Atyeti takes a ‘Data First’ strategy, because no amount of downstream analytics can replace solid data management or mitigate risk from poor governance. Data Engineers work closely with Business Analysts and Data Analysts to define appropriate quantification parameters and data remediation policies. Governance is designed-in at the infrastructure architecture, ensuring end-to-end comprehensive coverage before any data is even received.
"A PRINCIPLED, COMPREHENSIVE APPROACH."
Storage and Retrieval:
- Format Design: Data-centric design for Unstructured, Document, Relational, Timeseries, and Graph databases
- Analytic Enablement: MPP and Scalable Lakehouse design targeting analytic workflows
- Service Optimization: Optimization of data, schema and database for data serving to applications
- Archive Support: Cold storage and retention policies
Quality
- Consistency: Consistency of data with related sets
- Timeliness: Reliable SLAs of ingestion and analytics
- Validity: Model based validity assessments
- Completeness: Missing data, out of order records
- Uniqueness: De-duplication and redundancy elimination
Security
- Legality: Regulatory compliance
- Confidentiality: Access control, data masking
- Regional Control: Geographic fencing
- Durability: Redundancy, versioning, source retention
- Uniqueness: De-duplication and redundancy elimination
Transparency
- Accessibility: Easy visibility into quality metrics
- Accountability: Auditability, fault tracing
- Discovery: Searchable, automated data dictionaries
Provenance
- Origination: Meta regarding sourcing information
- Chain of Custody: Meta tracking of historic ownership and storage
- Analytic Record: Complete production and transformation legacy tracking
Scalability
- Data Volume: Scalable data parallelization
- Data Frequency: Reconfigurable batch, minibatch and stream analytics without recoding
- Computational Complexity: Micro services and task parallelization