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Data is as essential to the global economy as electricity. But just as we need power plants to keep the lights on and machines humming, we need systems that collect, store and organize data before it can be put to work.
Data warehouses bring together vast amounts of different types of data — customer profiles, financial transactions, product catalogs, equipment logs, market trends and so on — then make it easy to slice and dice that data in any number of ways. Data warehouses help CFOs forecast next year's revenues, HR leaders anticipate workforce needs, operations managers optimize their manufacturing facilities, and CEOs make strategic decisions about the future of their business. Data warehouses are also providing the foundation for new artificial intelligence tools by providing high-quality sources of information for training AI models.
In this article we'll explore what makes data warehouses unique, the components that go into creating them and how they offer organizations a competitive advantage.
Simply put, a data warehouse is a centralized repository that stores current and historical data from multiple sources across an organization, designed to support business intelligence (BI) and analytics. By creating a single source of truth for business data, data warehouses help eliminate the inconsistencies and duplication that occur when different departments use their own data repositories.
Unlike operational databases, which are designed to handle day-to-day transactions, data warehouses are optimized for complex queries, reporting and data analysis to support strategic decision-making. While an operational or relational database may contain near-real-time information about how different segments of the business are performing, data warehouses offer a more historical perspective across the entire organization. As a result, they use different organizational structures and methods for processing transactions.
Data warehouses also differ from data lakes, which are used to store raw data for use in future analysis. A data lake is a bit like a storage unit where you keep things you think you might need one day; in a data warehouse, you know exactly what you have and how you plan to use it. A data lake can serve as a source of raw data that can later be extracted, transformed and loaded into a data warehouse.
Data lakehouses are a more recent hybrid of these two concepts, combining a data lake's ability to store structured and unstructured data at relatively low cost with the advanced analytical capabilities of a data warehouse.
Though the exact feature set of each data warehouse may vary, they typically rely on a three-tiered architecture to process structured, unstructured and semi-structured data quickly and efficiently.
This is the layer where information from internal and external data sources is ingested and stored. This data is extracted from its original source and transformed or enriched by resolving inconsistencies, correcting errors, converting files to a uniform format and creating fields that support calculations. It is then loaded into a central repository — a process known as extract, transform, load (ETL). More modern architectures rely on the warehouse's internal processing power to transform the data after loading it, more commonly referred to as ELT.
The middle tier is where data analysis takes place. Here the warehouse also captures technical and operational metadata to track data lineage, ensure its trustworthiness and help users understand what the data means and how it can be used. An Online Analytical Processing (OLAP) engine is then deployed to analyze thousands of rows of data at the same time across multiple dimensions. For example, retail sales data could be analyzed by SKU, the dollar amount of each sale, cost of goods, transaction date and time, geographical location, store identity, customer segment and much more.
The final layer is where users can conduct ad hoc analysis of the data — for example, comparing online sales volumes vs. brick-and-mortar store sales, or analyzing performance of different customer segments across a range of geographies. Users can export these results to business intelligence tools or executive dashboards for further analysis.
Thus, data that originated in a customer relationship management (CRM) or enterprise resource planning (ERP) system can be pulled into a data warehouse, cleansed and standardized, optimized for analysis, exported to smaller domain-specific repositories known as data marts, or imported directly into BI platforms or dashboards where users can run queries against the data.
Every data warehouse is made up of the same fundamental components. At a minimum, a data warehouse will include:
In addition to CRM, ERP and other enterprise databases, data can include external sources such as market research and real-time streams from web applications or internet of things (IoT) sensors.
Tools that extract data from each source, clean and standardize it, and load it into the warehouse.
A buffer zone where raw data is temporarily stored, validated and prepared before entering the main warehouse.
Where the information physically resides, organized by a schema — a framework that describes how different pieces of data relate to each other.
Systems that maintain information about the structure of the data, the business rules governing it, and the history of the data as it is transformed and used.
Technical infrastructure that allows users to ask complex questions of the data and receive sophisticated responses.
Controls used to manage user authentication and access privileges, protect sensitive data and audit compliance with security policies.
External tools that enable users to conduct sophisticated analysis, create reports and visualize the results.
Not all data warehouses are created equal. Some are designed to assist with long-term strategic planning, others are built to help manage tactical day-to-day operations. There are also subsets of warehouses created for specific groups within an organization. They fall into three primary types: Enterprise data warehouses (EDW), operational data stores (ODS) and data marts.
