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Data warehouse

Apache Pinot to BigQuery integration — real-time, two-way sync

Keep Apache Pinot and BigQuery 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 Apache Pinot and BigQuery

Keep tables consistent across Apache Pinot and BigQuery, for a migration, a multi-warehouse stack, or a dataset two platforms both need.

Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.

Stacksync syncs tables between Apache Pinot and BigQuery continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.

Common use cases

  • 01 Sync Pinot aggregates into a warehouse to join low-latency metrics with modeled historical data.
  • 02 Serve user-facing analytics from Pinot while syncing daily rollups to finance and ops tools.
  • 03 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
  • 04 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools

Common sync patterns

Consolidation after M&A

Bring the acquired company's warehouse data across continuously instead of through one-off dumps.

Migration without a big bang

When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.

Serve tools that only connect to one platform

Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.

What you can sync between Apache Pinot and BigQuery

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 Pinot objects BigQuery objects How this pairing syncs
Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. Tables The syncable unit: only tables can be synced per the Stacksync docs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Segments Immutable data files that batch ingestion uploads and the cluster serves. Datasets Organizational container — you pick which dataset’s tables to sync. Segments is specific to Apache Pinot and Datasets to BigQuery — each maps to any object or custom field on the other side.
Real-time Tables Tables fed continuously from streams like Kafka, including upsert-enabled tables. Projects Connection scope: the service account grants access per project. Real-time Tables is specific to Apache Pinot and Projects to BigQuery — each maps to any object or custom field on the other side.
Offline Tables Batch-loaded tables merged with real-time data at query time. Partitioned tables Synced like regular tables; partition columns map to target fields. Offline Tables is specific to Apache Pinot and Partitioned tables to BigQuery — each maps to any object or custom field on the other side.
Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. Clustered tables Supported; clustering is transparent to the sync. Indexes is specific to Apache Pinot and Clustered tables to BigQuery — each maps to any object or custom field on the other side.

How changes propagate between Apache Pinot and BigQuery

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.

Apache Pinot BigQuery Interval-based propagation

DetectionStacksync polls Apache Pinot for changes on an incremental schedule, reading only records changed since the previous pass. Data enters Pinot via streaming ingestion or segment upload, not row-level writes.

DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.

BigQuery Apache Pinot Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is applied to Apache Pinot as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
What ships with Apache Pinot ⇄ BigQuery

Connect Apache Pinot and BigQuery for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Pinot or BigQuery 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 Pinot or BigQuery record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Apache Pinot and BigQuery.

How the Apache Pinot and BigQuery connectors work

Apache Pinot

Integration surface
REST API (SQL queries via the broker; administration via the controller); JDBC client available
Authentication
Deployment-dependent: HTTP basic authentication or token-based auth where enabled
Change detection
Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes
Capabilities
read · write
Rate limits
No fixed API quotas; query throughput depends on broker and server sizing

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide
How it works

How to connect Apache Pinot to BigQuery — 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 Pinot and BigQuery 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 Pinot connected
    BigQuery connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Apache Pinot and BigQuery 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 Pinot ⇄ BigQuery
    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 Pinot BigQuery
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Apache Pinot and BigQuery 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
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 Apache Pinot and BigQuery.

Popular · 6 of 473
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