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Last updated by: Mostafa, Last updated on: 29/06/2025

Introduction to Data Warehouse

This document is intended to introduce new students or those interested in joining the Data Warehouse team to the basics of what a Data Warehouse is and the role it plays within modern data-driven environments.

A Data Warehouse is a centralized repository designed to store, integrate, and analyze large volumes of data from multiple sources. Unlike traditional operational databases, which handle day-to-day transactions, a data warehouse is optimized for high-performance queries, historical data analysis, and strategic decision-making support.

Key Characteristics​

  • Subject-Oriented: Organizes data around key business subjects such as customers, sales, or inventory.
  • Integrated: Brings together data from various sources in a consistent and uniform format.
  • Non-Volatile: Data is stable—once entered, it is rarely modified, enabling consistent historical analysis.
  • Time-Variant: Tracks historical data over time, allowing for trends, comparisons, and forecasting.

The Role of a Data Warehouse​

The Data Warehouse is the backbone of business intelligence (BI) and analytics efforts. It enables organizations to:

  • Consolidate Data: Aggregate information from multiple systems (e.g., ERP, CRM, logs) into a single unified platform.
  • Clean and Transform Data: Improve data quality and consistency using ETL (Extract, Transform, Load) processes.
  • Enable Historical Analysis: Store snapshots of data over time to support deep analysis and trend detection.
  • Optimize Query Performance: Use techniques such as indexing, partitioning, and OLAP cubes for rapid data access.
  • Support Decision-Making: Provide reliable, accurate, and timely insights to stakeholders and leadership teams.

Real-World Application at Redback Operations​

At Redback Operations, the Data Warehouse plays a vital role in supporting various internal projects. The team uses the warehouse to:

  • Centralize data from different subsystems within the organization.
  • Ensure data governance, versioning, and consistency across documentation and services.
  • Provide a solution for the various teams to store their required data.

Technologies Used at Redback​

Our data warehouse ecosystem leverages several modern technologies:

  • Dremio: SQL query engine providing fast, interactive access to data
  • MongoDB: NoSQL database for flexible document storage
  • MinIO: Scalable object storage for handling large volumes of unstructured data
  • Mosquitto: MQTT broker for message handling and IoT data integration
  • Restic: Backup system ensuring data security and recovery capabilities
  • We are open to explore new ideas and tools to improve the Data Warehousing solution

Data Lakehouse Architecture​

At Redback, we implement a modern Data Lakehouse architecture that combines:

  • The flexible storage and scalability of data lakes
  • The structured querying and performance benefits of traditional warehouses
  • Schema enforcement and data quality features

This hybrid approach allows us to handle both structured and unstructured data while maintaining reliability.

Data Pipeline Overview​

[Data Sources] → [MinIO Preprocessing] → [Data Warehouse]

Our data flows through a standardized pipeline that ensures quality and consistency:

  1. Raw data is ingested from various sources
  2. The MinIO preprocessing pipeline handles transformation and validation
  3. Cleaned data is stored in the warehouse for analysis

Understanding the structure and purpose of a data warehouse is a fundamental step toward contributing effectively to data-centric projects. Whether you're interested in development, analysis, or operations, the Data Warehouse offers a solid foundation for impactful work at Redback.