Move data from operational systems into dependable analytics and AI workflows. MetaDynamic designs pipelines that are traceable, recoverable and owned from source to consumption.
Data engineering connects source systems to usable data products. Our work covers ingestion, transformation, validation and delivery—with attention to the failures, changing schemas and recovery steps that production systems must handle.
We select batch, incremental or event-driven patterns to suit the business requirement. Microsoft Fabric, Azure Data Factory, Databricks and SQL-based services are assessed against your existing estate, skills and operating costs.
Preserve the incoming structure and record extraction time, source identifiers and load status.
Apply agreed mappings, business rules and quality checks. Isolate exceptions for review.
Deliver documented data products to analytics or applications with clear access and freshness expectations.
Connect approved ERP, database, file and API sources with documented interfaces and extraction rules.
Design repeatable transformations with incremental loading, scheduling and dependency management.
Create raw, validated and consumption-ready data structures with clear retention and ownership.
Check completeness, duplicates, key relationships and control totals before data reaches consumers.
Review partitioning, processing windows, storage choices and workload behaviour against service needs.
Provide failure alerts, restart procedures, operational documentation and accountable support.
Extract operational data without losing the definitions and control totals that make finance and operations reports trustworthy.
Replace manual file handling with controlled intake, schema validation, transformation and managed delivery.
Evaluate event-driven patterns where a shorter response window has a clear business value and the sources can support it.
Mappings, transformation rules and agreed quality acceptance checks.
Versioned processing logic, reconciliation and failure-path testing.
Scheduling, dependencies, monitoring and recovery responsibilities.
The scope can include source integration, pipeline development, transformation, analytical storage, data quality checks and operational support. We define the boundaries and acceptance criteria before delivery.
ETL transforms data before loading it into the target. ELT loads it first and transforms it using the target platform. The appropriate pattern depends on platform capabilities, data sensitivity and workload requirements.
Yes, subject to the source system’s supported interfaces, permissions and licensing. We agree extraction rules and reconcile the resulting data with the system of record.
We design retry limits, checkpoints, alerts and recovery procedures for each pipeline. A rerun should avoid duplicate results and make incomplete processing visible to the support team.
No. Data volume alone is not the only factor, and many requirements can be served with simpler services. We assess scale, latency, complexity, skills and total operating effort before recommending a platform.
Yes. We begin with a technical and operational review covering dependencies, access, data quality, monitoring and documentation, then agree a support and improvement plan.
Share the source, destination and business requirement. We will help scope a reliable integration and delivery approach.