Two-way sync
Changes in Apache Druid or Box instantly reflect in both systems. No stale data, no manual imports.
Keep Apache Druid and Box in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Apache Druid keeps the tables and query results a business reports on; Box keeps the raw files, documents, and objects that the same business produces and shares. The two overlap wherever a dataset lives as both — a file dropped in Box that has to become rows in Apache Druid, or a result in Apache Druid that people downstream need back as a file in Box. When that overlap is bridged by manual export and import or an overnight job, one side spends the day working from a stale copy.
Stacksync syncs Datasources, Segments, Dimensions, Metrics in Apache Druid with Users, Groups, Tasks, Events in Box field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync keeps every copy consistent and resolves conflicts by rules you set.
Classifications, scores, or status derived in Apache Druid are written back onto the matching Users, Groups, Tasks, Events in Box as metadata or tags, so the file store reflects what analytics decided.
Files and exports that arrive in Box are parsed into Datasources, Segments, Dimensions, Metrics in Apache Druid as they land, so analysts query current data instead of waiting on the next scheduled load.
Curated tables and query results from Apache Druid are written to Box as files the rest of the business can open, keeping the shared copy current without a hand-run export.
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 | Box objects | How this pairing syncs | |
|---|---|---|---|
| Tasks Batch ingestion and compaction jobs monitored during data loads. | Tasks Review or approval assignments on a file with due dates and states; read for workflow reporting or written to start approvals. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Segments Time-partitioned immutable files that hold datasource data; ingestion produces them. | Folders Hierarchical containers whose tree, names, and parent moves are mirrored so a target system reflects Box's structure; the account root is always folder ID 0. | Segments is specific to Apache Druid and Folders to Box — each maps to any object or custom field on the other side. | |
| Dimensions String and categorical columns used for filtering and grouping in synced queries. | Metadata Structured key-value instances attached to files and folders via metadata templates; synced two-way with database columns for classification and search. | Dimensions is specific to Apache Druid and Metadata to Box — each maps to any object or custom field on the other side. | |
| Metrics Numeric columns, often pre-aggregated at ingestion via rollup. | Collaborations Access grants linking a user or group to a file or folder with a role such as viewer, editor, or co-owner; written to manage sharing programmatically. | Metrics is specific to Apache Druid and Collaborations to Box — each maps to any object or custom field on the other side. | |
| Ingestion Supervisors Long-running specs that pull from streams like Kafka; the write path into Druid. | Users Managed and app users in the enterprise; provisioned, updated, and deprovisioned to keep Box access aligned with an HR or identity source. | Ingestion Supervisors is specific to Apache Druid and Users to Box — each maps to any object or custom field on the other side. | |
| Lookups Key-value mappings joined at query time, refreshable from external systems. | Groups Named user collections used for bulk collaboration; membership synced from a directory or IdP. | Lookups is specific to Apache Druid and Groups to Box — each maps to any object or custom field on the other side. |
Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.
DetectionStacksync polls Apache Druid for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Druid through streaming or batch ingestion rather than row updates.
DeliveryEach detected change is written to Box through its API, with automatic retries and rate-limit backoff.
DetectionBox notifies Stacksync of record changes through webhook events. V2 webhooks fire on triggers such as FILE.UPLOADED, FILE.TRASHED, and METADATA_INSTANCE.UPDATED.
DeliveryEach detected change is applied to Apache Druid as a row-level write, with types converted between the two schemas.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Druid–Box connection.
Changes in Apache Druid or Box instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Apache Druid or Box data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single Apache Druid or Box record.
Track your Apache Druid ⇄ Box sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Apache Druid and Box.
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.
Authenticate Apache Druid and Box with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pick the Apache Druid and Box 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.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time two-way integration between Apache Druid and Box: authenticate both systems, choose the objects to sync (such as Apache Druid's Tasks and Segments), map fields visually, and changes propagate both ways in milliseconds — no code required.
On the Apache Druid side: Datasources, Segments, Dimensions, Metrics, plus custom fields where Apache Druid exposes them. On the Box side: Users, Groups, Tasks, Events. Stacksync auto-detects both schemas and converts types between the two systems.
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
Common patterns for Apache Druid and Box: Where Apache Druid computes the labels: push them onto the files; Where Box receives the raw files: land them as query-ready rows; Where Box is the shared drive: publish results back as files. Classifications, scores, or status derived in Apache Druid are written back onto the matching Users, Groups, Tasks, Events in Box as metadata or tags, so the file store reflects what analytics decided.
Apache Druid: 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. Box: REST API (api.box.com/2.0), plus the Events API and V2 Webhooks. Authentication: OAuth 2.0 for user-delegated access, or server-to-server auth via JWT (RSA keypair) or Client Credentials Grant (CCG) using a Box app; short-lived developer tokens for testing. Stacksync manages authentication, retries, and rate limits on both sides.
Apache Druid: Streaming ingestion from Kafka or Kinesis is managed by supervisors designed to provide exactly-once ingestion semantics. Box: Rate limits are enforced per user; hitting them returns HTTP 429, and clients are expected to honor the Retry-After header with exponential backoff. Stacksync's field mapping accounts for these differences between Apache Druid and Box without custom code.
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.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 431 integrations available for Apache Druid and Box.