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Data warehouse ⇄ Database

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

Keep Apache Pinot and SQL Server 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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Why teams connect Apache Pinot and SQL Server

Connect SQL Server and Apache Pinot 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 SQL Server's rows in Apache Pinot, 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 SQL Server where the services that read from it get them at normal query latency.

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

Common use cases

  • 01 Query per-account usage metrics from Pinot and sync them into CRM fields so sales sees product activity.
  • 02 Push reference and dimension data into Pinot via batch segment loads to enrich event queries.
  • 03 Mirror on-premises ERP data held in SQL Server into cloud CRM and support systems
  • 04 Feed a cloud warehouse from SQL Server continuously using native CDC instead of SSIS batch jobs

Common sync patterns

Offload heavy reads

Point analytical queries at the synced copy in Apache Pinot and keep SQL Server focused on its operational workload.

Operational data in the warehouse, minus the pipeline

Rows from SQL Server land in Apache Pinot as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in Apache Pinot sync into SQL Server, where whatever reads from that database gets them without querying the warehouse.

What you can sync between Apache Pinot and SQL Server

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 SQL Server objects How this pairing syncs
Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. Tables The primary sync target; rows map to records in connected systems. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. Schemas Namespaces (dbo and custom) used to organize synced tables. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. Custom fields on either side are included in the mapping.
Segments Immutable data files that batch ingestion uploads and the cluster serves. Databases Instance-level databases that scope a sync's reads and writes. Segments is specific to Apache Pinot and Databases to SQL Server — 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. Views Read-side projections used as outbound sync sources. Real-time Tables is specific to Apache Pinot and Views to SQL Server — 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. Columns Field-level mapping targets with T-SQL types. Offline Tables is specific to Apache Pinot and Columns to SQL Server — 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. Primary and Unique Keys Match keys for idempotent upserts and conflict handling. Indexes is specific to Apache Pinot and Primary and Unique Keys to SQL Server — each maps to any object or custom field on the other side.

How changes propagate between Apache Pinot and SQL Server

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 SQL Server 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 SQL Server as a row-level write, with types converted between the two schemas.

SQL Server Apache Pinot Sub-second propagation

DetectionChanges in SQL Server are captured at the source via change data capture — no polling loop against its API. SQL Server Native Change Data Capture (CDC).

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.
  • SQL Server: No API rate limits; throughput depends on instance resources, licensing tier, and connection limits.
What ships with Apache Pinot ⇄ SQL Server

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Apache Pinot or SQL Server 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 SQL Server record.

Observability

Monitoring

Track your Apache Pinot ⇄ SQL Server 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 SQL Server.

How the Apache Pinot and SQL Server 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

SQL Server

Integration surface
SQL over the TDS wire protocol (Tabular Data Stream), via ODBC/JDBC/ADO.NET drivers
Authentication
Database credentials entered as a connection string or as parameters (host/user/password) in the Create New Sync page
Change detection
SQL Server Native Change Data Capture (CDC); a DBA runs a one-time setup script with sysadmin privileges to enable CDC and create Stacksync wrapper procedures
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput depends on instance resources, licensing tier, and connection limits
SQL Server setup guide
How it works

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

    Choose tables

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

Apache Pinot and SQL Server 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 470 integrations available for Apache Pinot and SQL Server.

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