Terra
Data Pipelines
Reliable movement and transformation of data between sources, platforms and consumers.
Polaris · Intelligence · Service 3 of 4 · Semantic layer
Storage, processing and query architectures for data volumes that outgrow conventional tools.
big-data-processingSemantic layer
Big Data Processing
Our designs are cost-aware from the start. Every dataset has an owner, a retention rule and a defined access pattern, so storage and compute spend stay proportional to value rather than growing silently.
Capabilities
- [01]
Architecture
Lakehouse and warehouse design, storage formats, partitioning and cataloguing on AWS, Azure, GCP or on-premises clusters.
- [02]
Processing
Batch and streaming frameworks, incremental processing, backfills and reprocessing strategies.
- [03]
Query and serving
Analytical engines, semantic models and serving layers for dashboards, applications and models.
- [04]
Cost and lifecycle
Tiering, compaction, retention automation and spend observability per dataset and team.
In the Operational Model
Big data processing produces the semantic layer at scale: it is where raw records become resolved, deduplicated objects with history. Everything above it depends on the quality and freshness of this layer.
Where this service sits in the model
What you receive
- 01Target architecture and migration plan
- 02Provisioned storage and processing platform as code
- 03Data catalogue with ownership and retention rules
- 04Performance and cost baseline with dashboards
- 05Operational documentation
Related services
Terra
Public Cloud
Architecture, migration and operation on Amazon Web Services, Microsoft Azure and Google Cloud Platform.
Polaris
AI Development
Production models tied to named decisions, from problem framing to monitored deployment.