What is a Data Warehouse? Data Warehouse Definition & Architecture

Businesses are making data analytics a priority like never before. In fact, according to the Dresner Advisory’s Business Intelligence Market Study 2021, organizations in the retail/wholesale, financial services, and technology industries alone are planning 50% or more increases their annual business intelligence (BI) budgets.

Leveraging big data for data analysis is clearly a path that competitive businesses want to take to improve their decision making. But deriving value from an amount of data that grows every day requires companies to first be proficient in data management so that they can perform high-quality data analysis. A data warehouse stores and organizes various types of data — historical, operational, transaction processing, and metadata — from a variety of business processes for analytical use, improving data accessibility and enhancing a business's ability to make bottom-line decisions.

Data warehouse definition

A data warehouse is a central repository that stores current and historical data from disparate sources. It's a key component of a data analytics architecture, providing proper data management that creates an environment for decision support, analytics, business intelligence, and data mining.

An organization’s data warehouse holds business data from multiple sources, including internal databases and SaaS platforms. After the data has been loaded, it can be cleansed, transformed, and catalogued. Data quality is also checked before it's used for analytics dashboards, reporting, machine learning, and any additional needs by decision makers and other end users.

Data warehouse vs. data lake

Although a data warehouse is an effective and useful way to store large amounts of data for business analytics, it's best suited for structured data defined by a schema.

By contrast, a data lake can hold both structured and unstructured data, so in addition to sources defined by schemas, it can hold raw data such as log files, internet clickstream records, images, or social media posts. Data lakes are often built on a big data platform like Apache Hadoop.

Data warehouse vs. data mart

A data mart is a subset of a data warehouse, but holds data for one specific department or line of business, such as sales or finance. A data warehouse can feed data to a data mart, or a data mart can feed a data warehouse.

In summary, data warehouses, data lakes, and data marts perform different duties. Businesses may use all three for different purposes depending upon their data flows, workloads, and operational systems.

Data warehouse architecture

Typically, data warehouses are created with a three-tier architecture:

• The bottom tier features a data warehouse server to collect, clean, and transform data from a variety of sources.
• The middle tier features an OLAP (online analytical processing service) to increase query speed.
• The top tier features reporting tools for end users that allow for the creation of data dashboards that support data analysis.

Historically, businesses used ETL (extract, transform, load) tools to aggregate data into expensive on-premises data warehouse systems. Due to the limited capacity of these expensive systems, business users needed to perform as much prep work as possible before loading data into the management system.

Today, however, cloud-based data warehouses from most providers — including Amazon Redshift from AWS, Microsoft Azure SQL Data Warehouse, Oracle, Google BigQuery, and Snowflake — offer flexible infrastructures with processing and storage capacity that can quickly scale based on an organization's data needs. More and more businesses are opting to skip preload transformations in favor of running transformations at query time — a process referred to as ELT (extract, load, transform). This lets business users transform raw data within a data warehouse at any time for any particular use case.

The overall data analytics process using ELT involves several stages:

1. Extraction: New data is collected from different areas of the business, including company financial records, customer transactions, apps, and inventory.
2. Loading and storage: Data is loaded via a data pipeline from the source into the target system (the data warehouse), where it awaits transformation .
3. Transformation: The data may be checked for duplications or discrepancies, and organized for further use.
4. Analysis: The data is accessed for business intelligence, machine learning, and other analytics applications.

Benefits of an enterprise data warehouse

Data warehouses — whether on-premises or cloud data warehouse — allow businesses to optimize their analysis of complex datasets, yielding benefits in several areas.

• Fast answers: A data warehouse is purpose-built for speedy data retrieval and analysis, enabling business users to quickly access and query relevant data to best inform organization decisions.
• Availability, quality, and consistency: A data warehouse consolidates disparate data from different sources into one organizational source of truth. Companies can cleanse and convert information from multiple sources to enhance their data quality and consistency before storing it in a data warehouse, making the information available for all kinds of reporting.
• Business intelligence: Compared to a traditional database, data warehousing offers businesses better access to information. Businesses can improve processes and make better strategic and operational decisions if they have access to a wide and coherent set of current and historical data in areas such as inventory, finance, and sales. The benefits become even stronger when companies have access to real-time data.
• Cost savings and increased revenue: Data analytics — especially those that can be performed in real time — have a positive impact on the bottom line. A study by the Centre for Economics and Business Research, “The Speed to Business Value,” reports that 80% of companies have seen an uplift in revenue due to real-time analytics, with a potential value of up to $4.2 trillion.

Create your own data warehouse today

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