System Operations
We own the end-to-end operations of big data and AI platforms — covering infrastructure, pipelines, and MLOps with a systematic, reliable approach.
Data Dynamics operates one of Korea's largest big data and AI platforms in production.
Infrastructure Operations
Keep the physical and cloud infrastructure that underpins the platform available and stable.
- Cluster node add/remove/replace (scale out/in)
- OS and kernel patching and security updates
- Disk, network, and memory capacity management
- Hardware fault detection and replacement
- Certificate renewal and SSL/TLS management
Platform Operations
Manage the lifecycle of data and AI services such as Hadoop, Spark, Kafka, NiFi, and Impala.
- Service start, stop, and restart management
- Version upgrades and minor patch rollouts
- Configuration management
- Inter-service dependency and compatibility validation
- HA configuration and failover testing
Monitoring & Alerting
Achieve real-time visibility across every layer and detect anomalies proactively.
- Real-time cluster, node, and service health monitoring
- Application and job execution monitoring
- Threshold-based alerting and escalation
- Centralized log collection (ELK, Fluentd, etc.)
- Dashboard design and reporting (Grafana, Cloudera Manager, etc.)
Incident Response
Minimize service impact through a structured detect → respond → analyze → resolve → review process.
- Detect → first response → root-cause analysis → remediate → post-incident review (RCA)
- Service recovery execution (process restart, node isolation, failover)
- Incident history management and recurrence prevention
- Incident simulation and DR drills
Performance Management & Tuning
Analyze bottlenecks at the query, job, and resource level to achieve optimal performance.
- Spark, Hive, and Impala query analysis and optimization
- YARN and Kubernetes resource queue design and tuning
- Memory and CPU allocation tuning (Executor, Driver, Container)
- Data skew, shuffle, and partition optimization
- Storage format optimization (Compaction, Z-Order, Liquid Clustering)
- Slow query and hotspot analysis
Data Pipeline Operations
Guarantee pipeline SLAs from ingestion through transformation and serving.
- ETL/ELT job scheduling and monitoring
- Job failure retry, alerting, and escalation
- Data quality validation (null checks, schema validation, row-count checks)
- SLA management (pipeline latency monitoring)
- Source-system change handling (schema changes, connectivity failures)
Security & Access Control
Apply least-privilege principles to data and systems and ensure regulatory compliance.
- User, group, and role management (LDAP, SSO, SCIM)
- Kerberos, Ranger, and Unity Catalog authorization
- Data masking and encryption
- Audit log management (access and change tracking)
- Security vulnerability scanning and remediation
Backup & Disaster Recovery (DR)
Safeguard metadata and critical data and ensure rapid recovery.
- Metadata backup (Hive Metastore, Unity Catalog, NameNode)
- Critical data snapshots and replication
- DR plan development and periodic drills
- Backup integrity validation
Capacity & Cost Optimization
Analyze resource usage and achieve the best operational efficiency for the cost.
- Storage usage tracking and expansion planning
- Compute resource utilization analysis
- Cleanup of unused data, tables, and jobs
- Cloud DBU and compute cost analysis and reduction
- Chargeback and showback reporting
AI/ML Model Operations (MLOps)
Operate the full model lifecycle from training to serving and monitoring.
- Model training pipeline scheduling
- Model Registry management (versions, stages, approvals)
- Model serving endpoint deployment and monitoring
- Data and model drift detection
- A/B testing and model performance reporting
- Feature Store operations and feature freshness management