In the previous article, [A Closer Look at the Evolution of Databases](https://xx/A Close Look at the Evolution of Databases), we explored how databases have continuously evolved to meet growing demands and increasing data volumes. As data now grows at an exponential scale, traditional database systems can no longer satisfy modern requirements for storage, computing, and querying. This gap led directly to the rise of Big Data Architecture.
Big Data Architecture is not a single system or tool. Instead, it represents a complete technology ecosystem built around big data storage, big data computing, and big data querying. Together, these three pillars enable organizations to store massive datasets, process them efficiently, and extract real business value.
Characteristics of Big Data Architecture
With the rapid expansion of mobile internet, cloud computing, and IoT devices, data is generated continuously and at unprecedented speed. As a result, Big Data Architecture must address several defining characteristics:
- Volume – Data scales from terabytes (TB) to petabytes (PB), and even exabytes (EB).
- Variety – Data appears in structured, semi-structured, and unstructured formats.
- Velocity – Systems must support high-speed ingestion and near real-time processing.
- Cost Efficiency – Storage and computing must scale while keeping hardware and operational costs manageable.
Because of these constraints, traditional single-node databases struggle to cope. Therefore, Big Data Architecture emerged to systematically solve these challenges through distributed design.
Big Data Storage in Modern Big Data Architecture
Big data storage forms the foundation of Big Data Architecture. Unlike traditional databases, big data storage systems must be distributed, scalable, and fault-tolerant.
In practice, distributed storage splits data into blocks and replicates them across multiple machines. As a result, systems can store larger datasets while maintaining availability even during node failures.
Common big data storage technologies include:
- HDFS (Hadoop Distributed File System) – A core component of early Big Data Architecture, designed for high-throughput access and horizontal scalability.
- Object Storage – Technologies such as Amazon S3, Alibaba Cloud OSS, and Apache Ozone dominate cloud-native architectures due to elastic scaling and cost efficiency.
- NoSQL Databases – Systems like HBase and Cassandra store structured or semi-structured data that requires fast read/write access.
In short, the goal of big data storage is simple but critical: store more data, store it reliably, and store it at scale.
Big Data Computing: Turning Stored Data into Value
While storage preserves data, big data computing determines how quickly and accurately that data can be processed. Since data value decays over time, single-node computing is no longer sufficient.
Big Data Architecture solves this problem through distributed computing frameworks that process data in parallel.
Representative computing approaches include:
- Batch Processing – Technologies such as MapReduce and Spark process large volumes of historical data efficiently, supporting offline analytics and reporting.
- Real-Time Computing – Stream processing engines like Flink handle continuous data streams and enable millisecond-level analytics.
- Unified Batch and Stream Computing – Modern frameworks unify batch and stream processing, reducing duplicated logic and simplifying system architecture.
Through these approaches, big data computing ensures that data remains processable, fast, and accurate.
Big Data Querying: The User-Facing Layer of Big Data Architecture
While computing focuses on how data is processed, business users care most about how data can be accessed. This requirement makes big data querying a critical layer in Big Data Architecture.
Popular big data query technologies include:
- SQL on Hadoop – Tools like Hive translate SQL queries into distributed jobs, lowering the barrier for data analysis.
- Distributed Query Engines – Presto, Trino, and Impala enable low-latency interactive queries for BI and analytics workloads.
- MPP and OLAP Engines – Systems such as ClickHouse, Doris, and Kylin excel at multidimensional analysis and real-time reporting.
- Lakehouse Query Engines – Technologies like Iceberg and Delta Lake, combined with Spark or Trino, unify data lakes and warehouses.
If storage represents the foundation and computing acts as the engine, querying becomes the window through which users interact with big data. Its mission is clear: usable, fast, and intuitive access to data.
How Storage, Computing, and Querying Work Together
Big Data Architecture succeeds because its three core components form a tightly integrated system:
- Storage is the foundation Without scalable and reliable storage, computing and querying cannot exist.
- Computing is the bridge Computing transforms raw data into structured, query-ready results.
- Querying is the interface Query engines deliver insights directly to users and applications.
Together, these layers ensure that massive datasets move smoothly from raw storage to actionable intelligence.
Conclusion
This article deconstructs Big Data Architecture into its three essential pillars: storage, computing, and querying. Storage guarantees reliable persistence, computing extracts value at scale, and querying delivers insights to users.
By working together, these components form a resilient and scalable architecture that allows Big Data technologies to power analytics, decision-making, and innovation across industries.