What is a Data Platform Architecture?

What it means, why it matters, and best practices. This article provides definitions and insights into data platform architectures.

What is a data platform architecture? How does it help enterprises?

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 your 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 survives 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. What is a data platform? Simply put, 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 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:

  1. Data Ingestion Layer
  2. Data Storage Layer
  3. Data Processing Layer
  4. User Interface Layer
  5. Data Pipeline Layer

As you can see, 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 are 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.

Data platform architecture diagram

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. 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. It’s 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. After that comes the processing layer, where the data is cleaned and manipulated based on the needs of the business. 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. 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 observability is critical in any data analytics architecture.

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 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 the 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 organziation 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 a 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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