Scale with purpose
Big data is not a badge. It is a signal that ordinary database and reporting patterns are no longer enough for the volume, velocity, history, or processing requirements of the business.
Data
Data service track
Big Data
We build data lakes, distributed processing systems, Spark workflows, Hadoop ecosystems, and cloud-scale data infrastructure.
Approach
Infrastructure for data that outgrows conventional tools.
The work is structured around explicit decisions and usable outputs rather than a generic delivery template.
Practical distributed design
Solutyics helps teams design lake, lakehouse, streaming, and distributed-processing foundations where the scale justifies it. We focus on practical architecture, cost control, governance, and downstream analytics or AI use.
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 with large event, sensor, transaction, or log data
- Teams outgrowing traditional warehouse patterns
- Organizations building data lakes or lakehouses
- Products requiring distributed processing or streaming
Typical use cases
Situations the service can address
- Large-scale event analytics
- IoT or sensor data processing
- Data lake modernization
- High-volume machine learning pipelines
Deliverables
What Solutyics actually delivers
Each workstream is labelled for the outcome or artifact it is responsible for, not its position in a template.
Scale architecture
Big data architecture plan
Lakehouse foundation
Data lake or lakehouse setup
Distributed processing
Spark or distributed processing workflows
Ingestion design
Streaming or batch ingestion design
Governance and cost
Governance, cost, and monitoring recommendations
Process
How the work moves
A visible sequence of decisions, working outputs, review points, and handover, rather than a black-box delivery cycle.
Validate the scale problem
We determine whether volume, velocity, retention, or processing complexity truly requires big data architecture.
Design storage and processing
We choose lake, lakehouse, warehouse, streaming, and distributed-processing patterns around the use case.
Build the platform layer
We implement ingestion, storage, transformations, processing jobs, and access patterns.
Govern and operate
We document cost controls, ownership, monitoring, data quality, and platform maintenance.
Outcomes
What should improve after the work
A data platform matched to scale
Cleaner processing architecture
Better cost and governance visibility
A foundation for advanced analytics and AI
FAQ
Questions that shape the work
The answers below clarify scope, collaboration, ownership, and the conditions that usually affect delivery.
When does a company need big data infrastructure?
Big data infrastructure is justified when data volume, speed, retention, or processing complexity makes traditional databases and BI workflows too slow, expensive, or fragile.
Do you work with Spark?
Yes. We can design and implement Spark workflows where distributed processing is appropriate for the workload.
What is a data lake?
A data lake stores large volumes of raw or semi-structured data for processing, analytics, and AI. It needs governance and modeling to avoid becoming an unmanaged dump.
Is big data always cloud-based?
No. It can run on cloud, private infrastructure, or hybrid setups. The right architecture depends on cost, data sensitivity, team skills, and integration needs.
Can big data support machine learning?
Yes. Large-scale ML often depends on reliable ingestion, feature pipelines, distributed processing, and storage patterns that can handle large histories.
Next step
Use big data architecture only where the scale justifies it.
Bring the volumes, sources, query patterns, and cost concerns. We will help decide the right platform shape.