Current IT environments don’t fail because tools can’t integrate. They fail because integrations that once worked fine at a few thousand records now need to keep up with millions of entities, comments, alerts, and updates moving across dozens of systems.

The scale of the problem is global. Globally, more than 400 million terabytes of data are created daily, putting unprecedented strain on digital systems.

That growth doesn’t just hit storage. It hits integration. ITSM tools exchange incidents with DevOps systems. Monitoring tools flood ITOM platforms with events. CRM, CX, and support platforms keep each other in sync. The integration layer becomes the circulatory system of the enterprise - and if it cannot handle the volume, the entire organism slows down.

Why data volume is now the primary integration bottleneck?

For most organizations, the problem is no longer “Can we connect Jira and ServiceNow?” or “Can we push alerts from monitoring into ITSM?”. Those are solved problems. The harder question is:

What happens when we have hundreds of thousands of tickets, millions of alerts, deep comment histories, and continuous updates - and we still expect near real-time sync?

A typical enterprise scenario might look like this:

• A ServiceNow instance with years of incident history, thousands of active tickets, and dozens of custom fields per record.

• Multiple Jira projects with epics, tasks, and bugs, each carrying dense history, comments, and attachments.

• Monitoring tools like Datadog, Zabbix, or Prometheus generating alert storms when something serious goes wrong.

• CRM and CX platforms exchanging customer cases, opportunities, and SLA-related data.

None of these workloads are static. They grow every day. IDC’s connectivity research shows that around 30 percent of enterprises are seeing bandwidth demands increase by more than 50 percent per year, largely due to rising data generation and traffic between applications.

If your integration platform was chosen when volumes were moderate, it might still “work” today - but slowly, unpredictably, and at the cost of painful maintenance. That’s usually the moment teams start seriously evaluating alternatives. To make that evaluation rational instead of hopeful, we need to look at the technical forces that cause integrations to slow down in the first place.

The four main forces that break integrations at scale

From a technical perspective, most large-volume integration failures can be traced back to four forces:

1. API rate limits

2. Payload size and structure

3. Inefficient delta logic

4. Correlation and loop issues

They act together, and they get worse with time.

API rate limits: the hard ceiling on integration throughput

API rate limiting is not just a detail in the docs. It’s the hard ceiling that governs how fast any integration can move data.

Microsoft’s guidance on Microsoft Graph is very explicit: when a throttling threshold is exceeded, further requests are limited for a period, and clients receive HTTP 429 responses with instructions to back off. Salesforce’s documentation is equally clear that API requests can be rate-limited “to protect system performance and availability,” especially under unexpectedly high load.

In practice, it means that: If your integration uses a naive “poll everything frequently” strategy, it will eventually hit API limits and start to slow down or fail, no matter how powerful the integration server is.

To handle large data volumes sustainably, a platform must be rate-limit-aware, adapt its request patterns, and most importantly, avoid unnecessary calls in the first place. That’s where payload and delta strategies come in.

Payload explosion: when individual records become heavy

A record is rarely just a “record” in enterprise systems. Consider a typical Jira issue or ServiceNow incident in a mature environment:

• It may hold dozens of custom fields.

• It may carry a long description plus rich text notes.

• It often has many comments or work notes.

• There may be attachments, tags, watchers, and history logs.

Each of these layers adds weight. A ticket that started as a lightweight JSON document can, over time, grow into a dense, nested structure that’s easily tens or hundreds of kilobytes large. Move one such record occasionally and nobody notices. Move thousands per hour between multiple systems and you suddenly have:

• increased network transfer time,

• more CPU spent on parsing and serialization,

• higher memory pressure inside the integration engine,

• and a much higher chance of hitting API and time limits.

Platforms that treat payload size as an afterthought eventually hit a wall. Platforms that make it easy to trim fields, skip irrelevant histories, and avoid shipping full nested trees every time will survive far better as data grows.

