Real-time
Two-way sync
Changes in Jdbc or Sage 300 instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ ERP
Jdbc to Sage 300 integration — real-time, two-way sync
Keep Jdbc and Sage 300 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 Jdbc and Sage 300
Give your engineers Sage 300's data in Jdbc: read it with normal queries, write back through the sync, and skip the ERP API entirely.
ERP data sits behind interfaces built for the ERP's own modules, not for your internal systems. Teams that need those records, for reporting services, internal tools, or automations, end up writing integration code against a strict API and maintaining it through every upgrade.
Stacksync mirrors AR Customers, AP Vendors, GL Accounts, Journal Batches from Sage 300 into Jdbc and keeps both sides consistent in real time. Whatever Sage 300 is the system of record for, whether financials, operations, people, or procurement, those records become rows your code can query, and changes written in Jdbc sync back into Sage 300 with its validations respected.
Common use cases
- 01 Incrementally sync a high-volume table by polling an updated_at or auto-increment column, keeping a downstream store fresh without full reloads.
- 02 Consolidate several databases from different vendors into one relational store by syncing each through its own JDBC driver.
- 03 Write web and EDI orders into Order Entry and return order status to the source channel.
- 04 Replicate GL, AR, and AP detail to a warehouse for consolidated multi-company reporting.
Common sync patterns
React to ERP changes
Updates in Sage 300 arrive as row changes in Jdbc, so jobs and triggers can respond as the business record changes.
Where Sage 300 is the HR system of record: people data for internal systems
Worker and org records stay current in Jdbc for provisioning, access, and reporting systems that read from the database.
Controlled write-back
Choose exactly which tables and fields may flow from Jdbc back into Sage 300, keeping the ERP authoritative.
What you can sync between Jdbc and Sage 300
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.
| Jdbc objects | Sage 300 objects | How this pairing syncs | |
|---|---|---|---|
| Tables The base relational tables in the target database; synced two-way as rows over SQL, with each table's primary key driving upserts and row-level updates. | Journal Batches GL entries staged in batches that must be posted; a common write target for external systems. | Tables is specific to Jdbc and Journal Batches to Sage 300 — each maps to any object or custom field on the other side. | |
| Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. | Order Entry Orders Sales orders written from e-commerce or EDI feeds. | Views is specific to Jdbc and Order Entry Orders to Sage 300 — each maps to any object or custom field on the other side. | |
| Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. | Purchase Orders Procurement documents read for spend visibility and receiving. | Columns is specific to Jdbc and Purchase Orders to Sage 300 — each maps to any object or custom field on the other side. | |
| Primary keys & indexes Key and index definitions read via DatabaseMetaData; the primary key is required for reliable upserts, and indexes on the cursor column keep incremental polling fast. | Inventory Items Item master with costing and quantities, synced to storefronts and WMS. | Primary keys & indexes is specific to Jdbc and Inventory Items to Sage 300 — each maps to any object or custom field on the other side. | |
| Schemas & catalogs Namespaces that group tables and views; the connector targets a schema/catalog and lists its objects from the JDBC metadata to build the sync. | AR Invoices and Receipts Billing and payment records replicated for cash and revenue reporting. | Schemas & catalogs is specific to Jdbc and AR Invoices and Receipts to Sage 300 — each maps to any object or custom field on the other side. | |
| Stored procedures & functions Server-side routines callable via JDBC CallableStatement; invoked for custom read or write logic when a table-level mapping is not enough. | AR Customers Receivables customer master synced to CRMs and billing tools. | Stored procedures & functions is specific to Jdbc and AR Customers to Sage 300 — each maps to any object or custom field on the other side. |
How changes propagate between Jdbc and Sage 300
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.
Jdbc Sage 300 Interval-based propagation
DetectionStacksync polls Jdbc for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.
DeliveryEach detected change is applied to Sage 300 as a row-level write, with types converted between the two schemas.
Sage 300 Jdbc Interval-based propagation
DetectionStacksync polls Sage 300 for changes on an incremental schedule, reading only records changed since the previous pass. Scheduled polling.
DeliveryEach detected change is applied to Jdbc as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Jdbc: No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
What ships with Jdbc ⇄ Sage 300
Connect Jdbc and Sage 300 for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Jdbc–Sage 300 connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Jdbc or Sage 300 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 Jdbc or Sage 300 record.
Observability
Monitoring
Track your Jdbc ⇄ Sage 300 sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Jdbc and Sage 300.
How the Jdbc and Sage 300 connectors work
Jdbc
- Integration surface
- JDBC API (java.sql / javax.sql) executing SQL through a JDBC driver, typically a pure-Java Type 4 driver; reaches any relational database with a driver - PostgreSQL, MySQL, SQL Server, Oracle, IBM DB2, and others - via a JDBC URL such as jdbc:postgresql://host:5432/db.
- Authentication
- A database user's username and password supplied in the JDBC connection (DriverManager or a DataSource), typically over a TLS/SSL-encrypted connection. Some drivers add Kerberos, integrated Windows auth, or cloud IAM-token auth, but the available methods depend on the target database and its driver.
- Change detection
- No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks.
- Capabilities
- read · write
- Rate limits
- No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
Sage 300
- Integration surface
- Sage 300 Web API (REST) on newer releases; .NET/COM SDK and direct SQL Server access on-prem
- Authentication
- Sage 300 user credentials (Basic auth on the Web API); database credentials for direct SQL reads
- Change detection
- Scheduled polling; batch-oriented modules with no webhook surface
- Capabilities
- read · write
How it works
How to connect Jdbc to Sage 300 — 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 Jdbc and Sage 300 with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Jdbc connected
Sage 300
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Jdbc and Sage 300 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 · Jdbc ⇄ Sage 300Customers 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.
Jdbc Sage 300Company company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Jdbc and Sage 300 integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Jdbc and Sage 300: authenticate both systems, choose the objects to sync (such as Jdbc's Tables and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Jdbc and Sage 300. 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 Jdbc: No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks. On Sage 300: Scheduled polling; batch-oriented modules with no webhook surface. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Jdbc side: Stored procedures & functions, Sequences, Tables, Views, plus custom fields where Jdbc exposes them. On the Sage 300 side: AR Customers, AP Vendors, GL Accounts, Journal Batches. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Jdbc and Sage 300: React to ERP changes; Where Sage 300 is the HR system of record: people data for internal systems; Controlled write-back. Updates in Sage 300 arrive as row changes in Jdbc, so jobs and triggers can respond as the business record changes.
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
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:
Related integrations
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Popular · 6 of 434
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Sage 300 ⇄ AWS Aurora MySQL - Sage 300 ⇄ AWS Aurora PostgreSQL
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Sage 300 ⇄ CockroachDB
No integrations match .
