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February 28, 2022

Big Data Architecture: Definition, Processes, and Best Practices


Artur Haponik

CEO & Co-Founder

Reading time:

9 minutes

Since the early 2000s, the volume of data generated and the rate at which it is generated have increased tremendously. In 2018, more than 25 quintillion bytes of data were generated every day[1]. This trend is primarily driven by the ever-reducing cost of storing data automation in smaller devices. At this rate, even data warehouses will start getting overwhelmed with an influx of data[2].

Traditional database management systems were designed to store structured data. But with the advent of big data, such systems are becoming obsolete, thus necessitating businesses to come up with more effective means of data storage and processing. This is where big data architecture and big data consulting come in.

What is big data?

Big data is a term used to describe large volumes of data that are hard to manage. Due to its large size and complexity, traditional data management tools cannot store or process it efficiently. There are three types of big data:

  • Structured
  • Unstructured
  • Semi-structured

Structured big data can be stored, accessed, and processed in a fixed format. Although recent advancements in computer science have made it possible to process such data, experts agree that issues might arise when the data grows to a huge extent.

types of big data

Unstructured data is data whose form and structure are undefined. In addition to being large, unstructured data also poses multiple challenges in terms of processing [3]. Large organizations have data sources containing a combination of text, video, and image files. Despite having such an abundance of data, they still struggle to derive value from it due to its intricate format.

Semi-structured data contains both structured and unstructured data. At its essence, we can view semi-structured data in a structured form, but it is not clearly defined, just like in this XML file [4].

It might be interesting for you: MapReduce vs. Spark: Big data frameworks comparison

Characteristics of big data

Big data is defined by the following characteristics:

  • Volume (big data comes with a lot of information)
  • Variety (big data comes from diverse sources and in different forms)
  • Velocity (it can be generated very quickly, and it determines your company’s data potential)
  • Variability (if big data that you have is inconsistent, making use of it will be tricky, to say the least)

What is big data architecture?

Big data architecture is an intricate system designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database management systems.

Although there are several big data architecture tools [6] on the market, you still have to design the system yourself to suit your business’s unique needs. You need a big data architect to design a big data solution that caters to your unique business ecosystem.

big data architecture


That said, big data has a generic structure that applies to most businesses at a high level. You, however, don’t need all the components of a typical big data architecture diagram for successful implementation. Typical data architecture has eight layers, as discussed below.

Big data architecture layers

big data architecture layers

1. Data ingestion layer

This is the first step that big data coming from multiple sources makes on its journey to being processed. Here, the data is prioritized and categorized, enabling it to flow smoothly in the subsequent layers.

2. Data collection layer

This layer focuses primarily on transporting the data from the ingestion layer to the rest of the pipeline. In this layer, components are decoupled so that big data analytics can begin.

3. Data processing layer

This layer of big data architecture focuses primarily on the pipeline’s processing system. It’s where data collected in the previous layers are processed. The data is then routed to different destinations and classified. It is the first point where big data analytics occurs.

4. Data storage layer

Storage becomes an issue when dealing with huge chunks of data. That’s where solutions like data ingestion pasterns[6] come in. Here, the data is designated to the most efficient storage mediums.

5. Data query layer

This is where active analytic processing of big data takes place. The focus here is to gather the data resource values to make them more helpful in the next layer.

6. Data visualization layer

This is arguably the most prestigious layer in the data pipeline. It’s where users feel the value of data. Think of it this way; as a business, you need something to grab people’s attention in regards to data presentation. As such, you choose to present your data in various forms such as graphs so that it is well understood.

At this point, the size and complexity of big data can be understood. Here, a business can draw meaningful conclusions and make informed decisions based on collected data. Data ingestion can be achieved in two ways:

  • In real-time: This is basically the process of collecting and processing data in real-time. Since the velocity of data collection, processing, and storage is high, you need a solution that contains a queue to avoid losing events. This ensures that data is collected, processed, and saved as fast as possible.
  • In batches: Ingesting data in batches means that the data is moved from the source to the target location at scheduled intervals. This approach is mostly used when businesses need to collect data on a daily, weekly, or monthly basis.

Big data architecture best practices

If your current data architecture cannot handle the influx of data coming into your enterprise, then you need to modernize it. By following these best practices and using the right tools for the job, you can effectively achieve a positive ROI.

Eliminate internal data silos

The first step in modernizing your data architecture is making it accessible to anyone who needs it when they need it. Information silos are the norm for many businesses. But, despite their seemingly cost-effective nature, they might actually be working against you.

When you store data in disparate repositories, your employees may unwittingly duplicate it. And when this happens, it’s quite difficult to tell which data set is correct. But, when you cleanse and validate your data, you can better determine which data set is accurate and complete.

big data architecture best practices


Ensure all your data is trustworthy

While integrating, cleansing, and validating data from homogeneous sources is a great start, it’s only the beginning. Because your business also relies on data from external sources, you must modernize your big data architecture in a way that ensures that you can ingest data, cleanse it, de-duplicate it, and validate it when necessary.

Implement solid data governance

You must maintain data quality at every stage of your data pipeline. And since it’s an ongoing process, your big data architecture must be capable of supporting the process at every step.

data governance


This basically means that you must implement a robust data governance policy as part of your modernization plan.

While most organizations simply skim through the process of data governance [7], it’s crucial to modernize your data architecture in a way that facilitates strong data governance. This way, you can feel more confident in your data and rely on it to make informed strategic dissensions that give you a competitive edge.

Account for different data formats and structures

Traditionally, most data consisted of structured data that could be easily analyzed with basic tools. But those days are gone now. The advent of cloud computing and big data has completely revolutionized the nature and volume of data. As such, if your architecture model cannot accommodate all your data efficiently, there’s a huge chance that you’re missing vital information lurking in all that data.

Therefore, your big data architecture should be structured in a way that it can accommodate data from different sources in multiple formats.

Plan for the future

While modernizing your data architecture, you must also plan for the future. The ideal data architecture should be scalable, agile, flexible, and capable of real-time big data analytics and reporting. In this case, you should consider the sheer volume of data your organization has handled in the past few years, then extrapolate what the future might bring.

Choose the right tools

Without the right tools for the job, you cannot implement the aforementioned best practices efficiently. Therefore, you need to do extensive research for the best tools that can help you maximize the value of your organization’s big data.

Big data architecture – final thoughts

Big data architecture is an overreaching system that manages huge volumes of data so it can be analyzed to steer big data analytics and provide a suitable environment where big data analytic tools can extract and validate vital business information.

With the above-mentioned big data architecture best practices at your fingertips, you can be able to design a system that can handle all the processing, ingesting, and analysis needs for data that is too large and complex for traditional database systems. If you want to know more, see our big data consulting services and drop us a line!


[1] How Much Data is Created every day. URL: Accessed February 21, 2022
[2] The Extinction of Enterprise Data Warehousing. URL: , Accessed February 21, 2022
[3] Tapping the Value of unstructured data: Challenges and tools to help navigate. URL: Accessed February 21, 2022
[4] Sample XML File. URL: Accessed February 21, 2022
[5] Big Data Tools. URL:  Accessed February 21, 2022
[6] What is Data Ingestion?. URL: Accessed February 21, 2022
[7] Data Governance Solutions. URL: Accessed February 21, 2022


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