Pre-built infor m3 etl connector with every MMS/CMS/CRS/OOL/MMO/MITMAS prefix family supported, dual-path extraction (BE database + ION Connect APIs), multi-CONO + multi-language preservation, FBDI/HDL emitters and warehouse-ready Parquet output. Replace 6–12 months of bespoke ETL.
Custom M3 ETL always starts with one item-master query and ends 12 months later as 40,000 lines of tangled SQL nobody on the team can debug. A pre-built connector with M3-DNA changes the economics.
M3 BE schemas run to thousands of tables across the MMS/CMS/CRS/OOL/MMO/MITMAS/FGL/FAP/FSL prefix families. Multi-CONO joins are non-trivial. Multi-language fan-outs trip up junior developers. ION Connect APIs have their own pagination and rate-limit quirks. MEC trading-partner identifiers need preservation through every transformation. Modification Suite extensions vary tenant-to-tenant. Custom ETL development that handles all of this from scratch is a 6–12 month engineering programme — and then you own the maintenance forever.
The Syntra ETL infor m3 etl connector replaces that with pre-built support for every prefix family, every documented join pattern, every multi-CONO scenario, every multi-language fan-out, every standard ION BOD pattern, and the canonical FBDI/HDL/REST output formats for Fusion. The connector is the product, not the script. Quarterly updates track M3's own roadmap. SLA-backed support covers the migration phase and ongoing operations.
Same engine handles three deployment modes: one-time bulk migration to Fusion, hybrid steady-state integration between retained M3 and new Fusion, and warehouse-fed analytics integration to Snowflake / BigQuery / Databricks. Change destination without rewriting extraction logic. Add a CONO without rewriting transformation logic. Schema drift detected automatically. Audit log SOC 2-grade out of the box.
Each capability replaces a workstream that bespoke ETL would otherwise consume months on.
M3 BE database (Oracle/SQL Server) for high-volume tables, ION Connect APIs for entities where ION is system-of-record. Per-entity routing, unified manifest, single audit log.
Every row tagged with source CONO/DIVI, crosswalks per company, output partitions segmented per Fusion BU. Multi-org reconciliation produces clean per-entity evidence without bleed.
CMS-style per-language fan-out tables collapsed into target translation framework. Item descriptions, customer names, category descriptions — every language variant preserved.
Transaction, posting and statutory currency preserved per document. CRS055 exchange rate history archived with rate-type DFFs for forensic traceability.
MITLOC/MITALO chains preserved end-to-end. Source lot → manufacturing consumption → finished lot → shipped lot. Recall traceability works on day one in the target system.
Continuous schema-integrity verification at every extract run. New columns, missing columns, type changes, constraint changes — all surfaced with diff. No silent extraction against stale schema.
A repeatable setup workflow that lands the connector in production-grade configuration in four weeks.
In-scope CONOs, entities and historical depth confirmed. Read-only DB user provisioned with SELECT grants on scoped tables. ION Connect OAuth2 client established. KMS credential storage configured. Output destination(s) confirmed.
Connector crawls M3 BE schema, profiles row counts and update-frequency per CONO, maps multi-CONO/DIVI partitioning, inventories Modification Suite extensions. Output: extract topology with sizing and parallelism plan.
Pre-built crosswalk library loaded as starting point per domain. Tenant-specific edits captured in workshops with finance, SCM, manufacturing leads. Crosswalk register version-controlled.
Single-CONO single-FY test extract validates schema, joins, multi-language and multi-currency handling. Output: validated test dataset with KMS-signed manifest and reconciliation evidence.
Full historical extract for all in-scope CONOs and entities, parallel jobs (8–16 workers), throttled to M3 BE response SLAs. Loaded to target destination. Per-CONO per-entity reconciliation.
Production scheduling configured (cron, event-driven, modified-since deltas). Schema-drift monitoring deployed. Runbooks documented. On-call rotation established. Handover to operations.
One connector configuration, swappable output adapter. No rewrite for new destinations.
FBDI for bulk loads, HDL for HCM-adjacent, REST for incremental and delta. Schema-validated locally pre-submission. Every Fusion 26x release supported.
Direct JDBC insert into Oracle ADB schemas. Used for Fusion-adjacent reporting, post-cutover analytics, or as a Fusion warm archive layer.
COPY INTO from staged Parquet. Partitioned by CONO, fiscal year, entity. Direct integration with Snowflake catalog for analytics and BI.
Parquet load via gsutil and bq load. Native partitioning and clustering. Used for unified historical + current reporting alongside Fusion data.
Delta Lake write with full schema enforcement. Bronze/Silver/Gold medallion architecture supported. ML feature-store integration native.
Raw Parquet for object-store consumers, Kafka topics for real-time event streams. Same engine, different output adapter.
