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Database ⇄ Data warehouse

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

Keep Amazon Aurora and Apache Pinot 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 Amazon Aurora and Apache Pinot

Connect Amazon Aurora 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 Amazon Aurora'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 Amazon Aurora where the services that read from it get them at normal query latency.

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

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 Stream row-level changes from Aurora into a warehouse for near-real-time analytics without batch exports.
  • 04 Consolidate several Aurora clusters into one reporting database.

Common sync patterns

Fresh analytics without loading windows

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

Offload heavy reads

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

Operational data in the warehouse, minus the pipeline

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

What you can sync between Amazon Aurora and Apache Pinot

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.

Amazon Aurora objects Apache Pinot objects How this pairing syncs
Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Tables Relational tables synced bi-directionally at row level. Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Read Replicas Reader endpoints that syncs can target to keep load off the writer. Real-time Tables Tables fed continuously from streams like Kafka, including upsert-enabled tables. Read Replicas is specific to Amazon Aurora and Real-time Tables to Apache Pinot — each maps to any object or custom field on the other side.
Databases Logical databases within a cluster that scope a sync connection. Offline Tables Batch-loaded tables merged with real-time data at query time. Databases is specific to Amazon Aurora and Offline Tables to Apache Pinot — each maps to any object or custom field on the other side.
Views Read-only query-backed sources for downstream syncs. Indexes Inverted, range, and star-tree indexes that determine which sync queries run at low latency. Views is specific to Amazon Aurora and Indexes to Apache Pinot — each maps to any object or custom field on the other side.
Materialized Views Precomputed result sets (PostgreSQL-compatible clusters) readable as sources. Tenants Logical groupings that isolate workloads on shared clusters. Materialized Views is specific to Amazon Aurora and Tenants to Apache Pinot — each maps to any object or custom field on the other side.

How changes propagate between Amazon Aurora and Apache Pinot

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.

Amazon Aurora Apache Pinot Sub-second propagation

DetectionChanges in Amazon Aurora are captured at the source via change data capture — no polling loop against its API. Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters.

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

Apache Pinot Amazon Aurora 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 Amazon Aurora as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Amazon Aurora: No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits.
  • Apache Pinot: No fixed API quotas; query throughput depends on broker and server sizing.
What ships with Amazon Aurora ⇄ Apache Pinot

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon Aurora or Apache Pinot 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 Amazon Aurora or Apache Pinot record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Amazon Aurora and Apache Pinot connectors work

Amazon Aurora

Integration surface
MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS
Authentication
Database credentials or IAM database authentication
Change detection
Log-based CDC: binlog on MySQL-compatible clusters, logical replication/decoding on PostgreSQL-compatible clusters; polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits for wire-protocol access; throughput is bounded by instance class and connection limits

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
How it works

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

    Choose tables

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

Amazon Aurora and Apache Pinot 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 367 integrations available for Amazon Aurora and Apache Pinot.

Popular · 3 of 367
All Amazon Aurora integrations All Apache Pinot integrations Browse all integrations
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