Should we use a data lake, a warehouse, or both?

Use a data lake for raw, cost-effective storage and exploratory workloads, and a data warehouse for curated, high-performance analytics and BI. Many enterprises adopt a lakehouse or hybrid approach to get the best of both worlds and implement clear zone boundaries and governance.

How do you ensure data is discoverable and governed?

We design catalogs, data lineage, access controls, and automated metadata extraction. Policies for retention, encryption, masking, and role-based access are codified and enforced through CI/CD and orchestration tooling.

Data Warehousing & Lake Integration

Connect data flows into warehouses and lakes for enterprise insights

We architect ingestion, modelling, and governance so your data becomes a reliable foundation for analytics, BI, and machine learning.

Overview

A well-designed warehouse or lake turns disparate data into trusted analytics products. We integrate ingest pipelines, storage layouts, and curated models so analysts and ML teams can rely on consistent, performant datasets.

Core Capabilities

Ingestion & Integration

Stream and batch ingestion with CDC, ETL/ELT, and orchestration for reliable delivery into landing, staging, and curated zones.

Data Modeling

Dimensional, vault, and canonical models to support BI, reporting, and ML feature pipelines with versioned schemas.

Partitioning & Performance

Partitioning, clustering, compaction, and indexing strategies to optimize query performance and cost.

Governance & Security

Catalogs, lineage, RBAC, encryption, masking, and compliance controls integrated into pipelines and access flows.

Operationalization

CI/CD for pipelines, monitoring, SLA alerting, cost controls, and automated backfills and replays.

Analytics & BI Enablement

Expose curated datasets, semantic layers, and performance-tuned views for analysts and dashboards.

Our approach

We map data products to business value, define clear zone boundaries, and automate governance. Implementation choices prioritize reproducibility, discoverability, and cost-effective performance for your analytic workloads.

Deliverables

Architecture and zone design (landing, raw, curated, serving)
Data models, schema evolution plan, and migration strategy
Ingestion and transformation pipelines with CI/CD
Catalog, lineage, and RBAC policies
Operational runbooks, cost controls, and monitoring

Why partner with us

We deliver integrated warehousing solutions across cloud platforms and open-source tools. Our focus is on delivering fast, governed, and cost-effective data platforms that empower analytics and ML teams.

Design & delivery process

Discover

Inventory sources, consumers, SLAs, and regulatory constraints.

Design

Choose lake vs warehouse patterns, zones, and access models.

Implement

Ingest, model, and implement governance and automation.

Validate

Test data quality, performance, and security.

Operate

Monitor cost, performance, and compliance.