Foundation layer
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.
Data
Data service track
Data Engineering and Pipelines
We build ETL, ELT, data warehouses, streaming pipelines, data quality checks, and integration layers so teams can trust their data.
Approach
Reliable pipelines for analytics, AI, and operations.
The work is structured around explicit decisions and usable outputs rather than a generic delivery template.
Reliable data flow
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.
Fit
Where this creates leverage
The strongest engagements have a clear operating constraint, decision, workflow, or delivery risk to improve.
Best fit
Conditions that make the work valuable
- 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
Situations the service can address
- ERP and CRM data pipelines
- Analytics warehouse setup
- AI-ready document or event pipelines
- Operational data integration
Deliverables
What Solutyics actually delivers
Each workstream is labelled for the outcome or artifact it is responsible for, not its position in a template.
Pipeline architecture
Pipeline architecture
Ingestion and transformation
ETL or ELT implementation
Analytical modeling
Warehouse or lakehouse modeling
Quality monitoring
Data quality checks and monitoring
Data lineage and ownership
Documentation for sources, transformations, and ownership
Process
How the work moves
A visible sequence of decisions, working outputs, review points, and handover, rather than a black-box delivery cycle.
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.
Outcomes
What should improve after the work
Reliable data movement
Cleaner analytics foundations
Better data quality visibility
A scalable base for BI and AI
FAQ
Questions that shape the work
The answers below clarify scope, collaboration, ownership, and the conditions that usually affect delivery.
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 step
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.