Send data from AWS CloudWatch Metrics to SQL Server

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Send AWS CloudWatch Metrics to SQL Server

Sending AWS CloudWatch Metrics data to SQL Server requires connecting your AWS account to Kondado and authorizing access to your CloudWatch namespaces. Once authenticated, you select SQL Server as your destination database and choose which metric pipelines to replicate from your EC2 instances, Lambda functions, RDS databases, or custom applications. Kondado automatically extracts your time-series monitoring data and loads it into SQL Server on your chosen schedule, eliminating the need for manual CSV exports or complex Python scripts that require ongoing maintenance.

Kondado provides direct integration between AWS CloudWatch Metrics and SQL Server, replicating your infrastructure and application metrics on a configurable schedule ranging from every 5 minutes to daily. The platform maintains your historical time-series data in SQL Server tables, enabling complex analytical queries and correlation with business data without requiring any coding or manual API configuration.

Once your metrics arrive in SQL Server, you can build custom monitoring dashboards, create alerts based on historical trends, and join CloudWatch data with your sales or user analytics for comprehensive operational intelligence. This automated pipeline ensures your database always contains fresh monitoring data for immediate analysis.

Our prices start from $19 USD/month, and you can try Kondado for free for 14 days with no credit card required

Available AWS CloudWatch Metrics pipelines for SQL Server

Kondado offers two specialized pipelines for AWS CloudWatch Metrics replication to SQL Server. The Métricas (Incremental) pipeline captures new metrics collected since your last replication run, efficiently updating your SQL Server database without duplicating historical records. For scenarios requiring consistent time windows, the Métricas (Janela Móvel) pipeline replicates metrics within a configurable moving timeframe, ideal for rolling performance analysis and trend detection.

With your CloudWatch data in SQL Server, you can correlate EC2 CPU utilization with application response times, analyze Lambda execution patterns alongside error rates, or track RDS storage growth against customer onboarding metrics. This enables proactive capacity planning and cost optimization by joining infrastructure telemetry with business KPIs in your existing SQL Server analytics environment.

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Dynamic data

Kondado automatically reads the schema of your AWS CloudWatch Metrics. All tables, views, and fields available in your account are extracted without manual configuration.

2
available pipelines

What Kondado extracts

Métricas (Incremental)
Metrics collected incrementally from the last execution
Métricas (Janela Móvel)
Metrics with a configurable moving time window
Integration Description
Métricas (Incremental) Metrics collected incrementally from the last execution
Métricas (Janela Móvel) Metrics with a configurable moving time window
See all available data →

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How to send AWS CloudWatch Metrics data to SQL Server

Sync data automatically — no code, no manual exports.

1
Connect AWS CloudWatch Metrics

Navigate to the data sources section and add AWS CloudWatch Metrics by entering your AWS Access Key ID, Secret Access Key, and selecting your AWS region to establish the initial connection.

2
Configure SQL Server Destination

Enter your SQL Server connection details including server address, database name, and authentication credentials to designate where your CloudWatch metric data will be stored and updated.

3
Select Pipelines and Schedule

Choose between the Métricas (Incremental) or Métricas (Janela Móvel) pipelines, select which CloudWatch namespaces to replicate, and set your preferred update frequency from 5 minutes to daily.

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Send data from AWS CloudWatch Metrics to other destinations

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AWS CloudWatch Metrics > Amazon S3
BigQuery

AWS CloudWatch Metrics > BigQuery
Excel

AWS CloudWatch Metrics > Excel
Google Sheets

AWS CloudWatch Metrics > Google Sheets
MySQL

AWS CloudWatch Metrics > MySQL
PostgreSQL

AWS CloudWatch Metrics > PostgreSQL
Redshift

AWS CloudWatch Metrics > Redshift
SQL Server

AWS CloudWatch Metrics > SQL Server

Choose a tool to visualize your AWS CloudWatch Metrics data

If the software you need is not listed, drop us a messagem. You can use almost every tool

