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Sync data from Azure to Opensearch

CloudQuery is the simple, fast data integration platform that can fetch your data from Azure APIs and load it into Opensearch
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Opensearch

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Step by step guide for how to export data from Azure to Opensearch

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Table of Contents

MacOS Setup

Step 1: Install CloudQuery

To install CloudQuery, run the following command in your terminal:

brew install cloudquery/tap/cloudquery

Step 2: Create a Configuration File

Next, run the following command to initialize a sync configuration file for Azure to Opensearch:

cloudquery init --source=azure --destination=opensearch

This will generate a config file named azure_to_opensearch.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 login

Step 4: Run a Sync

cloudquery sync azure_to_opensearch.yaml

This will start syncing data from the Azure API to your Opensearch 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 data can CloudQuery sync from Azure to Opensearch?
The CloudQuery Azure Integration can sync a wide variety of Azure data to Opensearch. You can also limit the data that is synced to data contained in a particular subscription.
What permissions does the CloudQuery Azure Integration require?
We recommend that you only grant read permissions to the integration as this is all that you will require to sync your data from Microsoft Azure to Opensearch. As this is a source integration, CloudQuery will never write information to Azure.
Can I rate limit the requests that CloudQuery makes to the Azure API?
If you would like to reduce the number of requests that CloudQuery can make to the API when it is syncing data from Azure to Opensearch then you can set a rate limit in your account. These are managed on a per-subscription basis and further information can be found here.
What can I do with CloudQuery's OpenSearch integration?
CloudQuery's OpenSearch integration allows you to stream and manage cloud asset data into your OpenSearch clusters, providing real-time search and analytics capabilities for cloud infrastructure data.
How does the OpenSearch integration improve cloud asset management?
It helps you centralize and store cloud asset data in OpenSearch, enabling you to monitor and analyze asset performance, configuration, and utilization within a search-optimized platform.
Does CloudQuery support OpenSearch with authentication and encryption?
Yes, CloudQuery supports sending data to OpenSearch with authentication (HTTP basic or IAM roles) and encrypted communication via SSL/TLS. Ensure your OpenSearch cluster is configured with these security features enabled.
What kind of performance impact does sending data from CloudQuery have on my OpenSearch cluster?
CloudQuery is designed to optimize data streams and minimize load on OpenSearch clusters. You can manage performance impact by controlling batch sizes, scheduling data streams during off-peak hours, or limiting the scope of synced data.

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