Uses of Data Warehouse

A central location to store and analyze your data

The fastest way to set up scalable data infrastructure.

Everything you need to organize your data and prepare it for the BI tool of your choice.

Additional Resources

QuotaPath Implements an All-in-One Data Stack and Starts Leveraging Data Across the Organization

The Start-Up's Guide to a Modern Data Stack

The Right Time to Build a Data Stack

A 2022 IDC Global DataSphere forecast report predicts that data creation and replication will double between 2022-2026. Another study projects that global data creation will grow to over 180 zettabytes from 2020-2025. Advanced analytics technologies and inexpensive data storage contribute to the current data explosion. However, aggregating and consolidating this data into a centralized location for easier and more critical analysis remains complex.


With data trapped in different sources, businesses strain to access consistent and accurate data from multiple analytics. Fortunately, organizations can adopt and take advantage of the various uses of data warehouses to expedite business intelligence via aggregated data analysis. Modern data platforms like Mozart Data can help your organization centralize and consolidate massive volumes of data. 

What Is a Data Warehouse?

A data warehouse is a central repository system designed to aggregate business data from multiple sources and organize it for easy analysis. Typically, it acts as a business’s single source of truth by aggregating data within a standardized and non-volatile system easily accessible to employees.


The role of data warehouses in business intelligence is critical. It is solely intended to technically and practically perform queries and analyses from different sources, including internal and external databases. The importance of a data warehouse cannot be overemphasized; its analytical capabilities enable businesses and organizations to derive valuable insights and guide decision-making. Over time, it creates an invaluable historical record that business analysts and data scientists can use.


Business intelligence experts and data analysts must understand key data warehouse concepts before embracing this system. Some of the most common concepts include data warehouse architecture and cloud data warehouse software.

Data Warehouse Architecture

The exact data warehouse architecture depends on the organization’s specific needs. The three available modes include single-tier, two-tier, and three-tier architecture, each with its differences:


  • Single-tier stores as little data as possible.
  • Two-tier uses a database server to separate actual data sources and the warehouse.
  • Three-tier comprises many source systems, data warehouses, and reconciliation layers.


Data Warehouse Vs. Data Lake

When organizations want to analyze data from several sources, they supplement their databases with a data lake, data warehouse, or both. Let’s take a closer look at data warehouse vs. data lake:


  • A data lake stores all organization’s data in raw unstructured form. A data warehouse stores processed data in a structured format.
  • In a data lake, the data is used by data scientists, while in a data warehouse, it’s used by business professionals.

Why Your Organization Needs Data Warehouse

Advantages of a Data Warehouse include:


  • Snowflake data warehouses allow organizations to access crucial data from different sources all in one place. Therefore, it reduces the total turn-around time for data retrieving, analysis, and reporting.
  • It provides consistent and credible information on multiple cross-functional activities. It does this through integration, data optimization, and data standardization to produce consistent results.


Advantages of Data Lakes include:


  • Data lakes offer cost-effective solutions for storing massive volumes of data.
  • They give users immediate access to all data.


Organizations should pair data warehouses and lakes with modern data platforms like Mozart Data for efficient data analysis and management. This way, they will seamlessly store and query large volumes of data and gain valuable business and technical insights.

Data Warehouse vs. Database

While databases are similar to data warehouses in smoothening data analysis, the significant difference is that databases store only one type of data. Usually, databases store data before being transferred to data warehouses. Therefore, while they are helpful in primary data collection, they don’t facilitate easy data analytics like warehouses.

Components Of Data Warehouse

There are several components of data warehouses specifically designed to enhance system speed for fast results and precise data analysis. These include:


  • Load manager: Also called the front component, performs all operations related to data extraction and loads it into the data warehouse.
  • Warehouse manager: Performs operations related to the warehouse management process.
  • Query manager: Also known as the Backend component, performs operations managing user queries.
  • ETL (Extraction, Transformation, Loading) tools: ETL is a data integration process that extracts data from various sources, transforms it into a suitable arrangement, and loads it into the warehouse.
  • Metadata: Tells you everything about the source, value, usage, structure of data and other characteristics of the data warehouse.
  • Access tools: Enable users to interact with the data in the warehouse. They include query, data reporting, OLAP, data mining, application development, and EIS.