Airtable
AWS CloudWatch Metrics > Airtable
Alteryx
AWS CloudWatch Metrics > Alteryx
Amazon Quicksight
AWS CloudWatch Metrics > Amazon Quicksight
BIMachine
AWS CloudWatch Metrics > BIMachine
Cortex
AWS CloudWatch Metrics > Cortex
Data Studio
AWS CloudWatch Metrics > Data Studio
Geckoboard
AWS CloudWatch Metrics > Geckoboard
Grafana
AWS CloudWatch Metrics > Grafana
IBM Cognos Analytics
AWS CloudWatch Metrics > IBM Cognos Analytics
Klipfolio
AWS CloudWatch Metrics > Klipfolio
Looker Studio
AWS CloudWatch Metrics > Looker Studio
Looker
AWS CloudWatch Metrics > Looker
Looqbox
AWS CloudWatch Metrics > Looqbox
Metabase
AWS CloudWatch Metrics > Metabase
Microstrategy
AWS CloudWatch Metrics > Microstrategy
Pentaho
AWS CloudWatch Metrics > Pentaho
Power BI
AWS CloudWatch Metrics > Power BI
Qlik Cloud Analytics
AWS CloudWatch Metrics > Qlik Cloud Analytics
Qlik Sense
AWS CloudWatch Metrics > Qlik Sense
Qlik
AWS CloudWatch Metrics > Qlik
QlikView
AWS CloudWatch Metrics > QlikView
Redash
AWS CloudWatch Metrics > Redash
Superset
AWS CloudWatch Metrics > Superset
Tableau
AWS CloudWatch Metrics > Tableau
BI TOTVS
AWS CloudWatch Metrics > BI TOTVS
Zoho Analytics
AWS CloudWatch Metrics > Zoho Analytics

Frequently Asked Questions (FAQ)

Answers about sending AWS CloudWatch Metrics data to SQL Server automatically

How does Kondado connect to AWS CloudWatch Metrics for SQL Server replication? ▼
Kondado uses your AWS IAM credentials to establish a read-only connection to your CloudWatch namespaces and metric streams. You authorize the connection by providing your Access Key ID, Secret Access Key, and specifying your preferred AWS region during the data source setup. The platform then automatically discovers available metrics across your EC2, Lambda, RDS, and custom application namespaces.
What types of AWS CloudWatch Metrics can I replicate to SQL Server? ▼
You can replicate standard AWS service metrics from EC2, S3, Lambda, RDS, DynamoDB, and CloudFront, alongside custom metrics published from your applications via the CloudWatch API. This includes dimensional metrics with associated tags and instance identifiers, enabling granular filtering and grouping once stored in SQL Server. Both system-level infrastructure metrics and application-specific business metrics are available through the pipelines.
How often can I update my CloudWatch Metrics data in SQL Server? ▼
Kondado supports automated updates on a configurable schedule ranging from every 5 minutes to daily, depending on your monitoring requirements and SQL Server capacity. Near-real-time updates every 5 or 15 minutes work well for critical production monitoring, while hourly or daily schedules suit cost analysis and historical trending. You can modify your schedule anytime without disrupting existing data or requiring pipeline reconfiguration.
What is the difference between Incremental and Moving Window pipelines for CloudWatch Metrics? ▼
The Métricas (Incremental) pipeline appends only new metric data points collected since your last successful replication, minimizing data transfer volume and processing time for growing historical datasets. The Métricas (Janela Móvel) pipeline maintains a rolling window of recent data, replacing older records outside your configured timeframe, which is ideal for dashboards showing only the last 24 hours or 7 days of activity. Choose Incremental for long-term analysis and Moving Window for operational monitoring of recent performance.
How is CloudWatch Metrics data structured when it arrives in SQL Server? ▼
Each metric arrives as structured rows containing the metric name, timestamp, value, unit, dimensions, and namespace, organized into SQL Server tables that preserve the original time-series granularity. The schema includes separate columns for each dimension key-value pair, allowing efficient filtering and JOIN operations with your existing business tables. Timestamps are standardized to facilitate time-based aggregations and comparisons across different AWS services.
Can I combine AWS CloudWatch Metrics with other data sources in SQL Server? ▼
Yes, once your CloudWatch data resides in SQL Server, you can JOIN it with data replicated from other sources like Salesforce, Google Analytics, or your internal PostgreSQL databases. This enables correlation between infrastructure performance and business outcomes, such as analyzing how EC2 CPU spikes affect conversion rates or customer satisfaction scores. You can also send this unified data to Power BI or Looker Studio for comprehensive visualization.
Do I need to know SQL or AWS APIs to set up CloudWatch Metrics replication? ▼
No coding or SQL knowledge is required to configure the replication, as Kondado provides a visual interface for selecting your CloudWatch namespaces and setting your SQL Server connection parameters. The platform handles all API authentication, pagination, and data type conversions automatically. However, basic SQL knowledge helps you query the replicated data effectively once it arrives in your database.
What can I build with CloudWatch Metrics data once it is in SQL Server? ▼
You can create custom capacity planning reports that predict when RDS storage or EC2 instances will reach limits based on historical growth patterns. Build automated cost allocation dashboards that attribute AWS resource consumption to specific departments or projects using metric dimensions and tags. You can also construct SLA compliance reports by analyzing Lambda duration and API Gateway latency alongside your application logs for complete service level visibility.

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