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Data warehouse ⇄ Business productivity

Databricks to Wrike integration — real-time, two-way sync

Keep Databricks 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.

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

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Why teams connect Databricks and Wrike

Get the data locked inside Wrike into Databricks 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 Databricks continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in Databricks 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 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 04 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.

Common sync patterns

Where Wrike accepts updates: operational write-back

Segments, scores, or reference values computed in Databricks sync back onto records in Wrike, putting analysis where the work happens.

History that outlives the tool

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

Analytics on Wrike's data

Records and events from Wrike land in Databricks as queryable tables, current within seconds and ready to join with the rest of the warehouse.

What you can sync between Databricks 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.

Databricks objects Wrike objects How this pairing syncs
Volumes Unity Catalog file storage used for staging bulk loads. Workflows Sets of custom statuses grouped into stages (Active, Completed, Cancelled, Deferred); read to map task status transitions to database values. Volumes is specific to Databricks and Workflows to Wrike — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Spaces Top-level containers that hold Folders, Projects, and their members; used to scope which Folders a given sync covers. SQL Warehouses is specific to Databricks and Spaces to Wrike — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. 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. Change Data Feed is specific to Databricks and Tasks to Wrike — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. 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. Catalogs is specific to Databricks and Folders & Projects to Wrike — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. 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. Schemas is specific to Databricks and Custom Fields to Wrike — each maps to any object or custom field on the other side.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Comments Discussion and activity entries attached to Tasks and Folders; read out for history and reporting or written back as comments. Delta Tables is specific to Databricks and Comments to Wrike — each maps to any object or custom field on the other side.

How changes propagate between Databricks 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.

Databricks Wrike Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

DeliveryEach detected change is written to Wrike through its API, with automatic retries and rate-limit backoff.

Wrike Databricks 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 Databricks as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace 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 Databricks ⇄ Wrike

Connect Databricks and Wrike for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Wrike connection.

Real-time

Two-way sync

Changes in Databricks or Wrike instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks 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 Databricks or Wrike record.

Observability

Monitoring

Track your Databricks ⇄ Wrike sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and Wrike.

How the Databricks and Wrike connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace 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
Wrike setup guide
How it works

How to connect Databricks 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.

  1. 01

    Connect your apps

    Authenticate Databricks 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Databricks connected
    Wrike connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Databricks ⇄ Wrike
    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
    Databricks Wrike
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and Wrike integration FAQ

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.

Learn more about security
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:

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 555 integrations available for Databricks and Wrike.

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