The Data Warehouse: Key Benefits and Drawbacks


Data warehouses are a safe bet when it comes to data storage and management options. Until recently, data warehousing was the only method of enterprise-level data storage. They stood the test of time, which is why we still see them today.

However, data warehouses aren’t perfect, as they have some drawbacks. This article will help you decide if data warehousing is the right choice for your business, or if you should look into data lakes instead.

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What are data warehouses?

A data warehouse is a data collection and management system with a “traditional” structure. Data warehouses collect data from all sources across a company, consolidating it and storing it centrally but in a logically sound, hierarchical way.

Data warehouse’s architecture has a tiered structure:

  • The bottom tier is the server level, containing all the data.
  • The middle tier belongs to the data processing and analytics engine.
  • The top tier is the actual data user, for example, an analyst who wants to access the information using a BI platform.

The tiered architecture is a traditional form of storing and managing data. This is how we usually sort data in our daily lives: DRIVE C > My Pictures > Holiday 2021 > 2021_6_11.jpg.

But, based on the usage frequency and importance of single data pieces, some can be stored in fast storage, making them easy to access. Data you don’t use that often can be stored in slower, cheaper storage, making the whole warehouse more economical.

Benefits of data warehouses

Single source of truth

The main benefit of data warehousing comes from its tiered structure. Because every piece of data is processed upon storage and assigned a place in the hierarchy with a single role, data warehouses are exceptionally reliable.

This is one of the reasons why some traditional organizations of high importance use data warehouses — airlines, banks, healthcare institutions, and retail chains.

Historical data analysis

Because of this type of architecture and data organization within databases, data warehouses keep historical data usage records, which can be incredibly important to big data analysts. Looking at changes over time gives incredible insight to organizations, especially knowing that the data collected is curated and accurate, like in the case of data warehouses.

Excellent performance and scalability

Because data is processed on arrival and organized in a logically sound way and has pre-determined roles, the performance will never suffer. The end-user will always be able to get the necessary information in a matter of minutes.

Moreover, the organized, tiered structure ensures the data will remain just as accessible as the warehouse grows, making it easy to scale. This means you will never have to worry about your data management system bottle necking your company’s growth, no matter how much information you gather as time passes.

Improved security and data integrity

Because data warehouses serve processed data, databases will be less error-prone and more secure. Plus, if a bug does happen, the tiered structure makes things easier to troubleshoot, and the problem will affect fewer functions.

Drawbacks of data warehouses

Low flexibility

First, because of the architecture, data warehouses are only useful for holding structured data. If your business collects a lot of unstructured, raw data, data warehouses aren’t ideal.

Furthermore, once rules are set, it becomes challenging to introduce changes to your data warehouse in terms of ranges, queries, and indexes. This is especially true once the amount of collected and processed data grows, which is why it is imperative to set everything up on solid foundations early.

Setup takes a lot of knowledge and experience

While the data warehouse structure is tiered, which is somewhat logical, the lack of flexibility means you will need to set everything up in advance. That’s why it’s imperative to contact a data warehouse expert with the necessary experience to set up and future-proof your data management system.

Costs grow rapidly

Data warehouses scale well and have excellent reliability and performance, but this comes at a cost. This type of data management is very resource-intensive, as it requires a lot of (processing) power to serve data in this ready-to-use way immediately.

Also, as the amount of data grows, it will become increasingly difficult to retain the same amount of data-use rules across the organization. That makes setting procedures and training more difficult, also increasing costs.

Types of data warehouses

Enterprise data warehouse

Enterprise data warehouses (EDW) are the centralized database of the whole organization. Every data source across the organization is connected to an EDW, processed, and stored there.

Having every data piece a business collects in a single, organized location speeds up and simplifies the decision-making process. EDWs allow analysts to access valuable information from any part of the business in minutes, having a complete overview of any particular model. But, business intelligence tools and spreadsheets make it easy for any people across the organization to access the data.

Operational data stores

Operational data stores (ODS) are logical areas of data warehouses that pull data in real-time from multiple sources, transforming it and loading it into a data warehouse. Because of its real-time capabilities, ODS is an excellent choice for tracking orders, monitoring customer activity, and similar BI-powered activities.

In truth, ODS is not actually a type of data warehouse but a bridge between data sources and the warehouse itself. It can hardly operate independently, primarily because it has limited capabilities in terms of query complexity and the amounts of data it can handle. Think of operational data stores as RAM, while data warehouses would be more like HDD/SSD. The first is for rapid access, while the second is for storage and organization.

Data marts

Data marts store and organize data with a particular use in mind. While EDW stores everything for the whole organization, a data mart can serve one department, for example, sales. In fact, every EDW is usually segregated in data marts to keep things more easily accessible.

Segregating data also speeds up data analysis processes within departments and saves resources. Plus, making changes in data marts is easier than trying to switch things enterprise-level.

Based on their autonomy, data marts can be dependent, independent, or hybrid. Dependent data marts only access a curated amount of data from a single source, while independent marts can operate without a central warehouse. Hybrid marts can pull data from different sources, not just warehouses, making them more flexible and better for on-the-go integrations.

Data lakes are the exact opposite of data warehouses. While warehouses store data in a tiered, hierarchical way, data lakes store everything in a single data pool.

Data warehouses vs. data lakes

Data lakes are the exact opposite of data warehouses. While warehouses store data in a tiered, hierarchical way, data lakes store everything in a single data pool.

Unlike warehouses, data lakes store data in its native, raw form. Because of this, it is ideal for businesses dealing with unstructured data. Data lakes only process data when it needs to be used, not before. This makes them far less resource-intensive, especially when the data volume isn’t large.

Furthermore, because data lakes don’t pre-process the data nor store it in any hierarchical way, they are highly flexible. Each data piece can have unlimited functions and uses, allowing you to change things on the go easily.

Keeping everything in a single place, however, has some disadvantages. As the data pool grows, it will become increasingly difficult to scale and stay organized. Plus, because data is processed only upon request, large data pools will have diminished performance compared to an organized warehouse.

Furthermore, keeping everything in a single place, in unstructured and native form, carries security risks. Staying on top of every piece of information in a data lake becomes increasingly difficult as it grows. Keeping everything updated is harder, let alone troubleshooting problems.

Moreover, data analysts prefer the ability of data warehouses to hold historical data, allowing them to make advanced business decisions on the changes over time. On the other hand, data lakes don’t have such capabilities, which is why they are primarily used by data scientists and developers, not analysts.

Bottom line

Choosing how to store and manage your data is one of the most critical business decisions you will make. Although it may not seem that important at first, as the amount of data grows, the effects of your choice will start showing, both good and bad. For this reason, it’s essential to choose wisely, as early as possible, and set up your databases professionally.

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