From brittle pipelines to an AI Ready Lakehouse.
We help enterprise teams modernize ingestion and transformations with declarative YAML + pure SQL, optimize cloud native lakehouse performance, integrate real-time AI inference, and ship governance and FinOps as code.
Configuration as code ELT with schema evolution & testing.
Elastic compute, merge on read upserts, and IO optimization.
Unified features to avoid training serving skew and enable low latency.
Lineage, privacy controls, and cost automation by design.
Pipeline Modernization
YAML + SQL, not spaghetti code
- • Universal JDBC/ODBC connectivity
- • Automated schema evolution policies
- • Validation, privacy & lineage built in
AI Ready Lakehouse
Real time features & inference paths
- • Merge on read upserts for low latency updates
- • Feature definitions reused for training & serving
- • Cost governance via spot/serverless automation
Competency Framework
Four pillars that cover the modern data stack end-to-end
Your services engagement is structured around the critical failure points in today’s data platforms so modernization is measurable, repeatable, and scalable.
Declarative Data Engineering
Modernize ELT with Configuration as code
Replace brittle scripts and opaque ETL tools with YAML driven ingestion, quality checks, and SQL first transformations that are portable across clouds.
- Zero/low code ingestion patterns
- Automated schema evolution policies
- SQL transpilation (write once, run anywhere)
- Auto lineage + dependency graphing
Cloud-Native Lakehouse
Performance and elasticity at petabyte scale
Design cloud-native lakehouse topologies that separate compute and storage, solve small-files and upsert bottlenecks, and optimize IO for object stores.
- Compute/storage separation with Kubernetes
- Hybrid partitioning to prevent skew
- Merge-on-read upserts for near real time
- Metadata scaling strategies
AI & ML Infrastructure
Close the loop between data and inference
Unify feature engineering across training and serving to eliminate skew, support large sparse feature spaces, and enable real time inference paths.
- Unified feature definitions via SQL
- Real-time inference integration paths
- Support for sparse / large feature spaces
- Online learning ready architectures
FinOps & Governance
Cost, security, and compliance by design
Ship cloud cost controls, lineage, and privacy controls as code so production platforms remain auditable, secure, and economically sustainable.
- Policy-as-code for privacy and controls
- Column-level encryption and masking
- Auditability and automated lineage
- Spot ready + serverless cost strategies
Services
A complete catalog of capabilities from pipelines to AI to governance
Choose a targeted engagement or combine modules into an end-to-end modernization program.
What you get
Production grade architecture, implementation, and enablement delivered with the same declarative principles we advocate.
- Architecture blueprints and reference implementations
- Production deployment and hardening
- Team enablement (YAML + SQL + CI workflows)
- Performance and cost optimization playbooks
Engagement Model
A predictable path from assessment to production and continuous optimization
We structure work in phases so you can deliver value early, reduce risk, and scale confidently.
Assess & Align
Understand the current platform, constraints, and target outcomes. Define success metrics and a migration plan.
Design
Create reference architectures for ingestion, lakehouse layout, AI integration, and governance/FinOps controls.
Implement
Deliver production grade pipelines and platform components with security, testing, and operational runbooks.
Enable
Train teams on declarative engineering (YAML + SQL), local dev with DuckDB, and CI/CD workflows.
Optimize
Tune performance and cost continuously: lakehouse compaction/partitioning, spot/serverless strategy, and governance automation.
Starlake integrates effortlessly for maximum flexibility.
Technology Coverage
Broad interoperability, delivered with a cloud agnostic approach
We design for portability: logic lives in YAML + SQL, deployments adapt to your cloud and runtime choices.
Cloud Platforms
- AWS
- Google Cloud Platform (GCP)
- Microsoft Azure
Warehouses & Lakehouses
- BigQuery
- Snowflake
- Redshift
- Azure Synapse
- Databricks
- DuckDB / DuckLake
Orchestration & CI/CD
- Airflow
- Dagster
- Prefect
- GitHub Actions
- GitLab CI
Local Dev & Testing
- DuckDB
- Docker
- CI runners with ephemeral test datasets
Ready to modernize?
Let’s map your path to an AI Ready Lakehouse.
Share your current stack and constraints our team will propose an actionable blueprint and a phased delivery plan.
Typical outcomes
Concrete improvements you can operationalize within weeks, not quarters.
- Standardized pipelines that are self-documenting and audit-ready
- Faster delivery via declarative configuration changes
- Lower cloud spend with spot/serverless-ready architecture patterns
- Governance and privacy controls embedded in the data lifecycle
Start with a strategy call
We’ll review your pipelines, lakehouse layout, and AI/governance requirements and identify the fastest path to production value.
Prefer async? Email us at [email protected].