Big Data & AI
Data infrastructure that actually reaches production.
Pipelines, analytics platforms, and data infrastructure — built for reliability, not just capability. On private cloud and hybrid environments.
Pipelines
Batch and streaming ingestion with quality checks, lineage tracking, retry logic, and operational visibility.
Core Capabilities
What we deliver
Pipelines
ETL — Reliable pipelines
Batch and streaming ingestion designed with quality checks, lineage tracking, retry logic, and operational visibility across Kafka, Spark, Airflow, and dbt.
Analytics
BI — Analytics-ready data
Warehouses, lakehouses, and dashboards on BigQuery, Redshift, Snowflake, and Databricks — shaped around real business questions, not just available data.
Machine Learning
ML — Production foundations
Feature pipelines, model deployment, monitoring, reproducibility, and governance workflows for applied ML on cloud infrastructure — built to stay running, not just demo.
Real-time Processing
Streaming at scale
Kafka-based event streaming, Spark Structured Streaming, and Flink deployments for real-time data pipelines — with backpressure handling, schema evolution, and operational monitoring.
Governance
Data governance
Access controls, data lineage, catalog management, retention policies, privacy classification, and audit trails — the infrastructure your compliance and legal teams require.
AI Infrastructure
AI-ready cloud
GPU cluster provisioning, inference endpoint management, vector database infrastructure, and Anthropic Claude API integration for enterprise AI applications on cloud environments.
How We Work
Four-step delivery model
From mapping your data flows to scaling ML operations — every data engagement runs the same structured delivery model.
Map data flows
Review sources, consumers, volumes, freshness requirements, quality issues, ownership, privacy constraints, and reporting needs. Establish what data exists and who depends on it.
Design the platform
Select storage, processing engines, orchestration, governance tooling, access controls, observability, and cost guardrails. Validated before any infrastructure is provisioned.
Build critical paths
Implement priority pipelines, data quality checks, semantic models, dashboards, and operational alerts. Priority on production-grade reliability from day one.
Scale operations
Add governance runbooks, lineage tracking, cost reviews, performance tuning, and ML lifecycle support. Handover documentation so your team owns and operates it.
Engagement Scope
What's included
| Workstream | Capabilities | Typical owners |
|---|---|---|
| Pipelines | Batch, streaming, ETL, ELT, orchestration, retries, quality checks, schema management, lineage | Data engineering, platform |
| Analytics | Data warehouses, lakehouses, BI, semantic models, dashboards, query performance tuning | Analytics, finance, product, operations |
| ML foundations | Feature pipelines, model deployment, inference endpoints, monitoring, reproducibility, registry | Data science, ML engineering |
| Governance | Access control, lineage, cataloging, retention, privacy classification, audit, cost controls | Data governance, security, compliance |
From raw data to production ML — end to end.
PurePeak designs and operates data platforms that engineering teams can trust — reliable pipelines, governed warehouses, and AI infrastructure that stays running in production.