Data Warehouses

A Single Source of Truth
for Enterprise Reporting

Fragmented reporting environments — where finance, operations, and sales each maintain separate data extracts with diverging figures — are among the most costly and persistent problems in enterprise analytics. A properly designed data warehouse eliminates this fragmentation by providing a centralized, governed, and query-optimized repository that all business intelligence tools and analytical users consume from a single, trusted source.

Contact TecBrix

Finance

Operations

Sales

What Is a Data Warehouse?

A data warehouse is a subject-oriented, integrated, time-variant, and non-volatile data repository — designed specifically for analytical query workloads rather than transactional processing. Unlike operational databases optimized for high-frequency row-level inserts and updates, a warehouse is structured for columnar storage, aggregation, and complex joins across large historical datasets.

Dimension

Fact

Modern cloud-native warehouses — including Snowflake, Amazon Redshift, Azure Synapse Analytics, and Google BigQuery — separate compute from storage, enabling organizations to scale query capacity independently of data volume and pay only for the compute consumed.

Snowflake

Amazon Redshift

Azure Synapse Analytics

Google BigQuery

Common Challenges TecBrix Resolves

Inconsistent KPIs and conflicting figures across departments and reporting tools

Slow dashboard and report load times caused by queries hitting operational systems directly

Legacy on-premise warehouses with limited scalability and high maintenance overhead

Lack of historical data retention and time-series analysis capability

BI tools connected directly to source systems, creating performance degradation and data risk

How TecBrix Helps

Kimball

Data Vault 2.0

Warehouse Architecture & Dimensional Modeling

TecBrix designs warehouse schemas using proven methodologies — Kimball dimensional modeling, Data Vault 2.0, or hybrid approaches — selected based on your use case complexity, source system landscape, and reporting requirements. We define fact and dimension structures, slowly changing dimension strategies, and aggregation layers that optimize query performance while maintaining analytical flexibility.

Architecture decisions account for current data volumes and growth projections, query concurrency requirements, real-time versus batch refresh patterns, and the consumption tools that will sit above the warehouse layer.

Teradata

SQL Server PDW

Netezza

Oracle

Implementation & Platform Migration

Whether you are building a warehouse from scratch or migrating from a legacy platform — Teradata, SQL Server PDW, Netezza, or an on-premise Oracle environment — TecBrix manages the full implementation lifecycle. This includes data profiling, source-to-target mapping, transformation logic development, load validation, and performance benchmarking against acceptance criteria.

For cloud migrations, we implement the warehouse within your chosen cloud environment, configure auto-scaling and cost governance controls, and integrate it with your existing data pipeline infrastructure.

data quality rules

deduplication logic

referential integrity checks

Data Quality & Semantic Consistency

A warehouse is only as valuable as the data within it. TecBrix implements data quality rules, deduplication logic, and referential integrity checks during the transformation layer — ensuring that what enters the warehouse is clean, consistent, and reconcilable against source systems. We also define and document semantic definitions for key metrics so that every BI tool consuming the warehouse applies the same business logic.