Data Engineering
At Radix, Data Engineering powers the data pipeline from source to usable insight. We connect apps, files, APIs, and databases; standardize and de-duplicate data; and run monitored batch/stream pipelines with SLAs and alerts. Layered warehouses/lakes (raw → cleaned → curated) with a simple semantic layer keep metrics consistent across teams. Versioned datasets and secure, audited APIs expose trusted data to BI and ML, while reliability is built in. We provide dependable data services that plug cleanly into your existing production systems.
Connect apps, files, APIs, and databases; standardize schemas; de-duplicate and reconcile reference/master data so analytics and ML are trustworthy.
Monitored, retry-safe pipelines with SLAs; feature pipelines ensure consistent inputs for analytics and ML.
Layered (raw → cleaned → curated) storage on cloud or on-prem; a simple semantic layer so business terms mean the same thing everywhere.
Freshness, lineage, drift, and spend dashboards; CI/CD for data changes with quick rollback.
Versioned data/metric services for BI tools, apps, and model serving; role-based access controls and audit trails.
What you get
- A documented data model and ingestion plan
- Reproducible, monitored pipelines (with alerts)
- Curated tables + semantic layer ready for BI/ML
- Versioned datasets and APIs for downstream use
Representative case snapshots
High-volume fare/ops event processing (15M/day) on Hadoop/MySQL with Hive/SQL and parallel R/Java; powered proactive maintenance insights.
OCR + heading extraction + Solr indexing with an API that returns likely pages for terms—integrated into a PDF reader.
Two-stage Solr + ML architecture with ~87–89% recall on addresses/entities at scale; API deployment for production use.
What we build, how it performs - Explore our work!
Would love to hear your thoughts!
