LINEAR ANALYTICS GROUP

Your Enterprise. Integrated.

Principal-level integration architecture connecting Microsoft Dynamics, Azure, and legacy data ecosystems. Precise, secure, and built for high-impact enterprise solutions.

Core Technical Capabilities100% IDEMPOTENT

Cloud Compute Optimization

Refactor cloud data flows into centralized pipelines to reduce execution overhead and compute costs.

Parity Across Environments

Maintain consistent database and configuration state, eliminating environment drift and runtime errors while minimizing technical debt.

Dynamic Security Delivery

Dynamically inject credentials, certificates, and secrets into pipelines at runtime, eliminating hard-coded key exposure and security risks.

Resilient Data Flows

Architect idempotent integration middleware that adaptively scales to meet fluctuating demand.

Python 3.13OData v4pac CLI

inventory-sync-engine / dataverse_sync_runner.py

MYPY STRICT: 100% PASS

Cloud Compute Optimization

Refactor cloud data flows into centralized pipelines to reduce execution overhead and compute costs.

Parity Across Environments

Maintain consistent database and configuration state, eliminating environment drift and runtime errors while minimizing technical debt.

Dynamic Security Delivery

Dynamically inject credentials, certificates, and secrets into pipelines at runtime, eliminating hard-coded key exposure and security risks.

Resilient Data Flows

Architect idempotent integration middleware that adaptively scales to meet fluctuating demand.

Python 3.13OData v4pac CLI

"""Idempotent, thread-pooled Dataverse sync."""
class DataverseSyncRunner(
    InventoryDomainMixin,
    BaseODataSyncRunner,
):

    def __init__(
        self,
        source: RecordSource,
        write_window_size: int = 25,
    ) -> None:
        super().__init__(source, write_window_size)
        self._breaker = ConsecutiveFailureCircuitBreaker(
            failure_threshold=5,
        )

    def upsert(self, record: dict[str, Any]) -> None:
        if self._breaker.is_open():
            return
        key = record["lagsol_ExternalSKUID"]
        self.client.patch(
            f"inventoryset(lagsol_ExternalSKUID='{key}')",
            payload=record,
        )

Enterprise Ecosystem Integration

Build robust, scalable connections between D365, Dataverse, and your core enterprise applications.

Data IntegrationHigh-Throughput PipelinesArchitecture Modernization

Cloud & Compute Optimization

Refine Azure pipelines and database strategies for reduced cost and peak system performance.

Azure Data PipelinesDatabase StrategiesCompute Reduction

Environment Parity & Security

Implement least-privilege access, zero-trust patterns, and consistent environment configurations using ALM and source control to maintain compliance.

Governance & RiskZero-Trust AccessALMCI/CD

15+ Years Industry Experience

The Zero-Translation-Gap Advantage.

Most projects fail from miscommunicated business rules or a mismatch in understanding deliverables. With over 15 years of experience as a Product Owner, Systems Analyst, and Enterprise Architect, our pro-code engineering guarantees your data engines align precisely with enterprise goals.

Business Domain & Requirements Elicitation

Deep expertise uncovering business drivers, drafting clear technical specs, and mapping complex workflows without middle-tier translators.

Single Point of Accountability

Complete end-to-end execution. Discover stakeholder requirements, design the solution architecture, and engineer the pro-code integrations.

ROI & Cost Optimization

Engineered for financial efficiency—refactoring data flows, optimizing pipelines, and streamlining API consumption to structurally lower monthly operational costs.

Engage Consultancy Services

Direct contracting & high-ticket advisory engagements