Modernize Without the Big Switch: Data Streaming as the Continuity Layer for Cloud Migration

How secured finance teams migrate from on-prem to cloud without breaking integrations, reporting, or mission-critical workflows.

Co-authored by Solifi and Tamarack

Cloud migration isn’t hard because of infrastructure. It’s hard because of continuity.

Introduction: The real risk in cloud migration is not compute, it’s continuity

Cloud migration is no longer a question of ”if.” In secured finance, it is a question of ”how safely.”

The hard part is not spinning up cloud infrastructure. The hard part is maintaining continuity while you modernize. Because in secured finance, a platform is never “just” a platform. It is the heartbeat of downstream operational and financial processes:

• GL feeds and finance reconciliations
• servicing and collections workflows
• treasury, funding, and cash application
• regulatory reporting and audit trails
• partner integrations across the ecosystem

When those processes lose their data heartbeat, the business becomes operationally blind. And that is why cloud cutovers can feel like a cliff.

This post lays out a practical migration pattern that turns that cliff into a ramp: data streaming as the continuity layer that keeps integrations and reporting stable while you modernize at a safe pace.

At the end, we’ll also preview what comes next. Because once you have reliable data streaming in place, it stops being “just a migration tool.” It becomes the foundation for visibility, automation, and AI-driven decisioning across lines of business and asset classes. That is the focus of Blog 2 in this miniseries.

Why batch extracts break in modern secured finance

Most organizations have lived with batch for decades. Nightly extracts. Scheduled exports. ETL jobs chained together with careful dependencies and tribal knowledge.

Batch can work when the business can tolerate latency and when change is rare.

Secured finance does not operate that way anymore.

Batch creates a “time gap” the business can’t afford

Here’s the simplest illustration. Consider payment activity.

With a nightly extract model, you typically capture a snapshot: “state of the table” at a point in time. Everything that happens during the day remains invisible until the next run.

The problem is not merely that reporting is delayed. The real issue is that decisions and workflows are delayed:

• collections actions that should have been prioritized remain stale
• servicing teams triage exceptions with outdated context
• finance teams reconcile after the fact rather than with current events
• customers receive slower updates and less proactive service

When your business runs on events (payments, delinquencies, renewals, status changes), your integration strategy must be event-capable too.

The Tamarack Perspective: What batch pain looks like in the real world

When equipment finance companies rely on nightly batch extracts for reporting, every dashboard and KPI reflects yesterday’s activity, not today’s. That 24-hour lag limits real-time visibility into originations, funding volume, portfolio exposure, and delinquency trends. Managers can’t accurately assess same-day performance, leadership can’t see shifting concentration risk as it happens, and teams may react to performance gaps that have already been resolved. In a high-value, transaction-driven environment, delayed insight directly impacts responsiveness.

Batch-driven reporting also creates friction in reconciliation and issue detection. Funding activity, payoffs, or pricing anomalies processed during the day won’t surface in reports until the next morning, often requiring manual tracking in the interim. Errors or systemic issues can compound across multiple contracts before they’re visible in exception reporting. Even when operations move in real time, decision-making remains anchored to historical snapshots—reducing agility in a competitive equipment finance market.

Batch is fragile during upgrades and platform change

There is a second failure mode that becomes acute during modernization: batch is brittle when platforms evolve.

When you change environments (on-prem to cloud), upgrade platforms, or modernize workflows, batch jobs and downstream reports are often the first casualties:

• schema changes break extracts or reports
• small shifts in data shape require expensive rework
• validation becomes a multi-team scramble
• “temporary” workarounds become permanent

This is not because teams are incompetent. It is because batch integrations were never designed to be resilient under continual change.

Modernization demands a different interface model.

The Tamarack Perspective: Upgrades and downstream breakage

From an implementation perspective, reporting failures during upgrades and migrations typically stem from unclear priorities and uncontrolled scope. When organizations don’t explicitly define which reports are mission-critical and who depends on them, batch extracts and downstream reporting are often the first to break. Too often, teams attempt to migrate everything rather than prioritizing what is essential: corporate and management reporting, reconciliations, customer/vendor documents, application interfaces, and audit needs. Success depends on clearly defining who needs the data, in what format, and focusing first on what is required to run the business today.

The Most Common Real-World Issues Customers Experience:

• Unclear reporting priorities at project start
• Undocumented report dependencies and downstream consumers
• Scope expansion mid-migration (“bring everything over”)
• Data ownership ambiguity across teams
• Format mismatches for BI tools, exports, or interfaces
• Reconciliation reports overlooked until late-stage validation
• Temporary workarounds becoming permanent solutions

What is Solifi Data Streaming, in plain language

Solifi Data Streaming is designed for one job: reliable, real-time access to your Open Finance Platform data outside the platform, so you can keep downstream workflows running while you modernize.

