What Is Big Data Architecture?

Written by Coursera Staff • Updated on

Big data is key for strategic planning and effective business decision-making. Learn more about big data architecture, the different types of big data architecture frameworks, and how to choose the right big data architecture strategy.

[Featured Image] A big data architect sits in her office and talks on the phone to a colleague about big data architecture.

Big data is important for businesses that want to make data-driven decisions. It allows companies and organizations to identify trends, patterns, and correlations that provide insight for strategic planning. However, big data sets are typically so complex and large that traditional database setups cannot store, manage, or analyze them. Big data architecture solves this issue by providing a framework capable of taking, storing, processing, and analyzing these sets. 

Read more to learn about big data architecture, the different types of frameworks, and how to choose the right big data architecture strategy for your business.

Read more: What Is Big Data? A Layperson's Guide

What is big data architecture?

Big data architecture is a framework built out of different tools and techniques that have the ability to ingest, store, process, and analyze big data sets. Big data is unique due to its variety, velocity, and volume. These three factors typically mean the big data sets are too complex, fast-growing, and large for a traditional computer database to handle successfully. Regarding your own business, you can use big data sets with advanced analytics—such as machine learning and predictive modeling—to assist with solving problems and making business decisions.

Read more: Big Data Examples: 6 Ways Big Data Can Change Your Business

What is big data architecture used for?

Big data architecture supports big data sets' intake, processing, storage, and analysis. It allows businesses to gain understanding and insight in real-time, allowing executives to maximize the value of the data itself. 

The design of a big data architecture framework depends on a business’s needs and goals. These goals determine the strategy used, such as which types of analytics to run or the most ideal way to store the data within the framework.

How does big data architecture work?

Big data architecture utilizes several components to create a framework that supports the ingestion, processing, storage, and analysis of massive data sets. These frameworks include non-relational databases that allow for the storage of unstructured data. Some other components of big data architecture framework include:

  • Data sources: These are the various places big data sets pull from and then ingest into the big data architecture framework. Data sources include social media, websites, and the Internet.

  • Data storage: Data storage refers to the software framework capable of holding massive amounts of either structured or unstructured data. Storing large volumes of structured or unstructured data is frequently labeled a data lake.

  • Batch processing: This is the software used to sort and convert the big data sets into usable files that are ready for analysis.

  • Real-time message ingestion: This part of big data architecture places the data into groups, allowing an easier shift into the subsequent stages of the storage process. Essentially, this procedure permits the ingestion and storage of streaming data, which needs real-time processing.

  • Stream processing: This process takes the real-time messages, filters them, and prepares them for analysis by adding the processed messages to an output sink.

  • Analytical datastore: This is where the processed and cleaned data becomes accessible for use by different analytical techniques and tools.

  • Analysis and reporting: This is the part of the framework where software inspects the analyzed data for insights, patterns, and trends. Next, these results transfer to the reporting mechanism, preparing them for human viewing. You can then utilize this information to make more effective decisions for your business.

  • Orchestration: Orchestration is the software that allows the aforementioned processes to run on automated loops, so you're not required to handle each step manually as data comes in.

The different types of big data architecture frameworks you'll use depend entirely on your business’s needs and goals. These frameworks include:

  • Batch architecture: Handles data in a sequential format

  • Lambda and kappa architectures: Methods for handling streaming data as it arrives

  • Streaming architecture: Handles data in almost real-time

  • Hybrid architecture: Uses both batch and streaming processes

Who uses big data architecture?

If you want your business to make data-driven decisions, you can use big data architecture to support your data sets. It lets your business gain unique insights, plan innovatively, and make informed decisions. Industries such as business analytics, science, engineering, and medicine use big data architecture.

Pros and cons of using big data architecture

The pros and cons of big data architecture are important to consider when implementing a big data architecture strategy in your business.

Here are some advantages to consider:

  • Big data architecture's flexibility allows businesses to scale up or down as needed.

  • The insights gained from big data are only possible due to big data architecture and can help increase your company’s efficiency and productivity while enhancing customer service. 

  • Big data architecture uses a strategy called parallel computing, which provides the ability to perform multiple calculations at once. Doing this can benefit your company in terms of efficiency, and as a result, you can have more time for other projects and reduce your spending.

Below are some limitations to consider:

  • Big data architecture has security risks since the data exists in a lake that’s accessible to multiple users in your company, thereby making it difficult to keep secure.

  • If the architecture you’ve chosen isn’t robust enough, the volume of data sets may quickly become unmanageable.

  • The complexity of the data itself makes it difficult to choose an ideal architecture that might require constant tweaking and adjusting.

How to get started in big data architecture

If you’re interested in a career involving big data architecture, the first step is to familiarize yourself with it and big data. That way, you’ll learn more about the foundational knowledge and prepare yourself for a position within this field.

Depending on your coursework, a bachelor's degree in computer science or informational technology will provide a basic understanding of big data. IT experience is also helpful in gaining the necessary skill set to acquire a job in big data architecture. Additionally, you’ll want to use online courses, a degree, or certification to develop your programming language skills.

Once completing your degree, you may want to become a big data architect, a professional responsible for creating and maintaining big data frameworks in different industries. These databases allow businesses to store, process, and analyze big data sets. If you decide to pursue this profession, you can have the opportunity to earn a great salary. According to Glassdoor, the average annual base salary for a big data architect in the US is $134,132 [1].

Getting started with Coursera

Sharpen your big data architecture skills and learn more about a career in big data with courses on Coursera. With options like UC San Diego’s Big Data Specialization, you’ll discover more about big data and how to develop those skills for a promising career. This program covers topics like bid data integration, processing, and analytics. Upon completion, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.

Article sources

  1. Glassdoor. “How Much Does a Data Architect Make?, https://www.glassdoor.com/Salaries/data-architect-salary-SRCH_KO0,14.htm.” Accessed March 19, 2024.

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