An infor m3 etl connector is a pre-built piece of software that knows how to extract data from an Infor M3 Business Engine environment, transform it according to configured rules, and load it into a target system — without the team having to write or maintain bespoke JDBC, custom BOD subscribers, or one-off SQL transformation scripts. Syntra ETL's infor m3 etl connector ships pre-built support for every MMS/CMS/CRS/OOL/MMO/MITMAS/FGL/FAP/FSL prefix family, dual-path extraction (M3 BE database OR ION Connect APIs), multi-CONO awareness, multi-language and multi-currency preservation, FBDI/HDL/REST output for Oracle Fusion, Parquet/JSON output for warehouses, audit-signed manifests, OAuth2 and read-only JDBC security models. One configured connector replaces 6–12 months of bespoke ETL development.
Multi-CONO is treated as a first-class concern from the first extract — every row tagged with source CONO and DIVI, crosswalks executed per company, output partitions segmented per Fusion BU (or per target-system equivalent). Multi-language is preserved by collapsing M3's CMS-style per-language fan-out tables into the target system's translation framework — Fusion's item-master MTL_SYSTEM_ITEMS_TL, TCA party-name translations, or the warehouse-side translation table depending on target. Nothing gets lost. An item with English, German, French, Italian and Polish descriptions in M3 lands with all five descriptions in the target. A Nordic CONO's inter-company posting to a Continental CONO reconciles per pair without bleed.
Oracle Fusion (FBDI for bulk loads, HDL for HCM-adjacent data, REST for incremental and delta), Oracle Autonomous Database (direct insert via JDBC), Oracle Analytics Cloud (data feed for OAS), Snowflake (COPY INTO from staged Parquet), Google BigQuery (Parquet load via gsutil), Databricks (Delta Lake write), AWS S3 / Azure Blob / Google Cloud Storage (raw Parquet for downstream consumers), Kafka (event-stream for real-time consumers), and direct file output (CSV/JSON Lines/Parquet) for any custom downstream. Same infor m3 etl connector configuration, different output adapter — change destination without rewriting extraction or transformation logic.
M3 BE schemas evolve with patch levels, version upgrades and Modification Suite changes. Custom JDBC extractors break silently when columns change. The Syntra ETL infor m3 etl connector runs continuous schema-drift detection: every extract verifies the source schema against the last-known canonical schema, flags any new columns, missing columns, type changes or constraint changes, and surfaces the diff for the engineering team to review. Crosswalks affected by drift get flagged for update. The connector does not silently extract against stale assumptions — schema integrity is verified at every run, and stale crosswalks block the load until reconciled.
Yes. Cron-style scheduling, event-driven triggers (ION publish events, DB CDC watermarks), and one-shot manual runs all supported. Delta extraction uses modified-since watermarks (M3 audit-trail columns where present, DB CDC streams otherwise) so only changed rows since the last successful extract land in the target. Common scheduling patterns include nightly full master-data refresh, hourly delta during cutover parallel-run, and event-driven near-real-time during hybrid steady-state. Failed jobs resume from the last checkpoint, not from zero. Idempotent — re-running an extract produces identical output.
The infor m3 etl connector supports both extraction modes natively and can use them in combination per entity. ION Connect REST endpoints provide structured BOD payloads with pagination and rate-limit handling built in — used for entities where ION is the system-of-record for the integration (e.g., Sync-Item from M3 PIM, Process-Invoice from M3 AR). Direct database extraction (read-only JDBC against the M3 BE Oracle or SQL Server backend) handles the high-volume tables (FGL, FAP, FSL, MITLOC, OOH historical) where ION pagination would take days. The connector orchestrates both, with per-entity routing and a unified audit-signed manifest spanning both extraction modes.
Read-only DB user with SELECT-only grants on scoped tables — no schema modifications, no triggers, no admin shortcuts. ION Connect uses OAuth2 client_credentials with minimal scope. Credentials stored in cloud KMS. Every query and every API call logged with timestamp, user identity and row count for SOC 2 audit. Per-extract manifest signed with KMS private key — tamper-evident, auditor-verifiable. Rate-limit aware (429 back-off transparent). DB load monitored — parallelism throttled if M3 BE response times degrade. Replayable runs — failed jobs resume from checkpoint. Read-replica or snapshot-clone supported for strict change-window environments. Routinely passes Big 4 SOC 2, ISO 27001, FDA Part 11 reviews on first attempt.
Pricing is per CONO, per fiscal year of historical depth, and per output destination — not per row, not per GB. This means a 5-CONO tenant with 10 years of history loading to Fusion plus a warehouse pays a predictable annual subscription, not a usage-driven invoice that spikes during cutover and parallel-run. A typical mid-market multi-CONO M3-to-Fusion tenant is in the $80–180k/year range for the full infor m3 etl connector subscription including support, schema-drift monitoring, quarterly extractor updates aligned to Infor's roadmap, and 24x7 on-call for migration and cutover phases. Comparison: equivalent bespoke ETL development costs $400k–$1.2M upfront plus ongoing maintenance.
30-minute walkthrough. Connect to a sample M3 BE, run a multi-CONO extract live, see the audit-signed manifest, watch the FBDI emission, and see reconciliation evidence on real M3-shaped data.