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Data warehouse ⇄ Database

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

Keep Databricks and TiDB 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 TiDB

Connect TiDB and Databricks with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Operational databases and analytical warehouses want the same data at different moments. Analysts want TiDB's rows in Databricks, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in TiDB where the services that read from it get them at normal query latency.

Stacksync covers both directions with one connection. Tables or collections in TiDB sync into Databricks in real time, and result tables in Databricks sync back into TiDB, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 02 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 03 Stream row-level changes to a warehouse or Kafka via CDC for near-real-time analytics
  • 04 Serve mixed transactional and analytical workloads on synced data using TiFlash columnar replicas

Common sync patterns

Operational data in the warehouse, minus the pipeline

Rows from TiDB land in Databricks as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in Databricks sync into TiDB, where whatever reads from that database gets them without querying the warehouse.

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

What you can sync between Databricks and TiDB

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 TiDB objects How this pairing syncs
Views Curated read-only projections used as sync sources for downstream tools. Views Logical views for shaping reads before syncing outward. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Indexes Secondary indexes that keep incremental sync queries efficient. Delta Tables is specific to Databricks and Indexes to TiDB — each maps to any object or custom field on the other side.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Sequences Server-side ID generation relevant when external systems write rows. Materialized Views is specific to Databricks and Sequences to TiDB — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. Databases MySQL-style schemas addressed by any MySQL-compatible client. Volumes is specific to Databricks and Databases to TiDB — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Tables Row data stored in TiKV; the primary unit for reads, writes, and CDC. SQL Warehouses is specific to Databricks and Tables to TiDB — 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. Columns MySQL-compatible types mapped to fields in the paired system. Change Data Feed is specific to Databricks and Columns to TiDB — each maps to any object or custom field on the other side.

How changes propagate between Databricks and TiDB

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 TiDB 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 applied to TiDB as a row-level write, with types converted between the two schemas.

TiDB Databricks Sub-second propagation

DetectionChanges in TiDB are captured at the source via change data capture — no polling loop against its API. Log-based CDC via TiCDC, which captures row changes from TiKV and streams them to downstream sinks.

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.
  • TiDB: Throughput scales with cluster size rather than fixed API quotas.
What ships with Databricks ⇄ TiDB

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or TiDB 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 TiDB record.

Observability

Monitoring

Track your Databricks ⇄ TiDB 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 TiDB.

How the Databricks and TiDB 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

TiDB

Integration surface
MySQL wire protocol (SQL)
Authentication
Database credentials (MySQL-compatible username/password)
Change detection
Log-based CDC via TiCDC, which captures row changes from TiKV and streams them to downstream sinks; polling also works
Capabilities
read · write · CDC
Rate limits
Throughput scales with cluster size rather than fixed API quotas
How it works

How to connect Databricks to TiDB — 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 TiDB 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
    TiDB connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and TiDB 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 ⇄ TiDB
    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 TiDB
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and TiDB 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 473 integrations available for Databricks and TiDB.

Popular · 6 of 473
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