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Data warehouse ⇄ Business productivity

Apache Druid to GitHub integration — real-time, two-way sync

Keep Apache Druid and GitHub in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

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  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Apache Druid and GitHub

Get the data locked inside GitHub into Apache Druid as live tables, and send results back where GitHub can use them, without writing a pipeline.

Whatever GitHub is used for, it accumulates data the rest of the company wants to analyze, and that data usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay for it.

Stacksync syncs Repositories, Issues, Pull Requests, Commits from GitHub into tables in Apache Druid continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in Apache Druid can also be written back into fields in GitHub where the tool can use them.

Common use cases

  • Publish release data into customer-communication tools when a new version ships.
  • Two-way sync of GitHub issues with Jira, Linear, or a support system so engineering and customer-facing teams work in their own tools.
  • Sync Druid query results into a warehouse to combine real-time aggregates with historical models.
  • Keep lookup tables in Druid refreshed from a CRM or database so query-time joins use current reference data.

Cross-tool reporting

Combine GitHub's data with data from every other synced system to answer questions no single tool can.

Where GitHub accepts updates: operational write-back

Segments, scores, or reference values computed in Apache Druid sync back onto records in GitHub, putting analysis where the work happens.

History that outlives the tool

A continuously synced copy in Apache Druid preserves a queryable record even as data ages out of GitHub or gets changed inside it.

What you can sync between Apache Druid and GitHub

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

Apache Druid objects GitHub objects
Metrics Numeric columns, often pre-aggregated at ingestion via rollup. Labels and Milestones Classification fields mapped to statuses and sprints in external trackers.
Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. Repositories Top-level containers whose metadata and settings syncs read to scope other objects.
Lookups Key-value mappings joined at query time, refreshable from external systems. Issues Synced two-way with project trackers and support tools, including labels and assignees.
Tasks Batch ingestion and compaction jobs monitored during data loads. Pull Requests Review state, status checks, and merge status feed engineering dashboards and workflow tools.
Datasources The table-like unit of storage and querying, the main target of reads and ingestion. Commits Read-only history used to link code activity to tickets and releases.
Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. Releases Tagged versions synced into changelogs, CRMs, or customer-notification systems.
What ships with Apache Druid ⇄ GitHub

Connect Apache Druid and GitHub for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–GitHub connection.

Real-time

Two-way sync

Changes in Apache Druid or GitHub instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Druid or GitHub data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Apache Druid or GitHub record.

Observability

Monitoring

Track your Apache Druid ⇄ GitHub sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Druid and GitHub.

How the Apache Druid and GitHub connectors work

Apache Druid

Integration surface
REST API (SQL over HTTP and native JSON queries); JDBC via Avatica
Authentication
Deployment-dependent: basic authentication or an authenticator extension; often fronted by a proxy
Change detection
Not applicable for reads out (polling by time interval); data enters Druid through streaming or batch ingestion rather than row updates
Capabilities
read · write
Rate limits
No fixed API quotas; query concurrency is bounded by broker and historical node capacity

GitHub

Integration surface
REST API and GraphQL API
Authentication
OAuth 2.0, fine-grained personal access tokens, or GitHub App installation tokens
Change detection
Webhooks with a broad event catalog covering issues, pull requests, pushes, and releases; polling for backfill
Capabilities
read · write · webhooks
Rate limits
Authenticated REST requests are limited to 5,000 per hour per user; GitHub Apps scale limits per installation.
How it works

How to connect Apache Druid to GitHub — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate Apache Druid and GitHub with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Apache Druid connected
    GitHub connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Druid and GitHub objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Apache Druid ⇄ GitHub
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Apache Druid GitHub
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Druid and GitHub integration FAQ

SECURITY

Security teams love Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

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DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 386 integrations available for Apache Druid and GitHub.

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