Data Warehousing
Structured. Reliable. Ready to query.
Your insights are only as strong as the architecture behind them. We design intelligent, composable data warehouses—integrated with upstream prep and pipeline layers—to make sure data is clean, consistent, and always analytics-ready.
What We Do
Cloud-Native Warehousing
Deploy enterprise-grade solutions across Snowflake, BigQuery, Redshift, or Azure Synapse. We tune for scale, performance, and storage efficiency—so your business runs on a foundation that won’t crack.
Data Pipelines (ELT/ETL)
Our pipeline designs move and transform data with purpose—batch, streaming, or real-time. Powered by Airflow, dbt, Fivetran, or custom orchestrators, we automate flows from ingestion to delivery.
Storage & Query Optimization
From partitioning to caching to indexing, we fine-tune your system so that performance doesn’t suffer as your data grows. Cold data storage is automated. Hot data is always ready.
Lakehouse Engineering
Bridge the gap between raw flexibility and structured reliability. We build lakehouses that unify structured and semi-structured data—delivering speed and schema enforcement with the scale of cloud-native data lakes.
Data Preparation
We clean, enrich, and structure your data before it hits the warehouse. Schema mapping, validation rules, normalization, and metadata tagging ensure no garbage makes it downstream.
Key Outcomes
70% faster reporting time due to optimized querying
60% cost savings with tiered and compressed storage
100% traceability across prep, pipeline, and warehousing layers
Where It Fits
Unified analytics layer across siloed departments
Real-time data ingestion from product, finance, and CRM systems
Cloud migration from legacy on-prem warehouses
ML model training environments using live business data
Why Datafyze?
One partner for the entire lifecycle—from ingestion to visualization
Platform-agnostic, cloud-native, and scalable
DevOps and observability baked into every pipeline
Built for speed, compliance, and long-term resilience
FAQs
What tools and platforms do you use for data warehousing?
Snowflake, BigQuery, Redshift, Azure Synapse, Delta Lake, and more—chosen based on your use case and scalability needs.
Do you support both batch and real-time pipelines?
Yes. We implement both ELT and streaming pipelines using tools like Kafka, dbt, Airflow, and serverless architectures.
How do you ensure data quality?
We embed validation, deduplication, and schema enforcement during the prep and pipeline stages—before data hits your analytics layer.