A nightly ETL job keeps ERP product updates in sync.

Product updates from an ERP system are the lifeblood of a healthy catalog. Price changes, new SKUs, discontinued items—these details ripple through storefronts, warehouses, and customer-facing apps. The big question for many architects isn’t whether updates should flow, but how they should flow: in real time, or in batches that land overnight? If you’re pondering this for a real-world project, here’s a practical way to think about it, grounded in patterns you’ll see discussed in the Certified Integration Architect Designer space.

The question in plain terms

When you’ve got an ERP system pushing product catalog changes, what integration pattern keeps data current without turning complexity into chaos? The four options you might encounter look like this:

• A. Write a custom web service to accept product catalog changes from ERP

• B. Use the streaming API to receive product changes in real time from ERP

• C. Write an outbound message to send product changes in real time from ERP

• D. Build a scheduled ETL job to sync products on a nightly basis from ERP

The subtle truth behind the options

Options B and C promise immediacy. It’s tempting to chase the fastest path—real-time everything. In theory, you can slice, dice, and reflect every tweak the moment it happens. In practice, that brings a different set of headaches: event ordering, idempotency, sudden traffic spikes, and the need for robust error handling across systems that weren’t built to be perfectly synchronized.

Option A—custom web services—sounds neat and controllable. It can give you precise handling for specific change events, but it also drags in maintenance, security, versioning, and scale considerations that may not be obvious at first glance.

Option D—a scheduled ETL job that runs at night—appears slower. It’s easy to implement, easy to monitor, and straightforward to audit. It’s the familiar workhorse many teams rely on to keep data sane when real-time feels heavyweight or risky.

Why the nightly ETL pattern often wins in practice

Let me explain with a mental model you’ve probably used in other parts of integration architecture: consistency beats speed when you’re dealing with a catalog that, while important, doesn’t require an instantaneous mirror across every system.

• Predictability and control: A nightly ETL job gives you a single, well-defined window to pull from ERP, transform the data, validate it, and push it into target systems. You can design robust checks, rollback plans, and clear dashboards for data health without juggling a cascade of real-time edge cases.

• Simpler error handling: If something goes wrong, you’ve got a smaller blast radius to investigate. A batch window makes it easier to backfill, reprocess, and compare snapshots to ensure the catalog matches across platforms.

• Data quality and governance: With scheduled loads, you can implement strict data quality gates—field validations, normalization rules, and master data alignment—before anything hits downstream systems.

• Operational sanity: Nightly processing aligns well with human workflows—IT teams can monitor, troubleshoot, and tune jobs during business hours, while the updates land when people aren’t actively changing catalogs.

• Cost and complexity: Real-time streams and outbound messaging often require more complex error handling, message deduplication, ordering guarantees, and cross-system contracts. For many organizations, those complexities aren’t worth the marginal gain in timeliness.

A balanced view: when real-time might be worth the extra effort

That said, there are scenarios where real-time or near-real-time updates make sense, and you should stay open to them if business priorities demand it.

• High-velocity catalogs: If your product catalog changes multiple times per hour and those changes directly impact pricing, promotions, or stock availability visible to customers, real-time updates can improve user experience.

• Critical inventory or pricing accuracy: When a small delay can cause stock-outs, wrong pricing, or customer dissatisfaction, the extra rigor of a streaming or outbound approach may be warranted.

• Integration maturity and tooling: If your ecosystem already has robust event platforms, reliable streaming pipes, and strong governance around events, a real-time path can be more feasible.

In those cases, you’ll want to weigh questions like: Can you guarantee idempotency? How will you manage out-of-order events? Do you have a backpressure strategy if downstream systems slow down? Is there a clean way to handle late-arriving changes? The architectural gloves come off, and the pattern becomes less about “why this” and more about “how well can we do this without breaking downstream trust?”

Practical guidance for architects on product data feeds

If you’re leaning toward the nightly ETL pattern (the recommended approach in the scenario you’re studying), here are practical elements to consider so the design is robust and future‑proof:

• Incremental loads: Don’t pull the entire catalog every night unless you must. Use a reliable change-tracking mechanism (like a last-modified timestamp or a change data capture approach) to fetch only the delta. This minimizes bandwidth, reduces processing time, and lowers the chance of reprocessing identical records.

