Data
Big Data
We build data lakes, distributed processing systems, Spark workflows, Hadoop ecosystems, and cloud-scale data infrastructure.
Why it matters
Infrastructure for data that outgrows conventional tools.
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.
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.
Primary intent
Design distributed data systems for high-volume or high-velocity data.
Fit
Where this work creates leverage.
Ideal for
- 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
- Large-scale event analytics
- IoT or sensor data processing
- Data lake modernization
- High-volume machine learning pipelines
Scope
What Solutyics delivers.
Deliverables
- Big data architecture plan
- Data lake or lakehouse setup
- Spark or distributed processing workflows
- Streaming or batch ingestion design
- Governance, cost, and monitoring recommendations
Considerations
- Big data tools add operational complexity
- Cost control should be designed from the start
- Governance and cataloging matter at scale
- Not every analytics problem needs distributed systems
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
Process
How the work moves
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.
FAQ
Questions that shape the work.
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
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.