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A great deal of companies all over the world are deciding to adopt advanced, IT-based business solutions in order to improve their efficiency. Big Data Analytics can significantly increase your sales, positively affect user experience and even reduce the costs of running your company (thanks to process automation). Learn what you need to know before you start building your own Big Data Architecture.

Creating Big Data Architecture for business is never simple. However, Big Data Analytics can do wonders for your company — there is no doubt about that. But how do you start building it? What are the most important components of this architecture? What tools will you need and how do you ensure that your Big Data Architecture will be good enough to help you solve your business problems? 

Why should you consider building Big Data Architecture for your organisation?

You need some Big Data Architecture if you want to use Big Data Analytics. Now, you can install Big Data systems and tools in on-premises data centres – that is the traditional approach – or decide to leverage Big Data As a Service and depend on a cloud solution provider to grant you access to data platforms and tools stored in the cloud. 

If you want to benefit from Big Data, probably sooner or later you’ll have to invest in some Big Data infrastructure, as the architecture created for dealing with Big Data is rather complex. The thing is, without it, you will not be as flexible as other companies which have already taken that step. We’ve already mentioned several times in our articles how Big Data Analytics can be applied in marketing and other fields of business. Developing your own Big Data Architecture is a huge change for your company, but it will make it more mature and agile. 

Big Data analytics architecture – components

If you do a little research, you’ll soon realize that the description of Big Data architecture components differs depending on the author. After analysing it, though, we should all agree that such architecture consists of both layers and processes. We should ask ourselves, why do we build it at all? The answer would probably be: “To leverage big data analytics”. Big Data architecture should support your progression from receiving data from multiple sources, to gaining business insights from that data, to finally producing actual reports for non-tech-savvy users.

Layers of Big Data Architecture

There are four main big data architecture layers that exist in all big data architecture which you need to know about:

  • Sources Layer – there cannot be any reports without data — that is why good data sources are so important for any company. Real-time or batch information in various formats is arriving all the time from numerous sources (CRMs, IoT devices, applications, websites and others) in organisations all over the world. This Big Data architecture layer is capable of handling such large amounts of varied data.
  • Storage Layer – receiving data is one thing, but storing data is another matter. Data of different formats should be properly stored or modified if the chosen analytics software requires it. 
  • Analysis Layer – a special layer interacts with the storage layer to get accurate data and produce business insights. There are many big data tools necessary to perform analysis on big data. Some more advanced tools are needed, especially for analysing unstructured data.
  • Transformation layer – active analytic processing of Big Data takes place on this layer. Data are being transformed and cleaned (that includes fixing bugs in data, converting, changing format, etc.).
  • Data Visualization Layer –  finally, after performing analysis, the insights are produced. This layer is also called the report or BI (business intelligence) layer. There are various types of outputs that can be generated. A special form of output is needed for process automation, and totally different types of output are required for human users. Business Intelligence tools can be used for proper data visualization.

Big Data Architecture LayoutApart from these layers, there are also a few important processes that should be performed in well-designed big data architecture. Let’s learn what those are.

Big Data Architecture Layers – major processes 

To leverage Big Data Analytics, you have to acquire certain tools that will help you carefully plan and perform multiple processes. Here are the most important processes you have to bear in mind:

  • Data Ingestion – this is the very first process in the company’s data lifecycle. Data arrives from multiple sources, such as IoT devices, applications, chatbots and many others. At this stage, data is categorized – that ensures a smooth and efficient flow of data into the other layers of the architecture in later stages.
  • Data processing – data processing (preceded by steps such as data cleaning) is a complex process. Two types of data processing can be performed: batch processing and real-time processing. The first one involves taking data collected over a period of time from the storage layer, processing them and producing outputs. Real-time has become a very popular technique in recent years. Real-time processing software works very fast and can produce results in a short time from receiving the data.
  • Systems management – building good big data architecture requires advanced software and tools. They are crucial for performing big data analysis. Such a complex system created using many, various tools and programs has to be continuously monitored for a data science team to be capable of ensuring accurate results in the form of reliable business insights.
  • Big data governance – for organisations which deal with a large amount of sensitive data, compliance is a priority. Each instance of big data architecture should include governance provisions for data privacy and data security. There are many tools that can be used for that purpose. Specialized software is produced, for example, for the Hadoop environment, as it is a frequently chosen open source framework for many organisations. Of course, compliance and data security may also be ensured by an external service provider. Compliance policy must operate at every stage of the information lifecycle in the company.

Those are the most basic and important things you need to know about how big data architecture should look, but how do you build it for your company? 

How do you build efficient Big Data Architecture for your business?

In creating efficient big data architecture for your company, you should follow the same approach as for any other IT project. Building Big Data Architecture presents multiple challenges to the organisation, but carefully planning the whole process may help you get through it smoothly. Together with your data science team, experts hired especially for this project and external advisors can save you all the time you need to define your strategy and plan any necessary preparations. 

Define the problem 

Before you even start with the project, think of the problem that big data analytics and big data architecture is supposed to solve. Are there any alternative solutions? Make sure that the advantages of implementing the solution will yield greater value than the costs and work required to complete the project. If you are sure that big data architecture will solve a certain problem and make your business more mature, plan the whole operation. 

Select software vendors and service providers

As we mentioned at the very beginning of this article, you don’t have to do it all by yourself. Allow your data scientists to choose their preferred tools and programs. Trust them if they inform you that they need additional resources or support from more experienced data science specialists — you can always use staff augmentation to enhance your team for a particular project.

Take care of the technical details

Deployment can be on-premises, but you can also choose cloud-based solutions to gain more flexibility and reduce costs. If you decide on an on-premises approach, you need to know what capacity your business requires up-front. You also need disaster recovery solutions. It’s essential to carefully discuss those important matters with your experts.

If you don’t feel confident about building an efficient big data architecture on your own, contact us. We’ll be happy to assist you.

Check out our blog for more in-depth articles on Data Science & Advanced Analytics:


  • Paweł works with cloud computing technologies, especially as a Data Engineer on the Google Cloud Platform. His favourite areas are data pipeline performance tuning and financial optimization. He loves mountain sports and exciting cuisine.

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