Data
Data Engineering and Pipelines
We build ETL, ELT, data warehouses, streaming pipelines, data quality checks, and integration layers so teams can trust their data.
Why it matters
Reliable pipelines for analytics, AI, and operations.
Data engineering creates the foundation that reporting, AI, and operations depend on. If pipelines are fragile or definitions are inconsistent, every dashboard and model built on top becomes harder to trust.
Solutyics designs pipelines around source systems, transformation logic, quality checks, governance, latency, and downstream use. The goal is data that arrives reliably, can be understood, and can support future analytics or AI work.
Primary intent
Build reliable data movement and modeling foundations for analytics and AI.
Fit
Where this work creates leverage.
Ideal for
- Companies consolidating data from multiple systems
- Teams building analytics or AI foundations
- Organizations with brittle scripts and manual exports
- Products that need data ingestion and transformation pipelines
Typical use cases
- ERP and CRM data pipelines
- Analytics warehouse setup
- AI-ready document or event pipelines
- Operational data integration
Scope
What Solutyics delivers.
Deliverables
- Pipeline architecture
- ETL or ELT implementation
- Warehouse or lakehouse modeling
- Data quality checks and monitoring
- Documentation for sources, transformations, and ownership
Considerations
- Source-system reliability affects pipeline design
- Data quality checks should catch silent failures
- Transformation logic needs ownership
- Batch, near-real-time, and streaming designs solve different problems
Outcomes
What should improve after the work.
Reliable data movement
Cleaner analytics foundations
Better data quality visibility
A scalable base for BI and AI
Process
How the work moves
Map sources and consumers
We document source systems, data owners, refresh needs, consumers, and business definitions.
Design the pipeline
We choose ingestion, transformation, storage, quality checks, orchestration, and monitoring patterns.
Implement and validate
We build pipelines, data models, tests, and documentation around the agreed architecture.
Operationalize the data layer
We set up monitoring, ownership, change handling, and handover for ongoing use.
FAQ
Questions that shape the work.
What is the difference between ETL and ELT? +
ETL transforms data before loading it into storage. ELT loads raw data first and transforms it inside the warehouse or platform. The right choice depends on volume, tooling, governance, and analytics needs.
Can you build pipelines for AI projects? +
Yes. AI projects often need ingestion, cleaning, chunking, labeling, vectorization, feature preparation, or ongoing refresh pipelines.
How do you handle data quality? +
We add validation checks, schema checks, freshness checks, reconciliation, error alerts, and documentation so teams can detect and fix issues earlier.
Do we need real-time streaming? +
Only if the business process requires it. Many analytics workloads work well with scheduled batch pipelines, which are simpler and cheaper to operate.
Can you work with existing databases and tools? +
Yes. We can connect to existing databases, APIs, cloud warehouses, spreadsheets, ERP systems, CRM systems, and analytics tools depending on access and constraints.
Next
Build the data foundation your reporting and AI can stand on.
Bring the source systems, current reports, and reliability issues. We will help design the pipelines properly.