Bad or shallow delta logic: the silent performance killer

If you ask most teams how their integration handles “deltas,” you’ll often hear something like: “We filter on an updated_at field.” That’s a start, but for large data volumes, it’s nowhere near enough.

Robust delta logic needs to answer several questions precisely:

• When did we last successfully collect data from this source?

• Since that time, which records changed?

• Within those records, which nested pieces changed (comments, work notes, child objects)?

• How do we avoid reprocessing the same things repeatedly?

ZigiOps addresses this with Last Time expressions that can be attached not only to top-level entities but also to nested structures such as comments or changelog histories. That means the platform can say “bring me everything that changed since the last successful run” in a very granular way. Without that, platforms end up reloading vast amounts of data just to find the handful of changes that actually matter. At small scales you might get away with it. At hundreds of thousands of records, you won’t.

H3 Correlation and loops: the invisible source of duplication and noise

The last force is more subtle, but equally destructive: correlation.

If your integration can’t consistently map “Jira issue ABC-123” to “ServiceNow incident INC0012345,” it loses track of which record is which. From there, several bad things happen:

• Records get duplicated instead of updated.

• Comments are re-synced in a loop.

• Status changes bounce back and forth.

• The same payload gets posted to an API again and again.

All of those behaviors waste API calls, increase payload volume, and generate operational noise that teams then have to triage manually.

ZigiOps uses correlation fields (for example, mapping a Jira key into a dedicated correlation field in ServiceNow) and keeps tiny runtime files with correlation state to ensure updates remain clean even across restarts and failovers. Combined with integration-user filtering (updates created by the integration itself can be ignored), this dramatically reduces the risk of loops and duplicate churn.

Why architecture matters more than features at high volume?

Many teams are comparing tools primarily on feature checklists: does it support Jira, ServiceNow, Datadog, Salesforce, and any other tools; does it have a UI; can it do mappings, and so on.

But once you factor data volume into the equation, architecture matters more than features. Two tools can both “support ServiceNow ↔ Jira,” yet behave completely differently at 500k incidents. Broadly speaking, you’ll run into three architectural patterns.

Database-centric iPaaS: store-everything, then suffer

The classic iPaaS model is “store-and-forward.” The platform ingests data into its own database, applies transformations, then pushes results out to target systems.

There are benefits: durability, retries, historical tracking, etc. But there is also a predictable performance curve: as the internal database grows, queries slow, indexes need maintenance, and each additional month of data adds a little more friction. Over time, integrations that once ran in minutes begin taking hours. The platform’s performance is now tied to its own data growth, not just the external systems.

Workflow automation engines: good for logic, not for massive sync

Workflow and low-code automation engines are designed for orchestrating business actions, approvals, and logic flows. They typically model “event → trigger → workflow,” which works well for infrequent or moderately frequent events.

However, they struggle when asked to:

• sync huge volumes of tickets or alerts in both directions,

• handle deep nested structures,

• or process constant change streams without backlogs.

Queues get long, worker threads are saturated, and operational complexity climbs quickly. For high-volume data integration, these engines are often pressed into service for a use case they weren’t built to handle.

Stateless, real-time, no-data-storage engines: the ZigiOps model

A stateless integration engine works very differently. Instead of storing large data volumes internally, it acts as a real-time conduit between systems. It keeps only minimal runtime metadata (such as delta timestamps, correlation IDs, and configuration), while all actual business data lives exclusively in the source and target tools.

ZigiOps follows exactly this approach. ZigiOps is a no-code, no-data-storage integration platform: it processes records in real time through the APIs of the connected systems and maintains only tiny runtime files containing the current integration state.  

This has several consequences for performance:

• There is no internal database that grows over time, so the platform does not slowly degrade as history accumulates.

• Vertical scaling (more CPU/RAM) and horizontal scaling (more nodes) are straightforward, because state is small and easy to replicate.  

• High availability becomes practical: a backup ZigiOps server can take over simply by sharing configuration and runtime files, without heavy data replication.  

