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Data Warehouse Architecture: What You Need To Know

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Data Warehouse Architecture: What You Need To Know

Key Points

  • Data warehouse architecture is crucial for structuring business data in a way that enables analytics.
  • A data warehouse is a central hub for all data, consisting of structured or unstructured databases.
  • There are two main types of data warehouse architecture: traditional on-premise storage and cloud-based storage.
  • Popular cloud data vendors include Amazon Redshift, Google Cloud BigQuery, and Snowflake.
  • Challenges in data warehousing include scaling to handle large amounts of data and constantly adapting to changing business needs.

Data warehouse architecture is a concern of the utmost importance to the data-hungry companies of today. Here, we’re not talking about data analytics, per se, but rather about the techniques required to structure business data in such a way that analytics becomes possible. 

In order for analysts and other stakeholders to reliably access important data, data must first be assembled into a data warehouse system. A data warehouse is a central hub for all your data, generally consisting of a series of databases made up of either structured or unstructured data. 

Data warehouse architecture describes the design and structure of a data warehouse, particularly the hardware, and software that powers the warehouse and the process by which data is stored and retrieved. 

When executed successfully, data warehouse architecture helps organizations mature into robust business intelligence powerhouses. Proper data warehouse architecture also helps organizations save time and money on data collection, retrieval, and storage. Good data governance, then, is just good business sense. 

In today’s article, we’ll detail the strategies data engineers employ to ensure data’s orderly journey from raw material to BI-ready deliverable. Along the way, we’ll look at some of the big players in the space and discuss some helpful tips and tricks for beginners. Let’s dive in!

What is a Data Warehouse?

Companies like Facebook, Amazon, and Netflix are prime examples of businesses that process an enormous amount of data to gain insights into customer habits and trends. But none of this is possible without a solid data pipeline. And a crucial part of that pipeline is the data warehouse.

Before we dive into the different types of data warehouse architecture, let’s break down what a data warehouse is. If you have a little experience in the data pipeline, you’re probably familiar with the terms data lake, data mart, and data warehouse.

A data lake is primarily used to house unstructured raw data. Unprocessed data such as streaming insights, market data, historical data, and retail trends will often be in a data lake. This is the beginning of the pipeline, where data mining takes place. Before processing any of the data for further use, data will make its way from wherever it is recorded, and it will flow into the data lake.

The data then goes into a data warehouse, where it takes on a relational nature. Think of the key/value pairs of your typical relational database. This means that the data is more carefully curated and easily accessible to end users looking to manipulate that data for research purposes.

Many companies use both a data lake and a data warehouse in their data pipeline. Depending on how much data the company is processing and what they are doing with it, it might have other components, such as a data mart. Data marts are similar to data warehouses, but they tailor their functions to a specific business use case.

Types of Data Warehouse Architectures 

For simplicity’s sake, we’ll break down the different types of data warehouse architecture into just two. One system of data orchestration makes heavy use of on-premise data storage, while the other system relies much more on cloud services

Traditional Data Warehousing 

A traditional data warehouse denotes any on-premise data storage system that is entirely self-contained. By self-contained, we mean that the organizational data does not rely upon any off-site, third-party service to store data and maintain data integrity. 

Like any good data warehouse, a traditional system should still have a reliable way of storing, retrieving, and analyzing data. 

At the storage level, a traditional data warehouse system can include a number of different data sources—including relational databases, operational databases, and flat files. A relational database contains structured data, organized into rows and columns, that can be easily leveraged for analysis and reporting. Operational databases mainly record transactions—such as customer payments—and are less concerned with supporting analytical projects. Flat files include any data sets that show data in a plain text format, with commas or tabs acting as delimiters. 

On top of storing data, traditional warehousing methods should also outline a method for extracting data from the source. There should also be a way of transforming the data (if necessary) before being loaded onto analytics tools. Taken together, we call these steps the ETL (Extract, Transform, Load) process. 

Investor analyzing stock market investments with financial dashboard, business intelligence (BI), and key performance indicators (KPI) on smartphone and computer screens
An efficient data warehouse lets you call upon data to generate reports, dashboards, and visualizations.

The final layer of traditional architecture includes the analytical or BI component. Even if using a traditional data warehousing system, organizations should still leverage tools for generating reports, dashboards, and visualizations from the available data. 

Traditional data warehousing is becoming less and less common, but you will still see this method employed in small businesses—whether online or brick-and-mortar. Small nonprofits or local agencies also use traditional data warehousing, usually due to budget constraints. 

Cloud Data Warehousing 

These days, cloud data solutions are much more common. As the name suggests, cloud data warehousing involves storing organizational data virtually rather than on-premises. Organizations with regionally-dispersed locations and that work with large amounts of data are far more likely to use cloud services since they’re much easier to scale and are managed by a third-party provider. 

As an added benefit of using cloud data services, you also get built-in security and maintenance tools, rather than having to rely upon in-house IT and cybersecurity staff. Plus, rather than having to make a huge upfront investment in hardware and software, you can usually pay for a cloud service as you go. 

Those important distinctions aside, cloud data warehousing functions similarly to traditional warehousing—it’s just virtual rather than on-site. Otherwise, the same types of data sources come into play, as does the ETL process and the need for BI tools to leverage data effectively. 

If you’re interested in building a data warehouse on the cloud, you’ll need to know what options are available to you. There are innumerable cloud data services out there, but the following are some of the best-known products out there. 

Amazon Redshift

It shouldn’t come as any surprise that Amazon Web Services features prominently in the world of data warehousing software. AWS provides fully-managed, easy-to-use cloud data storage solutions for mid- and enterprise-level companies with complex data management needs. 

