Nightly ETL batches reduce complexity when integrating newly acquired systems.

When a company folds a new acquisition into its tech landscape, complexity often shows up like uninvited guests at a party. You anticipate integration headaches, but the question isn’t whether they’ll appear—it's how you handle them. The most pragmatic move for a technical architect looking to minimize chaos is to build all integrations as nightly ETL batches. Yes, nightly. Not real-time. Not “as-needed.” Nightly.

Let me explain why that cadence tends to reduce the mess that acquisitions bring and how you can make it work without turning your system into a sluggish beast.

Why batch processing trumps real-time in an acquisition scenario

When you’re integrating multiple, diverse systems—ERP, CRM, procurement platforms, and a host of ancillary apps—the landscape is jagged. Each system has its own data models, latency, uptime, and data quality quirks. Piling real-time integrations on top of that is like trying to drive a convoy through a city where roadwork pops up everywhere. It creates dependencies, escalates risk, and makes troubleshooting feel like detective work.

Nightly ETL batches offer a calmer rhythm. They load a defined slice of data every night, after data governance checks and validation. The benefits tend to stack up quickly:

• Predictable resource use: Batch windows let you schedule heavy data movement for off-peak hours, when servers are more available and user activity is lighter. This reduces contention and gridlock across systems.

• Clear fault boundaries: If a load fails, you know exactly when it happened and which data set was involved. Rollbacks, reprocesses, and root-cause analysis become more straightforward.

• Stronger data quality gates: You can perform cleansing, de-duplication, and validation in a controlled phase before data touches the core applications.

• Easier testing and rollback: You can simulate nightly loads in a sandbox, verify outcomes, and revert to a known good state if something goes wrong.

• Reduced coupling: With a batch flow, you avoid tight, continuous interdependencies between the new apps and your core platforms. That means you can evolve one side without destabilizing the other.

In short, you buy time to understand the acquired environment. You also buy resilience, which is no small thing when you’re stitching together several unfamiliar systems.

Pros and cons in plain terms

No sugarcoating: nightly ETL isn’t a universal fix. There are trade-offs, and a few caveats deserve attention.

• Latency: Data isn’t instantly available to users or downstream processes. If your business needs up-to-the-minute visibility, you’ll need a separate channel for critical events or a near-real-time path for a subset of data.

• Data freshness vs. stability: The batched approach favors stability and predictability over speed. If the acquisition involves fast-moving data like inventory changes in a manufacturing floor, you’ll want backup mechanisms.

• Complexity of pipelines: Paradoxically, you need to design robust batch pipelines and governance around them. That means metadata, versioning, and clear error-handling strategies.

That said, the gains in simplicity and reliability often outweigh the latency cost for the early days of an acquisition. The goal isn’t perfection in week one; it’s a sane, scalable start that buys you time to mature the architecture.

A closer look at the wrong turns you want to avoid

To understand why nightly batches are a smart baseline, it helps to see the pitfalls of the other options in the original list.

• Custom links for order status: It sounds user-friendly—redirect people to the ERP to see order status. In practice, it fragments the user experience. Different authentication schemes, inconsistent data views, and a patchwork of UI behaviors create confusion. If a user can’t find what they need in one place, trust erodes and support calls rise.

• Apex callouts to acquired apps: Direct calls from Salesforce to every acquired system couple Salesforce tightly to those apps. If one system is intermittently unavailable, you’re dragging that risk into your primary platform. Latency spikes become user-visible delays, and error handling multiplies across endpoints.

• An ESB to abstract Salesforce integration: An enterprise service bus can be valuable, but it’s another moving part. It introduces governance, maintenance, and a new set of failure points. If the ESB isn’t carefully designed, you end up trading one set of complexities for another.

Nightly ETL, in contrast, is a clean break from those pitfalls: a defined batch path with clear ownership, easier troubleshooting, and fewer surprise failure modes.

What to put in place when you choose nightly ETL

If you’re serious about making nightly batches work, here’s a practical blueprint you can adapt.

• Start with a canonical data model: Identify the core entities that matter across systems (for example, customers, orders, products, and invoices). Map each system’s data to this single model so you’re not re-inventing schemas every time data crosses an boundary.

• Layer your architecture: Create staging, integration, and core GW (gateway) layers. The staging area captures raw extracts; the integration layer handles transformations; the core layer publishes clean data to the primary apps. Keeping these layers well separated simplifies testing and future changes.

