Big Data Solutions
Build data platforms that move reliably from ingestion to analytics, operational dashboards, ML workflows, and governed access across cloud and hybrid infrastructure.
ETL
Reliable pipelines
Batch and streaming ingestion are designed with quality checks, lineage, retries, and operational visibility.
BI
Analytics-ready data
Warehouses, lakehouses, semantic layers, and dashboards are shaped around business questions.
ML
Production foundations
Feature, training, deployment, monitoring, and governance workflows support applied machine learning.
Data pipeline architecture
Nanosek designs ingestion, transformation, orchestration, validation, and delivery pipelines across cloud-native services, Kafka, Spark, Airflow, dbt, and warehouse platforms.
Analytics platforms
We design and operate BigQuery, Redshift, Snowflake, Databricks, lakehouse, and dashboard architectures with cost, security, and performance in mind.
Real-time processing
Streaming patterns support fraud detection, operational telemetry, product analytics, IoT events, security logs, and low-latency business workflows.
Governance and operations
Access, lineage, cataloging, data quality, retention, privacy, monitoring, and incident response are treated as platform requirements, not afterthoughts.
Delivery model
How Nanosek takes the work from design to operations
The goal is not a one-time implementation. Nanosek defines the architecture, proves the migration path, controls production change, and leaves the operating model ready for support.
Map data flows
Review sources, consumers, volumes, freshness, quality issues, ownership, privacy, and reporting needs.
Design the platform
Select storage, processing, orchestration, governance, access, observability, and cost controls.
Build critical paths
Implement priority pipelines, validation, dashboards, data models, and operational alerts.
Scale operations
Add governance, runbooks, lineage, cost reviews, performance tuning, and ML lifecycle support.
Scope map
What the engagement covers
| Workstream | Capabilities | Typical owners |
|---|---|---|
| Pipelines | Batch, streaming, ETL, ELT, orchestration, retries, quality checks | Data engineering, platform |
| Analytics | Warehouses, lakehouses, BI, semantic models, dashboards, performance tuning | Analytics, finance, product, operations |
| ML foundations | Feature pipelines, model deployment, monitoring, reproducibility, governance | Data science, ML engineering |
| Governance | Access, lineage, cataloging, retention, privacy, audit, cost controls | Data governance, security, compliance |
FAQ
Questions enterprise teams ask before starting
Can Nanosek modernize an existing data platform?
Yes. We can improve reliability, cost, access control, performance, data quality, orchestration, and observability without rebuilding everything.
Which data warehouse or lakehouse should we use?
The choice depends on existing cloud footprint, data volume, latency, governance, skills, analytics tooling, and cost model. Nanosek evaluates these before recommending a platform.
Does this include machine learning production work?
Yes, where needed. Nanosek can build the infrastructure foundations for feature pipelines, model deployment, monitoring, and governance.
Related paths
Connect this service to the wider infrastructure roadmap
Ready to plan the next step?
Nanosek can assess the current environment, define the target architecture, and build the delivery plan with the right security and operational controls.
Plan data platform