Your Atlassian tools generate a significant amount of valuable data. Jira tracks every work item, sprint, and delivery milestone. Jira Service Management captures every request, resolution time, and SLA metrics. Confluence holds documentation, decisions, and team knowledge. But for most organizations, the full picture of business performance does not live in Atlassian alone. Financial data sits in a data warehouse. Customer data lives in a CRM. Operational metrics are stored in external databases that have nothing to do with the Atlassian ecosystem.
When these data sources remain disconnected, the insights they produce are inherently incomplete. Leaders make decisions based on a partial view of the business. Teams analyze performance without the broader context that would make that analysis meaningful. And the value of the data an organization has already collected goes unrealized because no single tool can see all of it at once.
Atlassian Analytics addresses this directly. For organizations on the Cloud Enterprise plan, Atlassian Analytics supports connections to a wide range of third-party SQL databases and data warehouses, making it possible to blend external data with your Atlassian Data Lake in one unified analytical environment. The supported third-party sources include Amazon Athena, Amazon Aurora, Amazon Redshift, Databricks, Google BigQuery, Google Sheets, Microsoft SQL Server, MySQL, and Snowflake, covering the most widely used data platforms in enterprise environments today.
The result is a single place where Atlassian data and external business data come together, enabling richer analysis, more complete dashboards, and insights that reflect the full complexity of how your organization actually operates.
Before connecting a third-party data source to Atlassian Analytics, make sure the following requirements are in place:
Cloud Enterprise Plan: Third-party data source connections are available exclusively on the Cloud Enterprise plan for Jira, Jira Service Management, or Confluence. If your organization is not currently on an Enterprise plan, this feature will not be accessible.
Atlassian Organization Admin Permissions: Only Atlassian Organization Admins have the ability to manage and create data source connections. If you do not have this level of access, you will need to coordinate with your organization's admin to complete the setup.
Before touching anything inside Atlassian Analytics, you need to complete an important prerequisite on the infrastructure side. Atlassian Analytics communicates with your external database by sending outbound requests from a specific set of IP addresses. If your database or firewall does not recognize these addresses, the connection will be blocked before it even begins.
Log into your external database environment or work with your network or infrastructure team to add Atlassian's outbound IP addresses to your allowlist. The specific IP addresses can be found in Atlassian's official documentation and vary depending on your region. Completing this step first prevents connectivity failures later in the process and is the most common reason initial connection attempts fail.
Once your IP whitelist is in place, log into Atlassian Analytics from your Atlassian Cloud instance. In the left sidebar navigation, select Data Sources. This is the central hub where all of your connected data sources, both Atlassian and third-party, are managed. From here you can view existing connections, monitor their status, and add new ones.
Click "Add Data Source" to open the list of supported third-party providers. Browse the available options and select the specific platform you want to connect, whether that is Snowflake, Google BigQuery, MySQL, Microsoft SQL Server, Databricks, or one of the other supported sources. Each provider has its own dedicated connection form tailored to the specific details that platform requires, so the fields you see will vary depending on your selection.
This is the most technically detailed step of the process. You will need to provide the following information accurately to establish a successful connection:
Hostname or IP Address: This is the address where your external database is hosted. It may be a domain name or a numeric IP address depending on how your database environment is configured. Your database administrator will be able to provide this if you are unsure.
Port: Each database platform communicates over a specific port. MySQL uses port 3306 by default. Databricks uses port 443. Google BigQuery and Snowflake use their own standard ports. Make sure the correct port is entered and that it is open and accessible from Atlassian's IP addresses.
Credentials: Atlassian recommends using a read-only database user for this connection. This limits access to data retrieval only and prevents any risk of Atlassian Analytics modifying or deleting data in your external database. Depending on the platform, you will provide either a username and password combination or a Personal Access Token.
Database Name and Schema: Specify the exact database and schema you want Atlassian Analytics to access. This scopes the connection to the relevant dataset rather than giving broad access to everything in your database environment, which is both more secure and more practical for analytical purposes.
With all connection details entered, click "Test Connection" before finalizing. This triggers a live check that confirms Atlassian Analytics can successfully reach your external database using the credentials and network settings you have provided. If the test returns an error, review each field carefully, confirm that the IP whitelist from Step 1 is correctly configured, and verify that the credentials have the necessary read permissions.
Once the test returns a successful result, click "Connect" to finalize the integration. Your third-party data source will now appear alongside your Atlassian Data Lake in the Data Sources panel and will be available to use when building charts, dashboards, and queries within Atlassian Analytics. You can now blend this external data with your Atlassian data to create a more complete and meaningful view of your organization's performance.
Atlassian Analytics supports direct connections to a broad range of enterprise data platforms, covering the most widely used cloud warehouses, relational databases, and file-based sources in use across organizations today.
For organizations storing large volumes of structured business data in cloud-based warehouses, Atlassian Analytics connects directly to Snowflake, Google BigQuery, Amazon Redshift, and Databricks. These platforms are commonly used for storing financial data, customer analytics, operational metrics, and other business-critical datasets that, when blended with Atlassian data, produce a significantly richer and more complete picture of organizational performance.
For teams working with traditional relational database systems, Atlassian Analytics supports connections to MySQL, PostgreSQL, and Microsoft SQL Server. These are among the most widely deployed database platforms in enterprise environments and are often used to store transactional data, product data, and operational records that can add meaningful context to Atlassian project and service metrics.
For teams that manage data in spreadsheets, Atlassian Analytics supports direct connections to Google Sheets. This makes it straightforward to incorporate manually maintained datasets, such as budget trackers, headcount data, or custom KPI logs, into your Atlassian Analytics dashboards without requiring a formal database setup.
Most organizations are sitting on more analytical potential than they realize. The problem is rarely a lack of data. It is a lack of connection between the data they already have. When Atlassian data and external business data remain in separate systems, the insights produced by each are inherently limited by what they cannot see.
Connecting third-party data sources to Atlassian Analytics changes that. By bringing your cloud warehouses, relational databases, and file-based sources into the same analytical environment as your Jira, Jira Service Management, and Confluence data, you create a unified view of performance that reflects how your organization actually operates, not just how one part of it does.
The result is dashboards that tell the full story, decisions that are grounded in complete information, and a leadership team that no longer has to reconcile data from multiple disconnected tools to understand what is happening across the business.
Atlas Bench works deeply within the Atlassian ecosystem and has extensive experience helping teams turn Atlassian Analytics into a practical reporting capability, not just an enabled feature.
As an Atlassian Platinum Solution Partner, we focus on Atlassian tools and how they work together in real environments. That includes helping teams prepare Jira, Confluence, and Jira Service Management for cross‑tool reporting, designing scalable datasets, and building dashboards that reflect real workflows instead of disconnected metrics.
Is your team just getting started with Atlassian Analytics or struggling to make cross‑tool reporting useful at scale? Atlas Bench helps bridge the gap between raw data and meaningful insight.