What is a Data Platform Architecture?

January 8, 2024

In today's fast-paced business environment, companies need to leverage the power of data to gain a competitive edge. However, many organizations struggle to keep up with the changing technological landscape, particularly when it comes to their data platform architecture. 

Traditional data platform architectures can no longer meet the demands of modern enterprises. Modernizing your data platform architecture is crucial to improving performance, reliability, and scalability. 

In this article, we will explore the importance of upgrading your data platform architecture and how it can help your enterprise. 

Understanding Traditional Data Platform Architecture

Data platform architecture

Traditional data platform architectures are designed for a different era. They are typically monolithic, rigid, have data stored in silos, and are designed for on-premises environments. Traditional architectures have several challenges, including:

Inflexibility 

Traditional architectures are difficult to scale, maintain, and upgrade. They are often expensive to operate and can only handle limited data types and formats. This can create data silos and result in a lack of interoperability, making it difficult to extract value from data.

Limited Scalability

Traditional data platform architectures are not designed to handle the massive amounts of data generated by modern applications. As a result, they can be slow and unresponsive, limiting scalability and agility.

Compromised Performance

Modern-day businesses are gaining access to far more information as compared to previous years. The traditional data platforms are not designed adequately to process the growing volumes of data, which affects their performance and causes delays in reporting. 

Outdated Technology 

Many legacy data platform architectures are built on rigid platforms that can not be updated. A lack of updated technology causes businesses to miss out on remarkable innovations. Data platforms need to ingest increasing volume and variety of data, therefore they need to be quicker than ever. 

Lacks Governance

Data governance refers to the ability of decision-making for matters related to information. It creates a synergy between technology, processes, and people, which helps organizations in getting the best out of the data. Traditional data platform architectures lack data governance which can lead to inaccurate data being used in decision-making. 

What is a Data Platform Architecture? 

Data platform architecture refers to the different layers or components that make up effective data management. Your solution or set of solutions that make up your data platform needs to be able to service each layer of the data platform architecture. 

According to data architecture principles, there are five main layers that your platform must cover:

  • Data Ingestion Layer
  • Data Storage Layer
  • Data Processing Layer
  • User Interface Layer
  • Data Pipeline Layer

The data architecture best practices focus on the comprehensive management of data. Looking at a data architecture diagram can help you understand how these layers interact with each other as well so that you can better understand the tools you need in order to build a successful data architecture framework

Due to the size of a task like this, few offerings can actually cover every single layer as part of one software solution. The ones that claim to be frequently extremely expensive and impractically designed for the size of data that needs to be processed. That’s why many organizations tend to rely on several tools that can be used in conjunction with one another to service each of these layers. 

For instance, a database tool like Oracle Database might be used for the data storage layer, while a tool like Snowflake or Splunk can provide digestible charts for the user interface layer. Whatever tools you use, it’s important to know how data architecture works so that you can manage your data better.

How Does Data Platform Architecture Help? 

Every day, the amount of data that enterprises have to manage increases. The ability to effectively organize, secure, and analyze this data can give your business the competitive edge it needs to achieve its big goals. However, accomplishing this can be difficult.

Many organizations have to deal with thousands of data sources constantly inputting hundreds of data points every day. This raw data is then fed through a complex data pipeline in which, at any point, the data could be erroneously altered or its quality reduced. Finally, if the data survive that journey, it then needs to be correctly stored so that analytics programs can easily retrieve it and display helpful insights to business leaders so that they can make better decisions.

It should be clear that managing data manually is not a good idea. That’s why many organizations have already chosen to implement some form of data platform. 

A data platform consists of the functionalities and tools that an organization needs to manage data from the source to the user so that the business can become truly data-driven. In order to build a successful data platform, you need to understand data platform architecture.

Data Platform Architecture Diagram

Data platform architecture

Reviewing a data architecture example can also be a great way to learn more about the different layers that make up solid data management. A data platform architecture diagram shows all the different components and service areas that make up effective data management. Specifically, these diagrams will look different for each organization because each organization deals with different sources and has different data pipelines. Below mentioned are different layers of data platform architecture.  

Data Ingestion Layer

Any data architecture diagram example will often start with the ingestion layer. This is where it all begins. The data ingestion layer provides the connection between the source systems producing the raw data. 

Data Storage Layer

This is the second layer and then very likely that a data architecture diagram template will include the data storage layer. The name of this layer is pretty self-explanatory. The purpose of this layer is to store the data for processing and analysis. 

Data Processing Layer

After that comes the processing layer, where the data is cleaned and manipulated based on the needs of the business. 

Data Interface Layer

Once the data is processed, it is then piped to user interface applications where business leaders can use graphs and charts to analyze it and derive insights that can drive decision-making in the business. 

Data Pipeline Layer

The final layer that makes up a data architecture strategy and underlays this entire process is the data pipeline layer. The data pipeline layer is the one responsible for maintaining a constant flow of data throughout all of these layers. 

One tool that would fall under the category of the data pipeline is the Acceldata data observability platform. It enables you to increase pipeline efficiency and reliability while also being able to track the data journey from origin to consumption. This kind of data observability is critical in any data analytics architecture for adequate data quality management.

Data Architecture Tutorial

Of course, knowing all the details of what a data architecture is and how it works is only part of the solution to data management. Even utilizing the right tools for each layer or service area will still not get you across the finish line. In order to truly manage your data successfully, you need people. 

Everything we’ve discussed requires a solid data team that includes data platform architects, data analysts, data security professionals, and more. Finding these people can be a whole separate challenge. There are many data platform architecture jobs. However, the industry is facing the same challenges as many others. Plenty of jobs, but not enough people to fill them. According to PayScale, the average data architect's salary is $123,332. As the overall market becomes more and more intense, companies will need to offer even more than the average if they want to be able to secure the best people for their architect jobs and keep them. 

The typical data platform architecture job description includes responsibilities that cover all of the areas in data architecture. This means that they have storage responsibilities and build databases, but they may also have responsibilities surrounding the processing or even security layer where they use tools to manage the data for the organization. 

Sometimes, it makes sense to train people already within your organization with a data architecture tutorial. With data platform jobs experiencing high demand among enterprises, it’s been increasingly difficult to find and keep the right people. That’s why any solution that provides automation can be a major help to the data management space. Acceldata Torch automates data quality, so your people don’t need to spend valuable time on tedious tasks.

Modern Data Platform Architecture

Many people wonder, what makes a modern data platform architecture. Fundamentally, a truly modern data platform is one that enables a business to become fully data-driven. Despite the growing volume of data businesses are handling, this still remains an unrealized goal for many organizations. 

Sometimes, they may extract valuable insight from their data that allows them to get ahead, but then their competitor may uncover another trend and beat them. The only way to stay consistently ahead of the competition is to consistently analyze your data and extract insights that can power decision-making in your business. 

The AWS modern data platform admits that a one-size-fits-all approach to data analytics forces organizations to compromise on the quality of their management. According to AWS, modern data architecture principles should focus on flexibility, scalability, and performance. 

At the end of the day, modern data architecture examples show that the right combination of knowledge, tools, and people can result in amazing benefits for businesses and organizations in a variety of industries.

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