in the rule. It determines the data type of each attribute by parsing its value in the query statement’s result. (as in a DynamoDBv2 action). Then, it writes the value of each attribute to a separate record in the Timestream table. Further, use the cast() function in the query statement to specify or change the data type of an attribute.

Requirements

The following are the requirements for this rule action:

• AWS IoT can use the following IAM role to perform the timestream:DescribeEndpoints and timestream:WriteRecords. Then, we can choose, update, or create a role in the AWS IoT console to allow AWS IoT to perform this rule action.
• If we use a customer-managed AWS Key Management Service (AWS KMS) to encrypt data in transit in Timestream, the service must have permission to use the caller’s AWS KMS key.

Parameters

We must provide the following information when creating an AWS IoT rule with this action:

DatabaseName

The name of the Amazon Timestream database that contains the table that will receive the records generated by this action.

Dimensions

Each measure record contains metadata attributes for the time series.

name : The name of the metadata dimension. The column name in the database table record is this. Measure name, measure value, and time are all unnameable dimensions. These are reserved names. It does not allow the colon (:) character in dimension names that begin with ts_ or measure value.

value : The value to enter in this database record’s column.

RoleArn

The role’s Amazon Resource Name (ARN) that allows AWS IoT to write to the Timestream database table.

TableName

The name of the database table into which the measure records should be written.

Timestamp

The timestamp value to use for the entry. If blank, it uses the time when we process the entry.

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Conclusion

To sum up, our Support team thoroughly explained AWS Timestream.