Publish/subscribe, done for secured finance data

At a conceptual level, it is a publish/subscribe model:

• Solifi publishes changes as messages to defined topics (aligned to business objects and tables).
• Customers subscribe to the topics they need and consume those messages into their target systems.

Under the hood, Solifi Data Streaming leverages Kafka and managed streaming infrastructure, with structured schemas to support governance and consistency.

The important part is not the buzzwords. The important part is what this unlocks architecturally:

• You stop relying on periodic snapshots.
• You start consuming current operational truth as it changes.
• You gain an interface model that is more resilient as systems evolve.

The “who owns what” model reduces ambiguity

One reason integrations fail during migration is unclear responsibility boundaries. Data streaming makes those boundaries explicit:

• Solifi operates the platform-side publishing, the topics, and the streaming service foundation.
• Customers choose how they consume: what topics, what landing destination, what transformation, what monitoring and governance in their environment.

This clarity matters because it allows teams to modernize without confusion during critical phases of migration.

Streaming is not a report export. It is a system interface.

The migration pattern that de-risks everything: dual-run without double work

The best secured finance migrations are rarely “big bang.” They are phased. Controlled. Designed to preserve operational stability.

Data streaming enables a pragmatic, low-risk pattern:

Coexistence architecture during phased migration

As OFP shifts to cloud, the surrounding ecosystem rarely shifts all at once. Some systems stay on-prem for a time:

• ERP and GL integrations
• legacy reporting platforms
• regional compliance workflows
• risk tools or data platforms

Other tools remain third-party:

• credit, fraud, and payments providers
• customer communications platforms
• servicing and document ecosystems

Data streaming becomes the real-time spine that keeps this hybrid state stable. It ensures that, as you modernize one component at a time, data continues to flow to the processes that depend on it.

Replace point-to-point integrations with a hub-and-spoke flow

During migration, point-to-point integrations are a tax. Every connection is a validation surface. Every change is a regression risk.

Streaming provides an opportunity to shift from a brittle web of interfaces to a cleaner hub-and-spoke approach:

• Publish once.
• Subscribe where needed.
• Route downstream with predictable patterns.

This is not just a technical improvement. It is a governance improvement. It reduces the blast radius of change.

The Tamarack Perspective: Integration architecture guidance

Tamarack recommends starting migration with disciplined scope definition and integration simplification, not wholesale redesign.

The first step is identifying who is consuming the data and how it should be delivered:

• should it be accessed on demand via API
• delivered as a structured file (e.g., CSV)
• or surfaced through a BI tool such as Power BI that links standardized tables and topics?

Clients should prioritize enabling a small number of high-impact workflows first. These are typically core operational reporting, reconciliations, and one downstream integration. In this way, the publish-once, subscribe-many model is proven before broader expansion. Where appropriate, streaming data should empower business users to create their own filtered dashboards rather than recreating dozens of static legacy reports.

Each department should review its current reporting landscape during migration. Often, multiple reports can be consolidated into a single dynamic report, some legacy outputs are no longer needed due to improved platform functionality, and certain needs are better addressed through configurable worklists rather than formal reporting. Teams should also document pain points and future-state wish list items. They should clearly separate those from the current phase scope.

The most common mistake organizations make is trying to “boil the ocean” by redesigning every workflow at once. Expanding scope mid-project risks loss of focus and budget overruns. It is important to stabilize the first one to three critical integration patterns and prove success, then scale from there. A phased, prioritized approach reduces regression risk and establishes a scalable hub-and-spoke foundation before layering on additional complexity.

Reference architectures: choose the model that matches your reality

There is no single “right” destination architecture. There is a right architecture for your operating constraints, timeline, and risk posture.

Here are three common patterns we see work well.

Pattern A: Stream into a cloud data warehouse or data lake

Best for modernization and analytics readiness.

Flow: OFP → topics → customer consumer → cloud landing → BI and downstream apps

This pattern supports migration continuity and sets you up for cross-portfolio reporting later. It is often the fastest path to “current-state” dashboards and governed analytics.

Pattern B: Stream into an on-prem reporting database

Best for legacy continuity while the broader environment transitions.

Flow: OFP → topics → customer consumer → on-prem SQL target

This preserves existing BI tools and reporting processes without forcing everything to modernize at once. It is especially useful where regulatory reporting pipelines must remain stable during transition.

Pattern C: Stream into operational workflows

Best for real-time decisioning and automated actions.

Flow: OFP → topics → consumer → workflow/orchestration layer

Here, the power is not only visibility. It is responsiveness. For example:

• delinquency events trigger proactive collections prioritization
• renewal windows trigger task creation and outreach sequences
• missed payment patterns trigger exception workflows

Implementation, practically: start small, scale fast

The fastest way to make data streaming successful is to avoid making it theoretical.

Treat it like any critical platform capability: implement it in controlled scope, prove reliability, then expand.

