Big Data & Data Governance Services

Harness big data with scalable processing and governance frameworks that ensure quality, lineage, and compliance.

At Radiansys, we build scalable Big Data Platforms with governance baked into every layer. Our Hadoop, Spark, and Kafka ecosystems ensure data stays accurate, trusted, and audit-ready across cloud and hybrid environments.

Build large-scale batch and streaming systems using Spark, Hadoop, Flink, and Kafka.

Apply governance with metadata catalogs, lineage, data quality checks, and PII masking.

Enforce enterprise compliance with GDPR, HIPAA, SOC2, and ISO 27001.

Deploy hybrid and cloud-native big data platforms on AWS, Azure, and GCP.

How We Implement Big Data & Governance

At Radiansys, big data and governance are treated as a unified lifecycle discipline. We design ecosystems where scalability, lineage, quality, and security are built into every layer of the platform. Every implementation is engineered to keep data trusted, compliant, and ready for analytics and AI across cloud, on-prem, and hybrid environments.

End-to-End Big Data Architecture

We design scalable clusters using Hadoop, Spark, Flink, and cloud-native big data services. Each ecosystem separates ingestion, storage, processing, and consumption layers, giving enterprises predictable performance and room to grow. Automated job handling, workload balancing, and monitoring ensure consistent throughput across batch and real-time workloads.

01

Streaming & Real-Time Data Processing

We build high-throughput streaming pipelines using Apache Kafka, Spark Streaming, and Flink. These systems support event-driven operations for fraud detection, telecom streams, IoT telemetry, and operational analytics. Every stream is engineered for replay, fault tolerance, and low-latency processing even at enterprise scale.

02

Governance, Lineage & Metadata Management

We implement governance frameworks using Apache Atlas, Collibra, Alation, and Glue Catalog. Our approach includes column-level lineage, metadata versioning, profiling, anomaly detection, and automated quality rules. Sensitive data is protected using PII redaction, encryption, RBAC/ABAC, and compliance-ready audit trails.

03

Security & Compliance Controls

Data is secured end-to-end using encryption at rest and in transit, tokenization, IAM controls, logging, and policy enforcement. Our governance models are aligned with SOC2, HIPAA, GDPR, and ISO 27001. Every pipeline and dataset is validated for access controls, data minimization, and regulatory reporting.

04

Cloud-Native & Hybrid Deployments

We deploy clusters using EMR, Dataproc, HDInsight, and Kubernetes-based big data runtimes. Infrastructure is automated with Terraform, CI/CD, and auto-scaling to reduce operational overhead. Hybrid deployments support secure tunneling, on-prem storage systems, and federated governance across environments.

05

Monitoring, Quality & Observability

We use Prometheus, Grafana, OpenTelemetry, Atlas hooks, and custom Spark/Kafka sensors to monitor data health. Our observability stack captures lineage events, schema drift, throughput drops, job failures, and access patterns, enabling fast incident response and higher trust in enterprise data.

06

Use Cases

Streaming Analytics

Build real-time insights platforms with Kafka and Spark to power telecom usage analytics, fraud scoring, and operational dashboards.

Regulatory Data Lineage

Track lineage from ingestion to BI systems for financial audits, compliance checks, and reporting accuracy.

Healthcare Data Governance

Protect PHI with HIPAA-aligned quality checks, PII masking, and secure metadata catalogs.

Enterprise Data Catalogs

Create centralized catalogs for discovery, lineage, access control, and metadata governance across global teams.

Business Value

Trusted analytics

Lineage, validation, and quality checks produce reliable datasets that improve BI, ML, and enterprise reporting.

Compliance-ready data ecosystems

Strong governance frameworks align with GDPR, HIPAA, SOC2, and ISO 27001 requirements.

Scalable big data pipelines

Distributed Spark, Hadoop, and Kafka systems handle large volumes with predictable performance.

Secure enterprise data

Access controls, PII masking, and encryption protect sensitive data across hybrid and cloud environments.

FAQs

Yes, we build scalable batch systems with Spark/Hadoop and low-latency streams using Kafka, Flink, and Spark Streaming.

Your AI future starts now.

Partner with Radiansys to design, build, and scale AI solutions that create real business value.