February 28, 2022
Big Data Architecture: Definition, Processes, and Best Practices
Since the early 2000s, the volume of data generated has grown exponentially. In 2026, the world is expected to produce around 221 zettabytes of data — up from 181 ZB in 2025. That works out to roughly 402 million terabytes of new data every day, driven by mobile apps, IoT devices, online services and enterprise platforms generating data continuously.. 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.
Key Takeaways:
- Big data architecture is a system designed to ingest, process, and analyze data that is too large, too fast, or too varied for traditional databases — it’s defined by your business needs, not by a fixed set of tools.
- Big data is characterized by six “V”s: Volume, Variety, Velocity, Variability, Veracity (data trustworthiness), and Value (the business outcome that ultimately justifies the whole architecture).
- Most big data architectures come down to six core layers — ingestion, collection, processing, storage, query, and visualization — but not every project needs all of them.
- Ingestion happens in two modes: real-time (events processed as they arrive, requiring a queuing mechanism so nothing is lost) and batch (data moved on a schedule when minute-level freshness isn’t required).
- The biggest implementation risk is rarely the technology — it’s the input data. Cleaning and unifying existing data from scattered legacy systems often consumes the larger part of a project’s budget.
- Five best practices drive a positive ROI: eliminate data silos, ensure data is trustworthy, implement solid data governance, account for multiple data formats and structures, and plan for future scale.
- If you’re modernizing, start with one well-defined problem and fix data quality and governance first — tool selection and scaling get far easier afterward.
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.
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)
- Veracity — not all data is accurate or complete. The more sources you add, the higher the risk of errors, duplicates and inconsistencies. A big data architecture must be able to assess and improve data quality before that data reaches your analytics
- Value — sheer volume means nothing if you can’t extract business value from it. This is ultimately the measure of success for the whole architecture: whether the data actually supports decisions.
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.
Source: docs.microsoft.com
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. Most big data architectures come down to
six core layers. Not every project needs all of them — below we explain what each one is responsible for.
Big data architecture layers
1. Data ingestion layer
This is the first step that big data from multiple sources takes on its journey to being processed. Here the data is prioritized and categorized, so it can flow smoothly into the subsequent layers. Ingestion can be handled in two ways:
- In real time: data is collected and processed as it arrives. When the velocity of incoming data is high, you need a queuing mechanism (such as an event broker) so that no event is lost.
- In batches: data is moved from the source to the target location at scheduled intervals — daily, weekly or monthly. This approach works well when minute-level freshness isn’t required.
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 patterns[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 the layer where data finally turns into decisions — where business users actually feel the value of everything the pipeline has done. 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:
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.
In the projects we’ve delivered at Addepto, the most common cause of big data architecture problems has not been the technology — it’s been the input data. Teams routinely underestimate how much work it takes to clean and unify existing data before anything can be analyzed on top of it; with a lot of older, scattered source systems this can consume the larger part of a project’s budget. That’s why we order the best practices below in the sequence in which they actually pay off: data order and quality first, tools second.
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.
Source: dnb.com
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.
Source: imperva.com
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 decisions 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
A good big data architecture isn’t about picking the trendiest tools — it’s about deliberately designing every layer, from data intake to consumption, around your specific business needs. If your current architecture can’t keep up with the data coming in, start with three things: eliminate silos, ensure data quality, and put governance in place. Everything else — tool selection and scaling — gets far easier after that.
If you’d like to discuss what this would look like for your organization, book a 30-minute call with our team — we’ll walk through your current stack with you and show you where the biggest bottlenecks are.
References
[1] Explodingtopics.com. Amount of Data Created Daily (2026). URL: https://explodingtopics.com/blog/data-generated-per-day. Accessed February 23, 2026
[2] Medium.com. The Extinction of Enterprise Data Warehousing. URL: https://piethein.medium.com/the-extinction-of-enterprise-data-warehousing-570b0034f47f , Accessed February 21, 2022
[3] Dataversity.net. Tapping the Value of unstructured data: Challenges and tools to help navigate. URL: https://www.dataversity.net/tapping-the-value-of-unstructured-data-challenges-and-tools-to-help-navigate/. Accessed February 21, 2022
[4] Microsoft.com. Sample XML File. URL: https://docs.microsoft.com/en-us/previous-versions/windows/desktop/ms762271(v=vs.85). Accessed February 21, 2022
[5] Upgrad.com. Big Data Tools. URL: https://www.upgrad.com/blog/big-data-tools/. Accessed February 21, 2022
[6] Ezdatamunch.com. What is Data Ingestion?. URL: https://ezdatamunch.com/what-is-data-ingestion/. Accessed February 21, 2022
[7] Precisely.com. Data Governance Solutions. URL: https://www.precisely.com/solution/data-governance-solutions. Accessed February 21, 2022
FAQ
What is the difference between big data architecture and regular data architecture?
Data architecture is the overall design of how an organization collects, stores and serves data — regardless of scale. Big data architecture is a special case of it, designed for data that is too large, too fast or too varied for traditional databases to handle. In practice the difference comes down to distributed processing, horizontal scaling and support for unstructured data.
How many layers does a big data architecture have?
There is no single mandatory number — it’s a conceptual model, not a standard. In this article we describe six core layers (ingestion, collection, processing, storage, query, visualization). Other approaches simplify this to four (sources → storage → processing → serving) or describe the architecture through the Lambda and Kappa patterns. The number of layers matters less than whether every function — from data intake to consumption — has been deliberately designed.
What is the difference between Lambda and Kappa architecture?
Lambda runs two parallel tracks: a fast one (streaming, approximate) and a batch one (slower, exact), then combines the results at query time. Kappa drops the separate batch track and treats everything as a stream, replaying historical data from the event log. For new projects in 2026, Kappa is usually simpler to operate; Lambda makes sense when you have a legacy batch system you can’t decommission yet.
Does big data always mean Hadoop?
Not anymore. Hadoop was the foundation of early big data architectures, but today most teams build on cloud platforms and lakehouses (such as Databricks, Snowflake, BigQuery) and streaming engines (Spark, Flink). Hadoop is still used in on-premise environments with data sovereignty requirements, but it is no longer the default choice.
Should a big data architecture run in the cloud or on-premise?
The cloud is the fastest path for most organizations — you pay for usage, scale elastically and maintain no hardware. On-premise (or hybrid) makes sense under strict regulatory requirements, data sovereignty rules, or for very large, steady workloads where owning the infrastructure is cheaper. The decision is usually driven by regulation and workload profile, not by the technology itself.
What are the most common mistakes when designing a big data architecture?
The most common ones are: building for data the organization doesn’t yet generate (over-engineering), skipping an observability and lineage layer from the start, ignoring data governance until the first incident, and choosing tools before defining a concrete use case. The architecture should follow business needs, not a list of trendy technologies.
What are structured, unstructured and semi-structured data?
Structured data is data in a fixed format (tables, columns — e.g. a transactional database). Unstructured data has no defined form (text, images, video, recordings). Semi-structured data sits in between — it has some structure but not a strict one (e.g. JSON, XML, logs). A modern big data architecture has to handle all three types at once.
Where should I start when modernizing my data architecture?
Start with a single, well-defined problem — not a rebuild of everything at once. Begin by eliminating data silos and ensuring data quality, because without trustworthy data every layer above it loses meaning. Then put governance and observability in place, and only then choose tools for specific workloads. Modernizing in stages delivers measurable results faster than a big-bang project.
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