Real-time
Two-way sync
Changes in Dremio or Wrike instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Business productivity
Dremio to Wrike integration — real-time, two-way sync
Keep Dremio and Wrike in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Dremio and Wrike
Get the data locked inside Wrike into Dremio as live tables, and send results back where Wrike can use them, without writing a pipeline.
Whatever Wrike 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 Timelogs, Contacts, Workflows, Spaces from Wrike into tables in Dremio continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in Dremio can also be written back into fields in Wrike where the tool can use them.
Common use cases
- 01 Two-way sync of Tasks and their Custom Fields between a Wrike project and Postgres so ops teams work in SQL while project owners stay in Wrike.
- 02 Write closed deals or new orders from a CRM or ERP into Wrike Tasks to launch delivery, onboarding, or fulfillment projects automatically.
- 03 Reverse-ETL aggregates computed over lake data out to CRMs and finance tools for business users.
- 04 Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
Common sync patterns
Cross-tool reporting
Combine Wrike's data with data from every other synced system to answer questions no single tool can.
Where Wrike accepts updates: operational write-back
Segments, scores, or reference values computed in Dremio sync back onto records in Wrike, putting analysis where the work happens.
History that outlives the tool
A continuously synced copy in Dremio preserves a queryable record even as data ages out of Wrike or gets changed inside it.
What you can sync between Dremio and Wrike
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.
| Dremio objects | Wrike objects | How this pairing syncs | |
|---|---|---|---|
| Reflections Materialized accelerations that make repeated extraction queries cheaper. | Spaces Top-level containers that hold Folders, Projects, and their members; used to scope which Folders a given sync covers. | Reflections is specific to Dremio and Spaces to Wrike — each maps to any object or custom field on the other side. | |
| Jobs Query execution records useful for monitoring sync workloads. | Tasks The primary unit of work and main record; created, updated, completed, and deleted via the REST v4 API and synced two-way. Subtasks are Tasks linked by superTask/subTask references. | Jobs is specific to Dremio and Tasks to Wrike — each maps to any object or custom field on the other side. | |
| Sources Connected storage and database systems (S3, ADLS, relational databases) Dremio queries in place. | Folders & Projects The container hierarchy: Folders group Tasks, and Projects add dates, an owner, and a status. Each maps to a synced table scope, and its structure defines what a sync covers. | Sources is specific to Dremio and Folders & Projects to Wrike — each maps to any object or custom field on the other side. | |
| Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads. | Custom Fields Typed fields (Text, Numeric, Date, DropDown, Contacts, Checkbox) defined at account or space level; mapped to database columns, with values written by field ID. | Physical datasets is specific to Dremio and Custom Fields to Wrike — each maps to any object or custom field on the other side. | |
| Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts. | Comments Discussion and activity entries attached to Tasks and Folders; read out for history and reporting or written back as comments. | Virtual datasets (views) is specific to Dremio and Comments to Wrike — each maps to any object or custom field on the other side. | |
| Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads. | Timelogs Time-tracking entries logged against Tasks; read for billing and utilization reporting or written back when hours are recorded elsewhere. | Apache Iceberg tables is specific to Dremio and Timelogs to Wrike — each maps to any object or custom field on the other side. |
How changes propagate between Dremio and Wrike
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.
Dremio Wrike Interval-based propagation
DetectionStacksync polls Dremio for changes on an incremental schedule, reading only records changed since the previous pass. Polling via SQL.
DeliveryEach detected change is written to Wrike through its API, with automatic retries and rate-limit backoff.
Wrike Dremio Sub-second propagation
DetectionWrike notifies Stacksync of record changes through webhook events. Webhooks scoped to a folder, a space, or the whole account fire on events like TaskCreated, TaskStatusChanged, and FolderCreated, with event.
DeliveryEach detected change is applied to Dremio as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Dremio: Bounded by engine capacity and workload management rather than API rate limits.
- Wrike: Approximately 400 requests/minute per user (estimated on a per-second basis) with a higher per-IP ceiling; exceeding the limit or tripping Wrike's internal overload protection returns HTTP 429, handled with exponential backoff.
What ships with Dremio ⇄ Wrike
Connect Dremio and Wrike for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Dremio–Wrike connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Dremio or Wrike 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 Dremio or Wrike record.
Observability
Monitoring
Track your Dremio ⇄ Wrike sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Dremio and Wrike.
How the Dremio and Wrike connectors work
Dremio
- Integration surface
- Arrow Flight SQL, JDBC/ODBC, and a REST API
- Authentication
- Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
- Change detection
- Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
- Capabilities
- read · write
- Rate limits
- Bounded by engine capacity and workload management rather than API rate limits
Wrike
- Integration surface
- REST API v4 (JSON), single account endpoint such as www.wrike.com/api/v4; the data-center host (US or EU) comes from the OAuth token response, plus REST-managed Webhooks
- Authentication
- OAuth 2.0 for multi-user apps (Authorization header carrying access_token and requested scopes), and a legacy Permanent Access Token for single-account and testing use
- Change detection
- Webhooks scoped to a folder, a space, or the whole account fire on events like TaskCreated, TaskStatusChanged, and FolderCreated, with event filtering and custom payload fields; polling the task updatedDate is the fallback
- Capabilities
- read · write · webhooks
- Rate limits
- Approximately 400 requests/minute per user (estimated on a per-second basis) with a higher per-IP ceiling; exceeding the limit or tripping Wrike's internal overload protection returns HTTP 429, handled with exponential backoff
How it works
How to connect Dremio to Wrike — 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.
- 01
Connect your apps
Authenticate Dremio and Wrike with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Dremio
connected
Wrike
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Dremio and Wrike 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.
objects · Dremio ⇄ WrikeCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Dremio WrikeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Dremio and Wrike integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Dremio and Wrike: authenticate both systems, choose the objects to sync (such as Dremio's Reflections and Jobs), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Dremio and Wrike. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Dremio: Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed. On Wrike: Webhooks scoped to a folder, a space, or the whole account fire on events like TaskCreated, TaskStatusChanged, and FolderCreated, with event filtering and custom payload fields; polling the task updatedDate is the fallback. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Wrike side: Timelogs, Contacts, Workflows, Spaces, plus custom fields where Wrike exposes them. On the Dremio side: Physical datasets, Virtual datasets (views), Apache Iceberg tables, Spaces and folders. Stacksync auto-detects both schemas and converts types between the two systems.
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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.
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Common patterns for Dremio and Wrike: Cross-tool reporting; Where Wrike accepts updates: operational write-back; History that outlives the tool. Combine Wrike's data with data from every other synced system to answer questions no single tool can.
SECURITY
Security teams trust 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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
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:
Related integrations
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