Due to its global reach and best-in-class security capabilities, Redshift is often a top choice for being incredibly fast and reliable. Because it’s pay-as-you-go, AWS Redshift is also cost-effective. In fact, G2 estimates that it’s about a tenth of the cost of traditional data warehousing methods. 

Google Cloud BigQuery 

Next in our list of cloud data warehousing possibilities comes from the original data juggernaut, BigQuery. Google’s cloud warehousing service focuses on simplicity. It eschews fine-grained customization so that users can devote more energy to turning their data into actionable business insights. 

BigQuery has its own SQL interface, so you won’t need to worry about seeking out an additional service for querying data. With the SQL interface, teams can also train and deploy machine learning applications. 

Google Cloud BigQuery is similar to AWS Redshift in terms of cost-effectiveness. Most organizations can pay as they go, or opt for a fixed-rate contract if leadership determines that to be the more economical choice. 

Snowflake 

With good reason, many data engineers consider Snowflake one of the easiest—if not the easiest—cloud data warehousing solutions to use. What makes Snowflake particularly special is that its user-friendliness does not come at the cost of advanced functionality. Indeed, pretty much every business need or use case can find expression in Snowflake’s cloud data system. 

Snowflake provides an easy way for businesses to assemble their dispersed data sets into a unified data warehouse. With its pipeline capabilities, data engineers can then use Snowflake to build a well-organized stream of data from the source to its final output—usually a BI interface like Tableau

Snowflake also has its own user-friendly SQL interface, allowing you to create full-fledged, easily customizable query scripts directly in the application. Similar to Amazon and Google, Snowflake also allows you to set fine-grained access controls so that administrators can govern user access to sensitive business data. 

As with Amazon and Google, Snowflake allows organizations to pay a variable rate based on level of usage. One will just need to be careful to instruct individual users on how to be economical with their queries. 

Person using a futuristic HUD interface screen with data and key performance indicators (KPI) for business intelligence (BI) analytics, concept, financial dashboard, technology, virtual reality (VR)
Efficient data warehousing is crucial if you want to have easy access to relevant data for your company.

Ongoing Challenges in Data Warehousing 

The bulk of this discussion has dealt with considerations you need to make when setting up a data warehouse. These considerations help you start out. But any good data engineer knows that data warehousing is not a “one-and-done” operation. Data warehousing is an evolving process that changes in tandem with the organization. 

Scale

A key challenge with data warehousing is always going to be scale. Scale, after all, is why data warehousing came about in the first place. As businesses came to rely more and more upon quick access to large amounts of data, the concept of the data warehouse stepped in to meet this challenge. 

If ever-increasing scale and complexity were a challenge at the genesis of data warehousing, this reality would be even more pressing today. These days, many businesses need to be able to handle perhaps petabytes of data—and to be able to access the bulk of that data in real-time. You can’t do this job without a well-oiled data warehouse. 

A competent data engineer will need to constantly monitor their warehouse to ensure that it’s continuing to meet business demand effectively. You can easily do this by setting up automated testing and monitoring capabilities. All the major cloud data vendors have such tools built in to their services. 

If you’re constantly having to debug pipeline failures or other errors in the data system, it might be time to overhaul your data warehouse architecture. It’s natural to have to adapt to changing business realities. In fact, if your data is becoming larger and more unwieldy, you could probably take that as a good sign that your business is growing. 

As long as every component of your fundamental architecture is sound, scaling your system up or down—and adding new features here and there—shouldn’t be too much of a lift. Your service provider may even be able to help you come up with solutions. There are also incredibly helpful online communities like Github and Stack Overflow, where developers share challenges they ran into and any solutions they came up with to overcome blockers. 

Summary Table

Data Warehouse ComponentDescription
Data LakeUsed to house unstructured raw data such as streaming insights, market data, historical data, and retail trends. This is the beginning of the pipeline, where data mining takes place.
Data WarehouseThe data then goes into a data warehouse, where it takes on a relational nature. This means that the data is more carefully curated and easily accessible to end users looking to manipulate that data for research purposes.
Data MartData marts are similar to data warehouses, but they tailor their functions to a specific business use case.
Traditional Data WarehousingAny on-premise data storage system that is entirely self-contained. The organizational data does not rely upon any off-site, third-party service to store data and maintain data integrity.
Cloud Data WarehousingStoring organizational data virtually rather than on-premises. Organizations with regionally-dispersed locations and that work with large amounts of data are far more likely to use cloud services since they’re much easier to scale and are managed by a third-party provider.
Amazon RedshiftAWS provides fully-managed, easy-to-use cloud data storage solutions for mid- and enterprise-level companies with complex data management needs.
Google Cloud BigQueryGoogle’s cloud warehousing service focuses on simplicity. It eschews fine-grained customization so that users can devote more energy to turning their data into actionable business insights.
SnowflakeConsidered one of the easiest cloud data warehousing solutions to use. It provides an easy way for businesses to assemble their dispersed data sets into a unified data warehouse.

Frequently Asked Questions

What is a data warehouse?

A data warehouse consolidates data from multiple sources into a relational format for easy querying. It is a critical part of the data pipeline that allows you to analyze business data more closely than less-structured formats such as a data lake.

What is the 3 tier architecture of ETL?

It’s a process where data is extracted from sources, transformed for analysis, and then loaded into the data warehouse.

What is OLAP in data warehousing?

OLAP (short for online analytical processing), lets users dive deep into multidimensional data for insights and decision-making.

What is the difference between OLAP and OLTP?

OLAP is for complex data analysis, like in data warehouses, while OLTP handles daily transactions in regular databases.

What are the 5 key components of a data warehouse?

The five key components of data warehouses can be broken up into five distinct layers: the collection layer, storage layer, processing layer, analytics layer, and application layer.

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