A practical rollout sequence

1. Pick one, high-impact, workflow that is currently constrained by batch latency payments, proposal status, delinquency events, servicing updates, or similar.
2. Subscribe to a starter set of topics tied directly to that workflow
3. Run baseline validation to ensure correctness and completeness
4. Operationalize monitoring and schema-change handling
5. Expand topic coverage by product, region, and line of business over time

This approach does two things at once:

• It reduces migration risk immediately by stabilizing a mission-critical integration.
• It builds muscle memory and operational confidence in streaming as an interface pattern.

The Tamarack Perspective: Start small, scale fast playbook

Establishing a consumer and loading the initial set of topics into the database is typically  completed within a two-week sprint. From there, custom queries and reports are  developed according to the client’s timeline and priorities. Tamarack validates that record counts within each topic align with Solifi reference counts. Tamarack’s proven  processes perform spot checks by cross-referencing data directly within the Solifi user interface to ensure accuracy and completeness.

Consumption options: DIY consumer vs partner acceleration

Solifi does not force a single consumption method. Many customers already have preferred data platforms and integration tooling.

That said, speed matters. Especially during migration.

This is where implementation partners can reduce time to value, not by replacing your architecture, but by accelerating the build and hardening the operational model.

The Tamarack Perspective: How Tamarack accelerates consumption

Data consumers stream into a staging database, where productized views transform raw topics into reporting-layer structures. Standard reporting views feed semantic models on a defined schedule and are exposed through the reporting layer, enabling rapid deployment with minimal customization to align with a client’s business practices. Where needed, real-time access to raw tables can also be enabled, with structured queries supporting time-sensitive reporting. Tamarack enhances usability by parsing multi-value XML fields into additional relational tables, making the data more accessible and query-friendly. Stream integrity is continuously monitored with alerting thresholds set at 10 minutes to proactively detect and resolve issues.

All reports are validated through both per-record UI spot checks and reconciliation against aggregate portfolio metrics to ensure accuracy and completeness.

Proof by example: migration continuity in the real world

One of the most credible signals of value is what happens when a customer shifts from “platform reports” to a true BI foundation.

A practical example of migration continuity in action can be seen in Stonebriar Commercial Finance, now part of Eldridge Capital Management. The organization moved from operational-system reports to a dedicated BI foundation powered by Solifi Data Streaming. Using a publish and subscribe model with Change Data Capture, Stonebriar implemented a consumer and Azure-based landing environment, starting with a single high-value table and expanding to more than 90 InfoLease topics flowing into its BI layer.

This phased approach preserved reporting continuity during modernization. Instead of relying on custom reports or nightly extracts, the team gained near real-time visibility, enabled ad hoc querying, and reduced manual reporting effort. Over time, streaming became a resilient interface that supports ongoing platform evolution without disrupting downstream workflows.

Partner POV: why Solifi + Tamarack for migration continuity

Migration success is rarely about a single product feature. It is the combination of:

• a strong platform capability that provides continuity and control, and
• a delivery partner that knows how to make it real inside messy enterprise ecosystems

Solifi Data Streaming provides the continuity layer. Tamarack brings implementation expertise and a practical delivery approach for secured finance environments.

The Tamarack Perspective:

In the market, we consistently see migration projects struggle not because of technology limitations, but because of unclear scope, weak requirements definition, and insufficient governance. Projects often begin without a shared definition of success, which leads to scope creep, budget overruns, and misaligned expectations. Testing is frequently compressed to the end of the timeline rather than built into the project from the start, increasing regression risk. Data quality is another common failure point. Organizations attempt to migrate years of inconsistent or unnecessary data, adding complexity and avoidable downstream issues. A lack of defined data ownership and overextended internal resources further compounds risk.

Tamarack de-risks migration by enforcing disciplined scope definition, aligning requirements to measurable outcomes, and developing test plans early and directly tied to those requirements. We encourage clients to use migration as an opportunity to cleanse and rationalize data, moving only what is accurate and necessary. We also help establish clear data stewardship models and ensure dedicated project focus from key stakeholders. With deep experience in secured finance environments, Tamarack brings a practical, structured delivery approach that improves predictability, protects timelines, and drives successful outcomes.

The continuity layer that makes modernization safe

Secured finance teams do not modernize for novelty. They modernize to compete, to reduce operational drag, and to create the visibility required for better decisions.

But modernization must be safely delivered.

Data streaming is how you:

• modernize without operational interruption
• reduce upgrade and change breakage risk
• support hybrid environments while you sequence the roadmap

And importantly, it is also how you lay the foundation for what comes next.

In the next blog in this series, we’ll shift from continuity to opportunity: how streaming becomes the enterprise data spine for cross-LOB visibility, event-driven automation, and AI copilots that can identify risk and growth patterns earlier than humans can.