What We Build
DWH Architecture
Scalable, well-modeled data warehouses
- Star and snowflake schema design
- Data modeling and normalization
- Partitioning and indexing strategies
- Multi-tenant and multi-region support
Data Pipelines
Automated data ingestion and transformation
- ETL/ELT workflow orchestration
- Real-time streaming with Kafka and Flink
- Data quality validation and monitoring
- Schema evolution and versioning
ML Infrastructure
From feature store to model serving
- Feature store for ML training and serving
- Training environment provisioning
- Model registry and experiment tracking
- Automated model deployment pipelines
Technical Details
Data Stack
- Warehouses — ClickHouse, PostgreSQL, BigQuery, Snowflake
- Streaming — Apache Kafka, Flink, Debezium CDC
- Orchestration — Apache Airflow, Dagster, dbt
- Storage — S3-compatible object storage, data lake on Parquet/Iceberg
ML & Analytics
- Feature Store — Feast, custom feature pipelines
- Training — Kubernetes-based GPU/CPU training environments
- Serving — MLflow, Seldon Core, custom APIs
- Monitoring — Grafana, Great Expectations, data quality alerts
The Full Journey
From raw data to production ML — a complete data-to-intelligence pipeline.
What You Get
Discovery
We audit your data sources, understand business requirements, and design the target architecture.
Build
We set up the warehouse, build pipelines, and configure data quality frameworks.
Integrate
We connect data sources, deploy ML infrastructure, and run end-to-end validation.
Operate
Ongoing monitoring, pipeline maintenance, and infrastructure optimization.
Why It Matters
Single Source of Truth
All your data in one reliable, well-modeled warehouse.
Faster Time to ML
From data collection to production models in weeks, not months.
Data Quality from Day One
Built-in validation, monitoring, and alerting for data integrity.
Future-proof Architecture
Modular design that scales with your data and ML ambitions.
How to Get Started
Hybrid pricing: T&M setup followed by a monthly subscription for ongoing management.
Technology Stack
ClickHouse
Columnar OLAP database for real-time analytics
PostgreSQL
Reliable relational database for structured data
Snowflake
Cloud data warehouse with elastic scaling
BigQuery
Serverless analytics warehouse by Google
Apache Kafka
Distributed event streaming platform
Apache Flink
Real-time stream processing engine
Apache Airflow
Workflow orchestration for data pipelines
n8n
Workflow automation and integration platform
dbt
SQL-based data transformation framework
Tableau
Enterprise BI and data visualization
Metabase
Open-source analytics and dashboards
Grafana
Monitoring dashboards and observability
MLflow
ML experiment tracking and model registry
Great Expectations
Data quality validation and testing
Apache Iceberg
Open table format for large-scale datasets
Debezium
Change data capture for real-time sync
Frequently Asked Questions
We work with ClickHouse, PostgreSQL, BigQuery, Snowflake, and Redshift. We recommend the best fit based on your data volume, query patterns, and budget.
Yes. We handle full migrations including schema conversion, data transfer, pipeline rewiring, and validation to ensure zero data loss.
Yes. We build streaming pipelines with Kafka and Flink for real-time ingestion, alongside batch ETL for historical data processing.
We implement validation rules, schema checks, and monitoring alerts using tools like Great Expectations and custom data quality frameworks.
A basic DWH setup takes 2–4 weeks. Full data platform with ML infrastructure typically takes 6–8 weeks depending on complexity.
Contact us for more information
Reach out to us through an email or a phone call
Or book a call to get all your questions answered
Or book a call to get all your questions answered