Exploring Different Types of Data Architecture: Lake, Mesh, Vault, Warehouse

Data drives the modern world, and all business today depends on it. In this blog post we’ll explore some different types of data architecture you might employ to organize your own data—and why a modern organization scheme can benefit your business.
December 14, 2022
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To keep up in a fast-paced modern economy, businesses need to modernize their data architecture ASAP. Such modernization efforts help in the digital transformation process, keeping companies limber enough to respond to market changes on a dime.

The need to shift away from legacy processes is not lost on these businesses, either. One Deloitte study found that 84% of surveyed companies have already started on the path to updated systems. If you’re still part of the 16% that hasn’t started the shift, it’s time to start now—or risk being left behind. But what sort of data architecture works best for your business? Let’s explore some of the more common modern structures, so you can position your company for success.

data architecture
Source: Shutterstock

What is Data Architecture?

In the broadest sense of the term, data architecture is a framework for gathering, storing, and integrating data. How you structure your data collection and organization directly influences your business strategy; therefore, it’s important that you do so in a helpful way.

Your data architects should root your model and underlying structures in your company’s vision, business strategies, business rules and standards. Modern data architecture is key for building a limber, competitive organization.

Why is A Modern Data Architecture So Important?

Companies collect a massive amount of data every day—and the amount is only on the increase. But you need a modernized data architecture in order to make the most of all this information.

We live in an Information Age, where consumer data—and wielding it efficiently—is the coin of the realm. A robust, streamlined structure to neatly organize and access this information gives you the agility to respond to market changes and evolve as an organization, instead of drowning in data overload.

How can upgrading your system boost your business?

Real-Time Processing

In a fast-paced digital environment, companies need to make decisions quickly. Therefore, you need access to the most up-to-date information. Traditional batch processing methods simply can’t keep up with the deluge of information in the modern age—both when it comes to collecting data and using it to make decisions. A modernized data architecture enables real-time processing, which allows you to optimize and automate more of your business processes.

Support for Artificial Intelligence Capabilities

Artificial intelligence (AI) is everywhere these days, creeping into business processes more and more. You too can use AI to enhance your operations—but only with a robust, constantly updating data set. Here are some of the ways that AI can offload more manual, time-consuming tasks from you and your staff:

  • Automating customer service tasks, e.g. identifying and providing solutions for customer issues
  • Improved accuracy for sales and marketing forecasts, allowing for more efficient campaigns and resource allocation
  • Take over business processes by identifying and responding to patterns in a business environment, and making decisions accordingly
  • Optimizing inventory management by predicting future demand based on previous data, e.g. customer behavior and preferences, and improve stock management
  • Automating administrative tasks such as filing documents, scheduling appointments, etc. that would otherwise bog down staff’s time and resources

Data Democratization

Data democratization refers to the process of making data available to the average end user—with no gatekeeper. Democratization gives everyone access to data at any time, which translates to faster and more accurate decision-making. The key to successful democratization is aligning the data with the end user’s needs.

While data warehouses continue to be crucial in addressing the need for “raw” data and curation, businesses need to employ other data architecture types to help democratize the flow of information in their business. You need a multi-modal data infrastructure—one mode to serve real-time needs for decision-making and the other to cover analytical needs for operational purposes.

Types of Data Architecture

Data architecture can mean different things to different organizations. Depending on the implementation, use cases, and other factors, data and the underlying architecture can take many forms. Many of these architectures can be used in combination with one another, forming parts of a more robust whole.

Let’s explore some ways you might organize your own data for a more efficient, comprehensive architecture.

Lake

A data lake is a data management framework for storing, processing, and securing large amounts of raw structured, semistructured and unstructured data. This framework supports storing the data in its native format of any size.

Data lakes are beneficial for storing raw data, where the purpose is not yet determined—you can use this information for quick analysis, or to build complex AI models.

By nature, data lakes need massive storage volumes. The danger in storing all that raw data is that the information can get cluttered and unusable—you need strong data quality and governance policies to make data lakes work for you.

Warehouse

Data warehouses store large collections of business data, consolidating data from multiple systems such as sales, marketing, customer-facing apps and application programming interfaces (APIs). Companies can then use business data analytics tools to glean strategic insights and support decision-making.

Data warehouses differ from data lakes in that warehouses store processed data. The information in a warehouse has a predefined purpose.

Vault

A data vault is a data modeling approach that makes the data architecture agile, flexible and scalable. A vault separates the data’s structural information (primary keys and foreign relations) from its attributes. This approach was created to support storing historical information, and parallel loading and allows the organization to scale without needing to redesign the entire solution.

One common challenge with traditional warehouses is that the information needs to be processed before loading it. This processing involves extracting the information from the data source and transforming it into standard formats. Some organizations may have several data sources with unique structures; companies would need to create a complex schema within a data warehouse to unify these sources.

The vault solves this problem by storing raw data without processing it. Instead, data transformation takes place on demand.

Mesh

For business units to access important information quickly, you want to reduce bottlenecks as much as possible. The centralized nature of the data warehouse inhibits this access. A data mesh organizes data by business domain—for example, sales, customer service, or human resources. Distributing access in this way makes data a self-service function across the company.

The data mesh pairs particularly well with a data vault architecture; storing data in raw form gives each business domain the freedom to extract and work with the data in a way that meets their needs.

data architecture
Source: Shutterstock

Modernize Data Architecture, the Hakkoda Way

It’s impossible to escape the presence of data in the modern world. By extracting information from systems in a more trustworthy manner, modernizing data promotes well-informed decision-making. However, data modernization is also difficult and time-consuming—especially when done in-house.

Enter Hakkoda. Here at Hakkoda, we handle the complexities of data storage, ensuring your architecture supports the capabilities needed to achieve your company’s strategic goals. Our process takes a big-picture approach that considers the data pipelines, speed, quality, and performance requirements for your organization. We’ll modernize your data capabilities by helping you:

  • Modernize your ingestion process with ELT
  • Implement streaming data
  • Implement DataOps processes such as embedding data observability and automation into your pipelines
  • Create unified data storage to support structured and unstructured data
  • Improve collaboration with CI/CD processes
  • Add a metrics store for analytics and a feature store for machine learning
  • Implement a data catalog across the entire platform

Want to learn more about how Hakkoda can help modernize your data architecture? Contact one of our data experts today.

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