Inside the Web
Built with Atlas™, Databricks, Azure, Snowflake, Microsoft Fabric, Delta Lake, PySpark, MLflow
18+ years of data engineering means we've seen what works and what doesn't. Here's how we approach engagements—and a look at the kinds of problems we've solved across federal, healthcare, finance, and enterprise organizations.
We start by listening. What's broken? What does success look like? We dig into your current state, constraints, and goals before proposing solutions.
Architecture first. We define the end-state data model, platform choices, and governance patterns before writing code—so we're building toward something, not just reacting.
We deliver working pipelines and reports in iterations—not a big-bang go-live. You see progress, validate assumptions, and course-correct early.
We document, train, and transfer knowledge. When we leave, your team owns it—no vendor lock-in, no mystery code, no "call Spider to fix it."
A federal defense agency needed to modernize legacy financial data processing, transforming SAP-backed ERP extracts into governed analytics layers while maintaining audit-ready accuracy across complex financial domains.
Delivered: Unity Catalog–governed Delta Lake pipelines processing $50M+/month in transactions. Implemented Bronze/Silver/Gold medallion architecture with reconciliation logic, match/merge rules, and Power BI dashboards for executive reporting and audit readiness.
Download full case studyA financial services firm had a stalled legacy reporting ecosystem with fragmented SQL, Salesforce, and operational data spread across disconnected systems—causing trust and performance issues across the organization.
Delivered: Databricks + Azure Lakehouse architecture with governed Medallion pipeline. Built secure Gold-layer compensation and financial models consumed by Power BI, with schema redesign, match/merge strategies, and Delta Lake optimization.
Download full case studyNY Health needed to modernize SAP and legacy reporting workloads into a governed Azure Lakehouse architecture while establishing repeatable deployment patterns across development and production environments.
Delivered: Databricks environment setup using the Databricks CLI, workspace configuration, authentication patterns, dbx-based workflow packaging and deployment, and architecture alignment across ADF, dbt, Snowflake, AWS, and Databricks.
Download full case studyLithia Motors needed a scalable streaming foundation for Driver Connect telematics data, including vehicle location, diagnostics, event activity, and operational metrics across the Driver Connect platform.
Delivered: Databricks Structured Streaming pipelines with Autoloader-style incremental processing, checkpointing, schema enforcement, and fault-tolerant Silver/Gold tables for fleet analytics and operational monitoring.
Download full case studyBaxter International undertook an enterprise migration of its global patent and trademark portfolios from Patricia into Anaqua, requiring algorithmic family resolution across dozens of jurisdictions while satisfying Anaqua's single-invention-per-case data model.
Delivered: Multi-stage resolution pipeline combining hierarchical relationship mining against Patricia, shared-priority graph construction, and blocking-driven similarity scoring for canonical cluster assignment aligned to Anaqua's Case model. Parallel trademark architecture handling mark normalization, Nice classification alignment, and owner harmonization across Baxter's corporate-entity history.
Download full case studyOrmat Technologies had years of operational data spread across legacy SQL Server systems, hundreds of Excel files, and manually maintained workbooks supporting drilling performance, maintenance planning, equipment lifecycle, and vendor procurement decisions.
Delivered: Cloud modernization from legacy SQL Server and Excel-driven processes into an Azure-based governed analytics foundation. Historical data organized into governed structures separating raw preservation from analytics-ready outputs, with the platform prepared for predictive analytics including remaining-useful-life modeling for drilling equipment.
Download full case studyBuilt with Atlas™, Databricks, Azure, Snowflake, Microsoft Fabric, Delta Lake, PySpark, MLflow
Whether it's legacy migration, real-time streaming, lakehouse architecture, or getting your data AI-ready—we've probably solved something like it before. Let's talk about what you're working on.
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