• Clear data contracts: Define exactly what fields are synchronized, the source-of-truth hierarchy, and how conflicts are resolved. This reduces surprises when downstream systems read the data.

• Data normalization: Product attributes can be messy—names, units, categories, and SKUs often come from different systems. Apply consistent naming, unit standardization, and taxonomy alignment so downstream consumers get clean, usable data.

• Error handling and retries: Build idempotent loads with deterministic retry policies. Log, alert, and surface failures so teams can fix root causes quickly, without duplicating work.

• Scheduling and orchestration: Choose a reliable scheduler or workflow orchestrator that can handle dependencies, parallelism, and retry logic. Include a fallback plan for maintenance windows and unexpected outages.

• Observability: Instrument end-to-end visibility from ERP to all downstream targets. Key metrics include data volume, delta size, processing time, error rate, and reconciliation status. Dashboards with these signals help catch subtle data drifts before they ripple outward.

• Data quality gates: Before you push updates downstream, validate critical fields (pricing, availability, categories, and status). If failures occur, quarantine affected records and notify the right teams.

• Change management: Communicate catalog schema changes to downstream teams and adjust mappings in a controlled way. Keep versioning in mind so legacy consumers aren’t blindsided by updates.

Where real-time can fit in without derailing governance

If you do decide to explore real-time patterns, you don’t have to commit to a full-blown stream-first architecture. A pragmatic approach is to implement real-time triggers for high-priority changes while keeping the bulk updates batched at night. For example:

• Real-time events for price changes and stock status, with eventual reconciliation for non-price attributes.

• A streaming feed that carries only a minimal set of fields essential for immediate storefront updates, while more detailed product records refresh on a nightly cycle.

• An outbound message channel for critical changes to a subset of downstream systems that need instantaneous alerts, paired with batch loads for the rest.

This hybrid approach can offer a smoother transition from batch to real-time, letting teams test the waters, measure impact, and adjust governance as you go.

Real-world tangents that matter

A few common patterns and pitfalls crop up in the field, and knowing them helps you stay steady when things get busy.

• Time zones and business calendars: Nightly ETL windows must respect local time zones and warehouse cutoffs. A misalignment here can produce stale data at the moment of high activity.

• Data drift: Over time, ERP data models evolve. Build in versioning and contract renegotiation with downstream systems so you don’t chase broken mappings.

• Duplicate records and reprocessing: If a failed night run reprocesses data, you’ll want clean deduplication logic. Otherwise, you risk duplicating products, prices, or attributes.

• Tooling choices: Popular ETL platforms—Informatica, Talend, Apache NiFi, and cloud-native options like Azure Data Factory or AWS Glue—each have strengths. Pick the one that fits your team’s skills, governance needs, and the complexity you’re willing to manage.

• Vendor quirks: ERP ecosystems differ. SAP, Oracle, NetSuite, and others expose data in varied formats and with different reliability guarantees. Build a mapping layer that can absorb those quirks without breaking downstream consumers.

Bringing it back to the bigger picture

In the world of integration architecture, there’s a healthy tension between speed and reliability. Real-time sounds exciting, but it’s not always the most economical or the simplest path to data integrity across a multi-system landscape. A scheduled ETL job that runs nightly often provides a solid baseline—steady, auditable, and easy to govern—while still offering room to layer in real-time updates where business needs are truly urgent.

If you’re part of a team designing product data flows, start with a well-defined nightly sync, then build a measured plan for incremental enhancements. Use a few practical questions to guide decisions:

• What is the acceptable lag between ERP changes and downstream visibility?

• Which updates are mission-critical to reflect immediately, and which can wait until the next batch?

• How will you detect and correct data drift before it becomes a user-visible issue?

A thoughtful, data-driven approach helps you strike the right balance. And the best part? You’ll be designing systems that can grow with the business, not just keep pace with today’s needs.

Bottom line

For managing product updates from ERP, the nightly ETL approach often delivers a dependable mix of simplicity and reliability. It gives you clean data, clear governance, and manageable risk. Real-time methods are valuable when speed is non-negotiable, but they bring extra complexity that isn’t always necessary. By starting with a solid nightly load and keeping an eye on where real-time could add real value, you’ll build a catalog ecosystem that remains accurate, trustworthy, and responsive to changing business demands. And that’s the kind of architecture that teams can stand behind—quietly confident, even when the catalog changes overnight.