Overview
What is AWS Timestream: AWS Timestream is a fully managed time-series database service from Amazon Web Services built around databases, tables, records, measures, and dimensions for storing and querying high-volume time-stamped events.
How to integrate AWS Timestream with Datagrid
Use this setup when recurring Timestream data needs to feed Datagrid reporting and cross-system workflows. Configure the integration by choosing Timestream data and connecting it through AWS IAM before setting a sync schedule.
Configure the integration
The AWS Timestream integration imports time-series databases and tables into Datagrid and makes measure and dimension fields from Timestream records available for workflows. Follow these steps to start the initial sync:
Click + Create on the top left of the screen.
Select Connect Apps.
Search for the AWS Timestream integration.
Log in with your AWS account and grant the requested permissions.
Click Next.
Select the AWS Timestream data to include (databases, tables, and the measure or dimension fields you want to work with).
Click Start First Import to begin the initial sync.
Before starting, confirm these requirements are in place:
An active AWS account with Timestream access
An IAM role with permissions to read from Timestream
AWS credentials configured in Datagrid
A clear selection of which databases and tables to import
Authenticate with AWS IAM
For the Amazon Timestream for LiveAnalytics databases and tables described on this page, Datagrid authenticates with AWS Timestream through IAM-based credentials. AWS recommends IAM roles with temporary credentials over long-term access keys. The IAM role must include permissions to read the target Timestream databases and tables.
Schedule data syncs
Use these sync settings to plan how data moves from Timestream into Datagrid:
Synced objects: Databases, tables, and record-level measure and dimension fields.
Sync direction: One-way (Timestream → Datagrid).
Frequency options: Daily, weekly, or monthly.
Custom endpoints: Additional endpoints can be requested through Datagrid support.
For best performance and cost, point the integration at scheduled query target tables rather than raw source tables. Scheduled queries pre-aggregate data into smaller tables optimized for regular extraction.
Why use AWS Timestream with Datagrid
This integration is built for teams that need answers and action from time-series data without stitching together exports by hand. Here are some reasons why you need to use AWS timestream with Datagrid:
Automated time-series extraction: Datagrid pulls Timestream table data and exposes measure and dimension fields on a fixed schedule, so teams skip manual exports and custom scripts.
Cross-platform data joins: AI agents combine Timestream metrics with records from relational databases or CRMs to produce consolidated analysis for operations and engineering reviews.
Anomaly detection workflows: Datagrid's AI agents flag irregular patterns in imported DevOps or IoT metrics and automatically route alerts to the right teams.
Scheduled reporting pipelines: Pre-aggregated Timestream data feeds Datagrid's automated reporting workflows on daily, weekly, or monthly cycles for predictable operational cadences.
No custom middleware required: The integration handles setup and scheduling through Datagrid's interface, with no Lambda functions or Kinesis streams to maintain.
What you can build with AWS Timestream and Datagrid
Datagrid turns imported Timestream records into repeatable operational outputs across systems that do not naturally stay in sync. These workflows show what AI agents deliver when Timestream data meets business context:
IoT fleet health dashboards: Import device telemetry such as temperature and vibration from Timestream tables into Datagrid.
Infrastructure performance reports: Schedule daily imports of CPU and memory metrics from Timestream.
Sensor data enrichment for field teams: Pull industrial sensor data from Timestream into Datagrid, match it against spec documents and work orders stored in document management systems, and generate contextualized data packets that field technicians can act on without querying multiple systems.
Operational KPI tracking across data sources: Extract pre-aggregated Timestream metrics such as throughput and error rates, then combine them with financial data from ERP systems.
Resources and documentation
Frequently asked questions
How often can Datagrid sync data from AWS Timestream?
Datagrid provides daily, weekly, or monthly sync schedules for AWS Timestream data, configured during setup. For cost-effective recurring extraction, point the integration at scheduled query target tables that contain pre-aggregated data.
What data objects does the integration import from Timestream?
The integration imports databases and tables from Timestream and exposes measure and dimension fields from Timestream records for downstream workflows. Measures contain the actual metric values such as CPU usage or temperature readings. Dimensions carry metadata attributes like device ID or region.
Does the integration work with both Timestream variants?
The integration details on this page describe Amazon Timestream for LiveAnalytics and its databases-and-tables model. If you use another Amazon Timestream variant, contact Datagrid support for specifics on compatibility and setup requirements.
Is data from AWS Timestream encrypted during transfer to Datagrid?
AWS documents that Timestream encrypts data at rest and in transit, with support for AWS KMS customer-managed keys for magnetic store encryption. Timestream holds certifications including SOC 1/2/3, ISO 27001, PCI DSS, and HIPAA eligibility.
Similar Integrations
Amazon AWS S3: Stores Timestream UNLOAD results and batch loads as cold-tier archives for long-term retention and ETL pipelines.
Amazon Redshift: Joins exported time-series data with business records in a downstream warehouse for large-scale analytics and reporting.
Databricks: Runs ETL, stream processing, and ML on Timestream exports for feature engineering and model training.
Snowflake: Ingests Timestream exports into a cloud data warehouse for cross-domain analytics and BI.
Amazon RDS: Joins relational business records with Timestream time-series for enriched reporting, correlation analysis, and operational workflows.
Amazon Aurora: Combines transactional data with Timestream metrics in hybrid analytical workflows on a managed high-performance relational endpoint.