Types Of Data Warehouse

There are three primary types of data warehouses. The type chosen by an organization depends upon its requirements. They include:


  • An Operational Data Store is a central database for operational reporting. It is often used in online transaction processing applications.
  • Enterprise Data Warehouse is a centralized warehouse that offers support services across the organization.
  • Data Mart is a section of a data warehouse usually targeted to a particular team. It’s designed for specific groups like the sales team, finance, or marketing.

Data Warehouse Schemas

While there are different data warehouse schemas, our platform uses the snowflake schema for seamless and high-performance data warehousing. Mozart Data’s platform centralizes data and offers comprehensive and accurate analytics for improved decision-making.

Data Warehouse Tools

There are several data warehouse tools available in the market. Below are some of the most popular ones:


  • Snowflake: Snowflake delivers a cloud-computing-based data warehousing tool that provides simplified data processing.
  • Amazon RedShift: Amazon RedShift is an easy-to-use, low-cost warehousing tool that provides fast querying capabilities for structured data. It performs this task using Business Intelligence (BI) tools and SQL-based clients.
  • Oracle: Oracle is a well-established data warehousing tool created to offer business analytics and insights.
  • Microsoft Azure: It’s an open-source analytics tool for building, testing, managing, and deploying services and applications through Microsoft-managed data for urgent BI needs.
  • Google BigQuery: A high-end data warehousing tool that enables super-fast SQL queries offering users real-time data insights. Its features include cloud data flow, flexible data ingestion, automated data transfer, and read-and-write data.
  • Teradata: A relational database management system that provides all-in-one solutions for organizations with comprehensive data analytics.


Businesses that adopt Mozart’s platform spin up data stack in minutes and enabling businesses to:


  • Use data extraction tools and aggregate data for in-depth analysis.
  • Provide easy data access in google sheets
  • Maintain free data integration with automated data extraction tools
  • Enhance performance and improve Return on Investment 

Application Of Data Warehouse

Data warehouses are essential for every organization, no matter how small, big, or the industry it works. Organizations require data warehouses to connect their diverse unstructured and structured data for prediction, statistical analysis, analytical reporting, and business intelligence, facilitating robust decision-making.


Here are critical applications of data warehouses across different industries:


  • Healthcare data storage: All clinical, financial, and employee records are fed into the data warehouse. This data helps medical institutions predict and strategize outcomes, analyze and track feedback, generate employee and patient reports, provide feedback to physicians, and share data with relevant entities like insurance companies or medical aid services.
  • Banking: Warehouses play a crucial role in risk management, policy reversal, customer data analysis, government regulations compliance, market trend identification, product performance analysis, and financial decision-making.
  • Government: Data warehouses help maintain and analyze tax records, health policy records, criminal law databases, terrorist profiles and records, payroll administration, recruitment, and fraud detection.
  • Manufacturing, Production, and Distribution: Data warehouses enable businesses to predict market changes, identify potential growth areas, examine current and previous trends, identify profitable product lines, and make informed decisions.
  • Retail Industry: Retailers utilize a data strategy and record data from multiple parties, serving as intermediaries between end users and wholesalers. The importance of data warehouses in business becomes evident as they help organize data storage, track items, promotions strategies, buying trends, and analyze sales.
  • Hospitality: Data warehouses are used to create and assess promotion programs, track travel patterns, and optimize customer experiences.
  • Transport: Data warehouses record client data and feedback, manage crews, and analyze financial reports, enabling efficient transportation operations.

Unique Features of Mozart Data’s Platform 

The use of data warehousing in organizations is becoming increasingly necessary. A data warehouse is an essential business intelligence tool, and Mozart’s platform revolutionizes advanced analytics over specific periods. This modern data stack has proven to be over 70% more efficient than traditional approaches. It’s highly scalable and provides users with accurate, uniform, and unified data within minutes. Some of its key features include:


  • Fast synchronization with reliable no-code integration for rapid decision-making.
  • Automated data integration to efficiently answer complex queries.
  • Seamless synchronization with Google Sheets, allowing users to operate on familiar tools.


If you are considering a data warehouse solution or upgrading your current warehouse, the Mozart platform is here to help. Join other data-driven organizations using Mozart and unleash the full potential of your data.