Data Architecture 101: Trends and Terms to Know

Author: Tien Doan

In the modern digital world, data has become one of the most valuable resources for businesses. It is the driving force behind informed decision-making, innovation, and growth. With large volumes of data being generated and collected daily in every industry, having a well-constructed data architecture and framework to access, store, and analyze that data is critical for companies that want to drive business growth and success.


What is data architecture and why is it important?

Data architecture refers to the organization and management of data within a company. A modern data architecture encompasses the processes, standards, and technologies used to create a comprehensive framework for data storage, access, and analysis. The main components are:


  • Data sources: All the different systems, applications, and devices that generate and capture data.
  • Data ingestion and pipelines: The structures that collect and ingest data from various sources into central repositories.
  • Data platforms: The primary vehicles for storing and organizing data. Typical platform types include traditional relational databases, data warehouses, data lakes, and data lakehouses.
  • Data processing: The systems that transform and analyze data to extract insight.
  • Data visualization: The reports and dashboards that help users make sense of the data and insights.


No one architecture is best for every company, and many have a complex ecosystem of analytics tools, technologies, processes, pipelines, and storage repositories.


Regardless of the platforms used, leveraging data assets is a key component to any organization’s success, so developing and implementing the right architecture is important for a variety of reasons:


  • Improved accessibility: It enables organizations to manage their data more efficiently, make it more accessible, share globally, and ensure that the data is consistent, accurate, and current.
  • Better decision-making: It provides greater access to larger data volumes, which allows businesses to accurately report and analyze past performance and predict trends for the future.
  • Reduced costs: It can help reduce costs associated with data management, storage, and retrieval.
  • Improved data security: It facilitates the implementation of security measures like encryption, access controls, and data backups to ensure that data is secure and protected.
  • Increased agility: It can help organizations provide an adaptable and scalable framework for data management, making it easier to add new technologies in response to changing business needs.


Today’s data architecture trends

Businesses may choose from a wide variety of architecture approaches depending on their goals, technologies, and business structure. Two of the most popular architectures are data mesh and data fabric. Both aim to increase efficiency for the teams that need access to critical data, but there are pros and cons to each depending on the structure and needs of the organization.


Data fabric

A data fabric may be suitable if your organization has multiple data sources and needs to simplify data integration and management. This is a more centralized, metadata-driven approach that involves creating a unified and integrated view of data across multiple systems and data sources. With a data fabric, data can be accessed by any application or user, regardless of where the data resides, without having to move or copy the data. The main benefit of a data fabric is that it emphasizes centralized governance and integration, ensuring that data is consistent, accurate, and secure.


Data mesh

A data mesh may be ideal if your organization needs to scale data management and analysis capabilities while maintaining agility. Data mesh is a decentralized approach that leverages teams’ expertise, allowing each to take ownership of their data domains and source data based on their own requirements to provide a self-service approach to data management and analysis.


The benefit of a data mesh is that it enables organizations to remove data silos, making analyses more efficient and easier to modify based on changing business needs.


Selecting data platform(s) as part of your data architecture

In addition to selecting the best architectural approach for your business’ data needs, your architecture will need one or more data platforms to consolidate your data sources, store your data, and organize it for reporting.


The types of data platforms a company selects will play a critical role in determining its analytics capacity and the way it builds internal ETL and data pipelines. Determining which are best for your company depends on a variety of factors, such as the type of data you are collecting, the volume of data, the use cases for the data, the resources, and what you hope to accomplish using your data. There are benefits and drawbacks to each platform type.


Data warehouses

A data warehouse may be suitable if your organization requires historical data for trend analysis and predictive modeling. Data warehouses are comprehensive, hierarchical systems that can pull in recent and historical data from internal and external sources.


The benefits of a data warehouse include improved data quality, increased data consistency, and better data governance. They also enable faster and more accurate reporting and analysis. However, data warehouses are expensive to build and maintain, and they can be challenging to scale and adapt to changing business requirements.


Data lakes

A data lake may be appropriate if your organization requires agile data processing and analysis. Unlike hierarchical data warehouses, which require data to be pre-processed and structured before being loaded in, data lakes have a flat architecture and allow data to be stored in its original form, without any pre-processing or transformation. A data lake is a centralized repository for storing current and historical raw data from a variety of sources.


The benefits of a data lake include low-cost storage, high scalability, and flexibility. They allow organizations to store and analyze all types of data, regardless of its structure or format. However, data lakes can suffer from data quality and governance issues, as there is no pre-defined schema or data model. They also require significant effort to transform and organize data for analytics use cases.


Data lakehouses

A data lakehouse may be ideal if your organization requires real-time data processing and analysis capabilities. This approach combines the strengths of both data warehouses and data lakes. Data lakehouses store raw data in its native format and support both analytical and transactional workloads. They also allow organizations to process and analyze data in real time, providing faster insights and more agile decision-making.


The benefits of a data lakehouse include improved data governance, faster data processing, and increased agility. They allow organizations to store and analyze data in real time, with minimal ETL or data transformation overhead. However, data lakehouses can be complex to implement and manage, and they require significant investments in infrastructure and talent.


How can Wavicle help with your data architecture needs?

The correct data architecture is critical to an organization’s success, but with so many options, knowing what’s right for your company is not always easy. Careful consideration of every platform and framework with the help of data management experts can help you choose the right approach for your organization.


Wavicle’s data management experts can help you evaluate your stakeholders’ needs, your existing data infrastructure, and the tools and technologies available to develop a data architecture that best fits your business and will scale with your company in the years to come.


Contact us today to get started.