• Incremental loads and idempotence: Design loads so that re-running a batch won’t duplicate data or corrupt state. Use natural keys, upserts, and robust deduplication. Idempotence pays off when reconciling messy acquisitions.

• Validation gates: Build checks at multiple points—data type integrity, referential integrity, and business rules. Failures should surface clearly so engineers can triage quickly.

• Error handling and retry logic: Implement clear retry policies and alerting. If a batch fails, you want actionable alerts, not a flood of noise. Include automatic rollback mechanisms for partially completed loads.

• Monitoring dashboards: Track data volume, success/failure counts, latency, and pipeline health. Make the dashboards accessible to both the data team and the business side, so stakeholders can see progress without hunting for spreadsheets.

• Security and access control: Ensure data at rest and in transit complies with your organization’s security posture. Use role-based access and encryption where it matters most. Don’t forget audit trails for compliance needs.

• Performance tuning: Schedule batches during windows with the least impact on production workloads. If data volumes rise or volumes spike after a merger, you can re-balance the schedule without tearing the system apart.

• Tooling choices: Leverage mature ETL/ELT tools (Informatica, Talend, Apache NiFi, Microsoft SSIS, or vendor-specific solutions) that fit your tech stack. If you’re in a Salesforce-heavy environment, consider data integration platforms that play well with Salesforce and ERP systems. The key is choosing tools that you can support long term, not just what looks sleek on a slide.

• Change management: Acquisitions bring new business processes. Keep your data model adaptable and document changes. Communicate early and often with the teams who will rely on the integrated data.

A pragmatic, human-friendly way to talk about the work

Think of batch processing like meal-prepping for the week. You chop vegetables, you marinate protein, you portion meals. It’s not sleek, but it’s reliable. If a dinner rush hits, you don’t scramble to improvise; you reach into a ready-to-go bag. Nightly ETL does the same for data. It’s not flashy, but it keeps the lights on.

As you design, you’ll encounter moments when you want to chase perfection. You’ll spot data that would look nicer if you stitched things together in real time. It’s normal to pause and ask: what’s at stake here? Is the extra speed worth the added risk and complexity? In most new acquisitions, the sensible answer is no—until the new systems prove themselves and you have the bandwidth to invest in a more advanced architecture.

The value of a measured stance

A calm, deliberate approach to integration yields long-term benefits. You gain:

• Predictable timelines for deployment and upgrades.

• Easier onboarding for new teams and new systems.

• Clear ownership for data quality and governance.

• A foundation you can evolve without tearing down the core environment.

Beyond the technical, there’s a human element to this decision. People will be juggling deadlines, vendor negotiations, and internal politics. A batch-based strategy respects people’s need for clarity. It reduces the number of moving parts that can trip you up when speed and pressure rise.

A little nuance that often helps in real life

Sometimes, you’ll encounter data that truly needs timely availability—for example, critical inventory counts or customer service flags. For these situations, consider a hybrid approach. You can maintain the main nightly pipeline for the bulk of data while carving out a separate, lighter-weight path for high-priority data. This lets you preserve the gains in simplicity while still accommodating essential real-time needs. The trick is to formalize the split so it’s maintainable and auditable, not a cloak-and-dagger sideshow.

Wrapping it up

When a technical architect faces the challenge of bringing a new acquisition into harmony, the simplest, most resilient starting point is often the quiet one: batch processing. Building all integrations as nightly ETL batches reduces the immediate complexity, provides a clear roadmap for data governance, and gives the team room to grow the architecture without piling on fragility.

If you’re mapping out the approach, start with a solid canonical data model, establish a clean multi-layer architecture, and invest in robust validation and monitoring. Keep a watchful eye on latency and the potential need for a targeted real-time channel for truly critical data. And remember that a well-constructed batch pipeline isn’t a surrender to slowness—it’s a strategic choice for stability, clarity, and long-term momentum.

In the end, the right architecture isn’t about making everything instant; it’s about making things intelligible. When you can see how data moves, where it comes from, and how it’s transformed, you’ve already lowered the temperature of the integration heat. That clarity matters more than any single data point crossing systems at a hair’s breadth. It’s what lets startups, scale-ups, and enterprises alike keep moving forward—calm, calculated, and capable.