An EDW is a centralized repository that integrates data from all major business systems, providing a comprehensive view of the entire enterprise. It combines data from multiple systems into a unified format, may contain years of historical data, and is designed to support decision making and strategic planning across all business functions.
An ODS is designed to bridge the gap between transactional systems that store real-time data and data warehouses used for longer term analysis and strategy. It stores data in its original format, without the need for ETL or ELT processes. Because it is designed for day-to-day decision making and real-time analysis, an ODS is updated more frequently and contains less historical data than an EDW.
A data mart is typically a smaller subset of an EDW, limited to specific business domains such as sales, marketing, finance or human resources. Each data mart is optimized to meet the analytical needs of a specific business function, and is typically faster to implement and easier to maintain due to its more limited scope.
As with other mission-critical IT functions, organizations can choose how and where to deploy a data warehouse. Enterprises can maintain their own data warehouse in an on-premises data center, have it hosted in the cloud or deploy a hybrid architecture that combines the two. As shown in the table below, there are significant differences between on-prem and cloud in terms of infrastructure investment, performance, scalability, maintenance and cost efficiency.
Operational aspect |
On-premises data warehouse |
Cloud-based data warehouse |
Infrastructure |
Enterprises buy, install and maintain all hardware and software systems, managing the entire IT stack. |
Providers manage all physical infrastructure; enterprises can control infrastructure via APIs and web interfaces. |
Performance |
Defined by specific hardware configurations, which teams can optimize for specific workloads. Minimal network latency. |
Elastic performance can scale up or down depending on workload needs. Latency issues and the need to share resources with other cloud tenants may negatively impact performance. |
Scalability |
Increasing warehouse capabilities requires significant upfront planning and capital investment, and may result in overprovisioning if workload demands decrease. |
Compute and storage resources can scale nearly infinitely based on workload needs, using a consumption pricing model. |
Maintenance |
Enterprises are responsible for all maintenance, patching and upgrades, requiring significant staffing investments. |
Providers handle all infrastructure maintenance, while enterprises are responsible for data governance, security and application maintenance. |
Cost |
Requires significant upfront investment as well as ongoing operational costs, but costs remain relatively fixed and predictable regardless of actual usage. |
Costs scale directly with usage, which can result in unpredictable expenditures, along with large data egress charges. |
Enterprises that opt for on-premises solutions typically have strong IT organizations, deploy predictable, high-volume workloads, and require complete control over data sovereignty, governance and infrastructure. Organizations choose cloud providers when they lack internal expertise, need to deploy quickly, have widely varying workloads, require global access to data or want to minimize up-front capital expenditures.
Many organizations are beginning to adopt hybrid approaches that combine on-premises control for sensitive data with cloud scalability for analytics and development workloads. This enables them to optimize for both security and agility based on specific use-case requirements.
There are many reasons why organizations may choose to deploy a data warehouse. Some of the most important include the following:
As a single source of truth, data warehouses ensure everyone in an organization has the most accurate and up-to-date information for making decisions.
The ability to analyze years of historical data allows organizations to identify long-term trends and operational behavior patterns, aiding in strategic planning.
A data warehouse's built-in validation and cleansing capabilities ensure that enterprise data is reliable and complete.
Cloud-based data warehouses can automatically scale computing and storage resources based on demand, handling peak analytical workloads without over-provisioning.
Most data warehouses support advanced analytical features such as regression analysis and time-series forecasting; some also feature built-in machine learning capabilities, allowing data scientists to manage models directly inside the warehouse environment.
Regular databases are built to handle the day-to-day transactions of an organization, while data warehouses are designed for analytical queries on historical data, making them ideal for more strategic decision making.
Data warehouses can store multiple types of structured, unstructured and semi-structured data from a wide range of sources, such as internal enterprise databases, external market reports and real-time data feeds.
By serving as a single source of truth, data warehouses can eliminate data silos, ensure data consistency across departments, enable historical analysis, improve query performance for analytics and provide reliable reporting capabilities.
Modern warehouses provide enterprise-grade security including encryption, access controls, audit trails and compliance certifications. Cloud providers often offer security capabilities beyond what most organizations could implement independently.
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