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Data Engineering Services That Power Analytics ML Business Decisions
Zymr Data Engineering Services build reliable scalable data platforms turning messy sources into trusted analytics ML foundations. Data teams stop firefighting pipelines spend time analyzing insights instead of fixing breaks. We deliver lakehouse architectures streaming processing feature stores observability handling enterprise volume real world complexity.
Data teams waste 80% time on preparation. Pipelines break new sources arrive. Quality issues block ML models. Analytics waits weeks clean datasets. Legacy warehouses cost explode. Stakeholders doubt numbers. Zymr Data Engineering Services solve completely. Modern lakehouse architectures automated pipelines data observability feature engineering let data scientists work faster business trust insights. Production reliability zero surprises enterprise scale guaranteed.
Core Capabilities
Batch and Streaming Ingestion
Data Transformation and Quality Engineering
Feature Engineering for Machine Learning
Analytical Data Platforms
Data Observability
Data engineering services
Healthcare Lakehouse 47 EHR Feeds Unified
Regional health system struggled 47 EHRs siloed analytics claims IoMT SDOH. Zymr built Databricks lakehouse FHIR Spark streaming unified quality measures MIPS HCC RAF. Data latency dropped 72 hours real time. Analytics productivity tripled RAF scores 14% star ratings 3.7 to 4.4 first year revenue 22M recovered.
Project Details →
Retail Streaming 1B Events Daily
E-commerce needed real time personalization 1B events day. Zymr Kafka Flink streaming lakehouse customer 360 recommendations inventory routing. Sub second latency 99.97% Black Friday peaks conversion lift 47% personalization revenue 12M annual.
Project Details →
Financial ML Feature Store
Investment firm needed reusable ML features fraud risk credit scoring. Zymr Feast feature store point in time online offline Spark Flink streaming. Model development cut 60% fraud F1 score 23 points production models 87% AUROC.
Project Details →
Enterprise Data Architecture Expertise
Distributed Compute Platforms
Streaming Architectures
Lakehouse and Modern Data Warehousing
Feature Stores
Open Ecosystems and Technology Partnerships
Lakehouse and Analytics Platforms
Streaming Frameworks
Data Transformation Tools
Feature Store Technologies
Data Observability Solutions
Data Observability Solutions
Cloud Native Implementations Across Hyperscalers
AWS Data Engineering
Azure Data Engineering
GCP Data Engineering
Our Engineering Approach
Discovery Phase
Architecture Phase
Implementation Phase
Optimization Phase
Why Zymr for Data Engineering
Production Scale Delivery
Cloud Native Expertise
ML Ready Engineering
Data Observability Leadership
Data engineering services FAQs
What differentiates your data engineering
Production scale pipelines observability first ML ready feature engineering enterprise governance go beyond basic ETL scripts. We deliver petabyte pipelines billions events daily 99.97% uptime Fortune 500 healthcare financial retail. Automated data quality freshness lineage impact analysis saves data teams 80% preparation time accelerates ML model development stakeholder trust insights completely.
Support batch streaming workloads
Unified lakehouse Kafka Flink Spark Streaming batch streaming single platform analytics ML self service access. Databricks Unity Catalog Delta Iceberg tables serve BI dashboards real time personalization fraud detection single architecture. Retail customer unified 1B daily events batch history streaming recommendations single lakehouse reduced duplication 67% data team productivity doubled.
Typical engagement timeline
Pipeline modernization 3-6 months lakehouse migration 6-12 months enterprise platform 12-24 months phased delivery continuous value. Healthcare 47 EHR feeds lakehouse live 16 weeks analytics live 24 weeks population health ML 36 weeks RAF scores improved 14%. Retail streaming platform MVP 12 weeks production Black Friday peaks 28 weeks 1B events daily 99.97% uptime.
How handle data quality scale
Automated validation lineage impact anomaly detection remediation ML quality scoring enterprise dashboards alerts ensure zero surprises. Data contracts SLAs between producers consumers block bad data downstream. Monte Carlo Bigeye integration catches missing stale schema drift before analytics ML breaks. Production healthcare pipeline caught 23K anomalies daily prevented 450K bad records reaching models.
Cloud platforms supported
AWS Azure GCP Databricks Snowflake Fabric BigQuery multi cloud hybrid customer choice proven zero downtime migrations. AWS S3 Glue EMR MSK Redshift Lake Formation Azure Synapse Fabric Data Factory GCP BigQuery Dataflow Pub/Sub. Healthcare lakehouse spanned AWS GCP unified FHIR claims IoMT analytics single governance layer across hyperscalers.
How ensure data observability production
Pipeline health freshness quality schema changes downstream impact dashboards alerts root cause analysis automated incident response. Monte Carlo Bigeye Metaplane data downtime alerts stakeholder notifications SLA breaches blocked. Financial services pipeline caught 18K schema changes prevented 2.4M bad features reaching fraud models production ML stayed online zero incidents.CTA BannerBuild data platforms data scientists love stakeholders trust. Partner Zymr production grade data engineering observability ML readiness enterprise scale
Ready for Data Engineering Services making analytics productive ML reliable business confident?
Connect Zymrs data platform architects complimentary data maturity assessment pipeline health review modernization roadmap. Contact Zymr
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