What is Data Warehousing? The Definitive Guide for 2024

What is data warehousing?

Data warehousing is a data management process of centralizing and consolidating large amounts of data from multiple sources to support business intelligence and advanced data analysis.

This data management system is made possible by enterprise data warehouses that centralize and consolidate data from multiple sources, including large amounts of historical data.

Powerful data integration coupled with analytical processing capabilities of data warehouses allows organizations to acquire valuable business insights from their data and improve decision-making. At the same time, a data warehouse can build a respectable historical record that is invaluable to both business analysts and data scientists.

Data added to the data warehouse doesn’t change and cannot be altered. This is why a data warehouse is often considered an organization’s single source of truth — fresh information readily available to all departments that need it.

The first data warehouses were built with on-premises servers, which still have many advantages today.

On-premises data warehouses offer improved security, governance, and better latency (time that passes between the moment data is acquired and made public). However, on-premises data warehouses tend to be complex to manage and are not as elastic when it comes to scaling to accommodate future needs.

Cloud data warehouses, on the other hand, are much more elastic as they support variable compute and storage requirements. Also, they are fully managed by their cloud platform and, as a result, much easier to use. Also, the pricing of most cloud data warehouses is more transparent as they usually have a pay-as-you-go model.

Some of the most common cloud data warehouses include:

• IBM Db2
• AWS Amazon Redshift
• Google BigQuery
• Microsoft Azure
• Snowflake

5 key benefits of data warehousing

Data warehouses offer several benefits to organizations that wish to analyze large amounts of data and extract value from it.

Most of the benefits described below stem from the four unique characteristics of data warehouses:

• Integrated — Data warehouses ensure consistency of different data types that often come from different data sources.
• Non-volatile — Once data enters the warehouse, it’s stable and doesn’t change, regardless of the source.
• Subject-oriented — Users can analyze warehouse data on a particular subject or functional area (e.g. sales).
• Time-variant — Warehouse data analytics takes into account change over time.

1. Ensures consistent data quality

A data warehousing process improves the quality and consistency of data coming from diverse sources using the ETL (extract, transform, load). In the transformation stage, data integration processes are used to remove duplicate records, convert all data into a standardized format, and correct outdated data.

2. Combines data from multiple sources

Different departments create new data through their workflows, and even within individual departments, data could exist on multiple platforms. Both scenarios prevent a consolidated view from the decision-making place, so you need central storage to keep this data unified. A data warehouse allows you to combine data from all those business processes and make it readily accessible for analysis and reporting.

3. Pulls down data silos

With the democratization of technology and heavy reliance on cloud tools, businesses run at risk of developing data silos — data systems where individual departments store data and source their information. Data warehousing can prevent those situations by regularly pulling data from disparate sources to a central repository which teams can resort to directly to get the necessary information.

4. Provides historical intelligence

To make data-driven business decisions, you often need to see how the numbers have changed over time and use those insights to make more learned predictions. Data warehouses can store historical information with much bigger date ranges than individual apps, allowing teams to summon the necessary information through a few queries.

5. Improves data security

Once inside your data warehouse, your business data doesn’t depend on the status of individual platforms anymore. If, for any reason, a vendor or service provider decides to change their policy or discontinue service, that doesn’t affect your data. For example, this is how you can protect your Google Analytics data after its standard properties stop processing data on July 1, 2023.

Data warehouse architecture

A data warehouse architecture is made up of three tiers.

1. The top tier is the front-end client that teams can use to present results through reporting tools.
2. The middle tier consists of the analytics engine that accesses and analyzes the data.
3. The bottom tier is the database server, where the data is loaded and stored.

The architecture of a data warehouse depends on the specific needs of the organization. Some of the common architectures include:

• Simple: This architecture has a basic design in which metadata, summary data, and raw data are stored in a central repository. The repository is loaded by data sources on one end, and accessed by end users on the other end for analysis, reporting, and data mining.
• Simple with a staging area: Many data warehouses add a staging area for data before it enters the warehouse to simplify data preparation. Operational data must be cleansed and transformed before it goes to storage.
• Hub and spoke: By adding data markets between the central repository and end users, organizations can customize their data warehouses to support various lines of business. Once the data is ready to use, it’s moved to the appropriate data mart.
• Sandbox: A private, secure area that allows companies to quickly explore new datasets and ways to analyze data in an “offline” mode without having to comply with the formal data warehouse rules or protocols.

Data warehouse, database, and data lakes

Businesses sometimes use a combination of databases, a data lake, and a data warehouse to store and analyze data.

So before we explain how these systems work and integrate with each other, let’s briefly explain the key points:

Data warehouse

Designed especially for data analytics to aggregate and structure large amounts of data over time. Data is organized in a tabular format according to a pre-designed schema so SQL can be used to query data.

Best used for: Online analytical processing (OLAP) — advanced data analytics when organizations need to draw historical data from multiple sources across their business environment.

Data lake

A centralized repository for all data, including structured, semi-structured, and unstructured data. Data lakes can hold raw data, the purpose of which has not yet been determined. Applications like big data analytics, full-text search, and machine learning can access data, even if it’s semi-structured or completely unstructured.

Source

Best used for: Low-cost storage for unformatted, unstructured data from multiple sources that might have a purpose in the future. Preferred by data science teams, data lakes can solve some of the often-heard data warehouse challenges, such as high operational costs.

Database

A transactional data storage system that monitors and updates tabular real-time data that is typically related to a single application built to record one targeted process. Can be structured as a transactional or operational system.

Best used for: Online transaction processing (OLTP), capturing, and storing real-time data for later reference.

A data warehouse can pull data from multiple databases to understand relationships and trends across the organization’s data.

A data lake can be an interim solution for the inexpensive storage of large amounts of disparate data formats or for machine learning purposes. After being processed, this data can be replicated in a data warehouse for greater usability throughout the departments.

Data mart vs. data warehouse - similarities and differences

A data mart is a data warehouse that serves a specific team or business department, such as marketing, sales, or product. In comparison to a data warehouse, a data mart is smaller, more focused, and might contain summarized data that best serve its targeted community of business users.

A data mart can also be designed as a subset of a data warehouse.

Let’s now take a look at how data warehouse stacks against a data mart:

Conclusion

Data warehousing is a vital part of business intelligence with a number of benefits and applications across industries.

However, to develop the full potential of your data warehouse, you need a way to connect new sources and load data without ticketing a data engineer every time.

Whatagraph solves this problem by offering a code-free way to move data from multiple sources to Google BigQuery, effectively automating the data flow so that even non-technical users can run data transfers.

Our data transfers are simple and safe to execute in just four steps:

1. Connect the destination
2. Choose the integration
3. Set the schema
4. Schedule the transfer

Once you create a transfer, you can automate the process even more by scheduling when and how often you want to load your data so it refreshes automatically without your intervention.

Should you need to report on data stored in your BigQuery warehouse, you can use Whatagraph’s visualization feature to create engaging reports and dashboards with plenty of customization options.

Learn more about Whatagraph’s point-and-click data transfers and make your data warehouse your most formidable sales asset.