For organizations expecting their integration workloads to keep growing -— and they will -— this stateless, real-time model is far more sustainable than architectures that accumulate large internal data stores.

Engineering strategies that actually work at scale

With the architectural context in place, let’s get more practical. What can IT architects, DevOps leads, and migration experts actually do to make large-volume integrations performant and robust?

Treat delta logic as a first-class design decision

Delta logic isn’t a checkbox; it’s central to performance design. When you define an integration, you should be answering questions like:

• Which timestamp or field defines that a record has changed in a meaningful way?

• How do we track “last successful run,” not just “last attempted run”?

• Do nested elements like comments, histories, or work notes have their own change markers?

ZigiOps provides built-in Last Time expressions that you can attach to different fields and levels of the source data, making it possible to build very precise “only bring me what changed recently” filters.  

In a high-volume Jira to ServiceNow integration, for example, you might:

• Use the issue updated date for ticket-level deltas.

• Use comment creation timestamps for nested comment deltas.

• Combine both with reporter or sys_created_by filtering to ignore changes made by integration users.

The tighter your delta logic, the fewer unnecessary API calls you make -— and the longer your integration will scale cleanly.

Aggressively optimize payloads - especially for “chatty” entities

Payload optimization is where many teams leave easy wins on the table. Start by asking:

• Which fields are absolutely required for downstream teams to do their job?

• Are we copying long descriptions that no one is reading on the other side?

• Do we really need full change histories, or just the current state plus key dates?

• Are attachments essential, or can we use deep links instead?

ZigiOps’ mapping makes it easy to select only the fields you truly need on the target side, and to transform or conditionally populate them. Over time, shaving 20 to 40 percent off each payload can be the difference between “we’re constantly hitting limits” and “this just runs.”

Split monolithic integrations into multiple logical workflows

A good rule of thumb: if your integration diagram looks like a giant spaghetti ball, performance and troubleshooting will suffer.

A more scalable pattern is to break a large integration into several focused workflows, for example:

• one flow that creates tickets or records,

• a second flow that syncs state and key field changes,

• a third one that moves comments or work notes,

• a fourth (optional) one that handles attachments or rarely used data.

ZigiOps supports multi-operation integrations where each operation has its own trigger, delta logic, and mapping configuration. This lets you run different parts of the integration at different frequencies, tune them individually, and scale them independently as load changes.

Align polling, push, and scheduling with real-world behavior

Frequency is a subtle but powerful lever. Pull too often without strict filters and you hit rate limits. Pull too infrequently and teams end up working with stale data.

In practice, many organizations use a mix of:

• short intervals (e.g., 1–2 minutes) for high-priority incident flows,

• moderate intervals (5–10 minutes) for less critical syncs,

• and scheduled or event-based flows for large bulk or migration jobs.

What matters is that you measure how much data actually changes between cycles and adjust intervals based on reality, not guesswork. ZigiOps logs and diagnostics make it easier to see how many records each operation processes per run, which helps tune both polling and scaling.  

Design for loop prevention and correlation integrity from day one

Loop prevention and correlation are not “extra safety” features. They’re primary controls for performance and correctness.

A reliable pattern looks like this:

• choose a dedicated correlation field on each side (e.g., a custom text field in Jira that stores the ServiceNow number, and a correlation_id field in ServiceNow that stores the Jira key),

• ensure those are populated only by the integration,

• configure ZigiOps to read and write those fields for correlations,

• and configure filters so that updates made by the integration user are not considered “new changes” to be pushed back.

That simple discipline prevents most of the pathological behaviors -— duplicate flooding, comment echoes, and API storms -— that make integrations unstable and undermine user trust.

Use horizontal scaling and HA when volume just keeps climbing

There is a point in every large organization where the question changes from “Can we optimize further?” to “Do we need more nodes?” Because ZigiOps does not store business data and keeps runtime state small, it is designed to scale both vertically (more resources on a single host) and horizontally (multiple ZigiOps instances). The documentation describes how to set up a primary and backup server with synchronized configuration and runtime files, and how to use shared storage on Linux for automatic state sharing.  

