Data Platform Architecture
Data platforms engineered around your data, not a reference architecture. We will design a data warehouse, a data lakehouse, or a hybrid solution, depending on your needs. Query patterns, data governance, and regulatory requirements are also factored into the design.
Ingestion and Data Pipelines
Batch and streaming pipelines that stitch together data from applications, SaaS, databases, files, and third-party APIs. We build in monitoring to keep pipelines running and data fresh.
Data Modeling and Semantic Layer
Dimensional models and semantic layers that standardize definitions of metrics, dimensions, and segments so that they can appear consistently across dashboards, notebooks, reports, and AI applications.
Data Quality
Design and implementation of automated data freshness, volume, schema, and logic tests so that downstream systems are not polluted with bad data.
Business Intelligence
BI dashboards built around decisions, not deliverables. Governance and smart defaults baked in so that the decisions that matter to your business are actually happening.
Real-Time Analytics
Streaming pipelines that power use cases that require seconds instead of hours or days. Fraud detection, operational analytics, personalization engines, and similar use cases all require fresh data to function.
Data Governance
Implementation of data lineage, cataloging, access controls, retention policies, PII identification and masking, and other governance controls at the data layer. These controls secure your data but also enable enterprise AI initiatives by establishing an audit trail.
