Real-time
Two-way sync
Changes in Apache Pinot or AWS Aurora MySQL instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Apache Pinot to AWS Aurora MySQL integration — real-time, two-way sync
Keep Apache Pinot and AWS Aurora MySQL 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 AWS Aurora MySQL
Connect AWS Aurora MySQL 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 AWS Aurora MySQL'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 AWS Aurora MySQL where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in AWS Aurora MySQL sync into Apache Pinot in real time, and result tables in Apache Pinot sync back into AWS Aurora MySQL, with schema and type mapping between the two systems handled for you.
Common use cases
- 01 Push reference and dimension data into Pinot via batch segment loads to enrich event queries.
- 02 Sync Pinot aggregates into a warehouse to join low-latency metrics with modeled historical data.
- 03 Give backend services read and write access to ERP or billing data by syncing it into Aurora tables the application already queries.
- 04 Stream row changes from Aurora into SaaS tools via binlog CDC instead of scheduled batch exports.
Common sync patterns
Offload heavy reads
Point analytical queries at the synced copy in Apache Pinot and keep AWS Aurora MySQL focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from AWS Aurora MySQL 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 AWS Aurora MySQL, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Apache Pinot and AWS Aurora MySQL
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 | AWS Aurora MySQL objects | How this pairing syncs | |
|---|---|---|---|
| Tables The queryable unit, defined as offline, real-time, or hybrid; the main read target. | Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Offline Tables Batch-loaded tables merged with real-time data at query time. | Views Can serve as read-only sync sources for derived or filtered datasets. | Offline Tables is specific to Apache Pinot and Views to AWS Aurora MySQL — 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. | Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. | Indexes is specific to Apache Pinot and Foreign keys to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Tenants Logical groupings that isolate workloads on shared clusters. | Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. | Tenants is specific to Apache Pinot and Stored procedures and triggers to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Schemas Column definitions (dimensions, metrics, time columns) mapped during integration setup. | Databases (schemas) Logical namespaces that scope which tables a sync connection can see. | Schemas is specific to Apache Pinot and Databases (schemas) to AWS Aurora MySQL — each maps to any object or custom field on the other side. | |
| Segments Immutable data files that batch ingestion uploads and the cluster serves. | Rows Inserted, updated, and deleted individually or in bulk during two-way syncs. | Segments is specific to Apache Pinot and Rows to AWS Aurora MySQL — each maps to any object or custom field on the other side. |
How changes propagate between Apache Pinot and AWS Aurora MySQL
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 AWS Aurora MySQL 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 AWS Aurora MySQL as a row-level write, with types converted between the two schemas.
AWS Aurora MySQL Apache Pinot Sub-second propagation
DetectionChanges in AWS Aurora MySQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback.
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.
What ships with Apache Pinot ⇄ AWS Aurora MySQL
Connect Apache Pinot and AWS Aurora MySQL for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Apache Pinot–AWS Aurora MySQL connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Apache Pinot or AWS Aurora MySQL 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 AWS Aurora MySQL record.
Observability
Monitoring
Track your Apache Pinot ⇄ AWS Aurora MySQL 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 AWS Aurora MySQL.
How the Apache Pinot and AWS Aurora MySQL 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
AWS Aurora MySQL
- Integration surface
- SQL wire protocol (MySQL-compatible), standard MySQL drivers and JDBC
- Authentication
- Database credentials, optionally AWS IAM database authentication, over TLS
- Change detection
- Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback
- Capabilities
- read · write · CDC
How it works
How to connect Apache Pinot to AWS Aurora MySQL — 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.
- 01
Connect your apps
Authenticate Apache Pinot and AWS Aurora MySQL with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Apache Pinot
connected
AWS Aurora MySQL
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Apache Pinot and AWS Aurora MySQL 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.
objects · Apache Pinot ⇄ AWS Aurora MySQLCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Apache Pinot AWS Aurora MySQLCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Apache Pinot and AWS Aurora MySQL integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Apache Pinot and AWS Aurora MySQL: authenticate both systems, choose the objects to sync (such as Apache Pinot's Tables and Offline Tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Apache Pinot: Data is stored in immutable segments; batch writes happen by building and uploading segments rather than issuing row inserts. AWS Aurora MySQL: Aurora separates compute from a distributed storage layer that replicates data six ways across three Availability Zones, independent of the instances that CDC readers and sync writers connect to. Stacksync's field mapping accounts for these differences between Apache Pinot and AWS Aurora MySQL without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Apache Pinot and AWS Aurora MySQL records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Apache Pinot and AWS Aurora MySQL connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Apache Pinot–AWS Aurora MySQL integration in-house.
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Yes — Stacksync ships production-grade connectors for both Apache Pinot and AWS Aurora MySQL. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Apache Pinot: Not applicable for reads out (polling by time column); data enters Pinot via streaming ingestion or segment upload, not row-level writes. On AWS Aurora MySQL: Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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.
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:
Related integrations
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