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Sync data from Datadog to PostgreSQL
CloudQuery is the simple, fast data integration platform that can fetch your data from Datadog APIs and load it into PostgreSQL
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Step by step guide for how to export data from Datadog to PostgreSQL
Table of Contents
MacOS Setup
Step 1: Install CloudQuery
To install CloudQuery, run the following command in your terminal:
brew install cloudquery/tap/cloudqueryStep 2: Create a Configuration File
Next, run the following command to initialize a sync configuration file for Datadog to PostgreSQL:
cloudquery init --source=datadog --destination=postgresqlThis will generate a config file named datadog_to_postgresql.yaml. Follow the instructions to fill out the necessary fields to authenticate against your own environment.
Step 3: Log in to CloudQuery CLI
Next, log in to the CloudQuery CLI. If you have't already, you can sign up for a free account as part of this step:
cloudquery loginStep 4: Run a Sync
cloudquery sync datadog_to_postgresql.yamlThis will start syncing data from the Datadog API to your PostgreSQL database! 🚀
See the CloudQuery documentation portal for more deployment guides, options and further tips.
FAQs
What is CloudQuery?
CloudQuery is an open-source tool that helps you extract, transform, and load cloud asset data from various sources into databases for security, compliance, and visibility.
Why does CloudQuery require login?
Logging in allows CloudQuery to authenticate your access to the CloudQuery Hub and monitor usage for billing purposes. Data synced with CloudQuery remains private to your environment and is not shared with our servers or any third parties.
What data does CloudQuery have access to?
CloudQuery accesses only the metadata and configurations of your cloud resources that you specify without touching sensitive data or workloads.
How is CloudQuery priced?
CloudQuery offers flexible pricing based on the number of cloud accounts and usage. Visit our pricing page for detailed plans.
Is there a free version of CloudQuery?
Yes, CloudQuery offers a free plan that includes basic features, perfect for smaller teams or personal use. More details can be found on our pricing page.
What authentication details do I need to sync from Datadog to PostgreSQL?
In order to set up your sync, you will need to create a
DD_CLIENT_API_KEY and a DD_CLIENT_APP_KEY. Full details on how to do this can be found in the Datadog documentation. When you set up the app key, Datadog will give you the option to set an access scope for that particular key. Cloudquery only requires read access to sync data from Datadog to PostgreSQL and you can limit the fields that the key grants access to as required.How can I track the information that CloudQuery is reading from Datadog?
All of the requests that Cloudquery makes to the Datadog API when running a sync to PostgreSQL will appear in the Datadog audit trail. You can use this to understand exactly what data is being read and adjust the scope of your app key if required.
What account details are required to set up a sync?
Alongside the App Key and API key, you’ll need to specify the name of your Datadog account in order to set up a sync. Full details on how to do this are contained in the authentication section of our integration documentation.
Which database versions are supported?
The CloudQuery PostgreSQL integration supports syncing to PostgreSQL version 10 or higher and CockroachDB version 20.2 or higher.
How long will the sync take?
Sync duration depends on several factors, but there are a number of CloudQuery features that ensure that syncs are as fast as possible. CloudQuery syncs data from Datadog to PostgreSQL in a streaming fashion using Apache Arrow. This means that as soon as it receives data from Datadog it delivers it to the PostgreSQL integration. This means that data is delivered as the sync progresses rather than only at the end.
Can I send data to multiple PostgreSQL instances using the destination plug-in?
Yes, you can configure multiple destination blocks for different PostgreSQL instances or databases.
What are the best practices for handling large datasets with the PostgreSQL destination integration?
Use indexing, partitioning, and batch inserts to manage large datasets effectively and minimize performance impacts.
How do I monitor and troubleshoot data transfers to PostgreSQL?
Check CloudQuery logs for detailed error messages, validate connectivity settings, and monitor database performance using PostgreSQL’s built-in tools.