This means you can:

• run separate ZigiOps instances for different integration sets,

• prepare hot-standby servers for failover,

• and handle very large volumes by distributing load rather than trying to super-size a single box.

How this plays out in real integration scenarios?

To make this less abstract, it’s helpful to consider how these ideas manifest in real-world use cases.

ITSM ↔ DevOps at enterprise scale

Imagine a global company using ServiceNow for ITSM and Jira for development, with:

• 300,000+ incidents in ServiceNow,

• dozens of active Jira projects,

• multiple deployments each day,

• and teams spread across time zones.

The integration needs to:

• create Jira issues from incidents that meet specific criteria,

• sync status, priority, and assignment changes,

• move comments and work notes,

• and avoid loops, even as dozens of people work on both sides.

A store-and-forward iPaaS will slowly suffocate under the history it accumulates. A workflow engine will struggle to keep up with the constant churn. Stateless tool like ZigiOps can focus on the last few minutes of changes, using delta logic and correlation fields, and keep systems aligned without dragging the weight of years of history behind every operation.  

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Monitoring alerts flowing into ITOM / ITSM

Another archetypal scenario is monitoring and observability. A single incident can generate hundreds or thousands of raw alerts. A critical region outage may unleash a full storm of events.

A recent article on incident management challenges highlighted that integration problems between tools create data silos and hinder having a unified system view -— which, in turn, slows down response.

If your integration naïvely pushes every raw alert into your ITSM tool, you will both overload the target and make life miserable for on-call engineers. A better strategy, which ZigiOps supports, is to:

• filter alerts based on severity or correlation,

• enrich them with context from other tools,

• and sync only meaningful incidents into ITSM, with updates flowing back.  

At scale, that difference in design can mean handling a major outage gracefully -— or drowning in noise.

Data-heavy migration projects

Finally, consider migrations: moving from one ITSM, DevOps, or monitoring platform to another, while business continues as usual. Migrations are “worst case” scenarios for volume because they often involve years of historical data.

Here, stateless processing and Last Time expressions are particularly powerful. You can:

• bulk-sync historical records in phases,

• sync only new or changed data after the initial bulk load,

• keep systems aligned until the cutover moment.

Because ZigiOps doesn’t store that history internally, you’re not paying a permanent performance penalty just because you went through a big migration.

What a “high-volume ready” integration platform really looks like?

When you strip away the marketing gloss, a platform that can genuinely handle large data volumes in 2025 and beyond will have a few defining characteristics:

• It’s stateless with respect to business data (no giant internal databases that slow down over time).

• It’s API-native and built to respect rate limits, backoff headers, and throttling behaviors.  

• It offers powerful, configurable delta mechanisms for both records and nested structures.  

• It lets you trim payloads aggressively and design lean mappings.  

• It encourages clear correlation models and loop prevention patterns from the start.  

• It supports vertical and horizontal scaling, plus high availability, without fragile state sharing.  

That is essentially the design brief of ZigiOps: a 100% no-code integration platform that does not store customer data, offers rich mapping and filtering controls, and is architected around real-time, API-driven data flows that remain performant as volumes grow.  

Next step: see it handle your volume, not just read about it

Reading about integration performance is useful, watching your own workloads run through a stateless engine is better.

If your team is currently:

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• fighting slow, brittle integrations,

• worried about how existing iPaaS solutions will behave as volumes double,

• or preparing for a high-volume migration or monitoring roll-out, then the logical next step is to validate an architecture built for scale against your real systems.

Book a demo of ZigiOps, bring a realistic subset of your data and scenarios, and see how a no-data-storage, real-time integration engine behaves under your actual load profile.

Then you won’t have to guess whether your next 12–24 months of growth will break your integration layer - you’ll know.