New Math Data designs, builds, and optimizes high-performance data architectures and scalable data pipelines to process large volumes of data, fast.
Dashboards, AI models, product innovation, and real-time ops can’t run on stale CSVs and siloed systems. They thrive on engineered data that’s clean, governed, and delivered at cloud speed, and that’s exactly what we deliver.
Common data engineering use cases we work on include:
Ultra-low-latency pipelines ingest card swipes, blockchain events, and KYC documents into a consolidated lakehouse. Real-time anomaly detection flags potential fraud before settlement, while automated AML/KYC workflows reduce the need for manual review. Compliance teams can trace every data point back to source, satisfying auditors without overtime.
High-throughput ETL streams SCADA feeds, smart-meter telemetry, and weather data into a geospatial lake. Operators receive minute-by-minute load forecasts and DER insights that reduce balancing costs and shorten grid planning studies. Modernized meter-data infrastructure slashes query times from hours to seconds and supports new EV-load products.
HIPAA-ready pipelines convert free-text clinician notes into structured codes, join them with imaging and device data, and surface a single source of truth for predictive models. Hospitals reduce readmissions, researchers utilize genomics at cloud scale, and automated lineage meets FDA, GDPR, and PHI audits without manual spreadsheets.
Serverless ingestion pipes LMS clicks, video transcripts, and financial-aid data into a unified student-360 lakehouse. Nightly attrition-risk scores refresh dashboards, instructors see live engagement metrics, and learners jump straight to key lecture moments—boosting retention and content reuse.
From first audit to autoscaling production, our team can help with:
We blueprint and build lakehouse platforms on AWS that separate storage from compute, enforce fine-grained security, and autoscale with demand, so throughput grows without forklift upgrades.
Policy-driven rules, automated tests, and column-level lineage keep data trustworthy. Dashboards surface SLA breaches, while approvals and versioning satisfy regulatory scrutiny.
Glue crawlers, open-metadata frameworks, and automated classifiers discover, tag, and profile new sources the moment they land. Self-service portals let analysts find and trust data fast.
Using scalable patterns (Fivetran, dbt, Spark, or AWS Glue), we build transformation logic that’s modular, testable, and CI/CD-ready—turning brittle scripts into maintainable code.
Kafka, Kinesis, and AWS DMS pipelines move data reliably across hybrid footprints. Exactly-once semantics and schema evolution keep downstream jobs humming through change.
Flink- and Spark-Streaming jobs enrich, window, and alert on events in flight, powering fraud interdiction, grid switching, or patient-vitals monitoring where milliseconds count.
We benchmark query patterns, refine partitioning and Z-ordering, and set autoscaling policies that trim latency while cutting waste, often dropping compute spend 20%+.
Tiered storage, lifecycle rules, and intelligent compression balance performance with budget. Cold data remains queryable, while hot data remains blazing fast.
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