What is a Data Warehouse? Everything You Need to Know

As a marketer or business analyst, you know that data is an important part of your success. And the way in which you store and organize your data makes your work easier or more difficult.

There are many ways to store data, one of which is data warehousing. This is an excellent option for businesses that need to view large amounts of data from multiple sources. Today we’re going to learn what a data warehouse is and how it can help you analyze your data.

With a data warehouse, you can query and look at historical data over time to improve decision-making. The most important people in a company who will use data warehouses are data scientists and business analysts.

A data warehouse draws data from multiple sources, including relational databases or transactional systems. To access the data, analysts use business intelligence tools to analyze, data mining, create visualizations, and generate reports. With data constantly evolving, it is imperative for businesses to leverage data to stay competitive.

What is the bottom line of a data warehouse?

The ultimate result of a data warehouse is to gain insights, monitor performance, and improve decision-making. Using reports, dashboards, and visualizations, analysts have all the tools they need to make the right decisions.

Benefits of Using a Data Warehouse

1. Historical data.

One of the main advantages of data warehouses is the ability to look at a large amount of historical data over time. With a data warehouse, you can consolidate a large amount of data from many sources to better make your business decisions. By looking at historical data, you can analyze trends over time and develop effective strategies.

2. Data from multiple sources.

In addition, a data warehouse gives you data from multiple sources, so you can get a more complete picture as you analyze the information. With something like a data mart, you only get data from a single subject, as opposed to data warehouses, which are supposed to process and organize data from multiple sources.

3. Stability.

Data warehouses are also more stable data sources that you can use to look at data at a high or granular level. This gives you the flexibility to look closely at data and perform queries quickly. A data warehouse has high quality data because it comes from multiple sources, is consistent, and more accurate.

What data warehouses are not

When you first hear the term “data warehouse” you might think of a few other data terms such as “data lake”, “database”, or “data mart”. However, these things are different because they are of a more limited scope. Although they can perform a similar function, the structure is different. Let’s dive in below.

Data lake vs. data warehouse

A data lake stores unfiltered data from multiple sources that are used for a specific purpose. That means you’re looking at raw data from social media or an app. The data sets are created at the time of analysis. This is inexpensive storage for raw, unstructured data.

On the other hand, data warehouses are used to analyze and process data. The data is already collected and contextualized in a data warehouse and is ready for analysis. Ultimately, it is a more advanced data storage tool that can use large amounts of historical data.

Data mart vs. data warehouse

A data mart is a subset of a data warehouse. Usually they are designed to easily provide specific data to a specific user for a specific application. Data marts are inherently one topic, while data warehouses cover several topics.

Database vs. data warehouse

Databases are often confused with data warehouses because they serve a similar purpose. The difference, however, is that databases are not intended to be used to analyze a large collection of data. Databases are used to record and retrieve data, while data warehouses are used to analyze large amounts of data. Think of it this way: Data warehouses store data from multiple databases.

Data warehouse architecture

A data warehouse architecture is a method by which you organize, communicate, and present your data.

You can use a base architecture, a staging area, or a staging area and data marts.

This means that you can get a data warehouse into its data and then users can see the reporting and analysis. Or you can have the data broken down in data marts before users look at the analysis and reporting.

The staging area that you see in some of the images below is used to cleanse and process data before it is taken to a warehouse. This simplifies data preparation. To get an idea of ​​what these look like, take a look at the pictures below.

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Data warehouse software

1. Snowflake Data Warehouse

Snowflake Data Warehouse is a data platform based on the cloud infrastructure. This is a great option for businesses that don’t have the resources to support internal servers.

With Snowflake, users can pay for storage and easily share data. As a data consumer, data provider, and data service provider, you can seamlessly mobilize data across public clouds. This software helps you democratize data analysis in your company so that all users with different levels of expertise can make data-based decisions.

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2. MarkLogic

This data warehouse solution enables you to perform complex searches on various types of data, including documents, relationships, and metadata. MarkLogic is a fully managed, fully automated cloud service for integrating data from silos.

3. Oracle

Oracle Autonomous Data Warehouse is a fully managed database optimized and optimized for data warehouse workloads with the performance of Oracle Database. It offers a new, comprehensive cloud experience for data warehousing that is simple, fast and flexible.

While data solutions may seem overwhelming, they are important to your day-to-day business decisions. With a data warehouse, you can simplify your data storage, management, and analysis.