What Are the Differences Between a Data Warehouse and the Hadoop Big Data Platform?
The Hadoop big data platform can be regarded as a next-generation data warehousing system. It has the characteristics of modern data warehouses and is widely used by enterprises. Because of the scalability of MPP, the MPP-based data warehousing system is sometimes classified as a big data platform.
However, data warehouses greatly differ from the Hadoop platform in function and user experience in different scenarios. For details, see the following table.
| Feature | Hadoop | Data Warehouse |
|---|---|---|
| Number of compute nodes | 1000s | Max 256 |
| Data volume | Over 10 PB | Max 10 PB |
| Data type | Relational, semi-relational, unstructured (voice, images, and video) | Relational only |
| Latency | Medium to high | Low |
| Application ecosystem | Innovative/AI | Traditional/BI |
| Application development API | SQL and other programming language APIs, such as MapReduce | Standard database SQL |
| Scalability | Unlimited, with comprehensive programming APIs | Limited, supported by UDFs |
| Transaction support | Limited | Comprehensive |
Data warehouses and the Hadoop platform work together in different scenarios. GaussDB(DWS) on the public cloud can seamlessly integrate with Hadoop-based MRS on the public cloud to provide the SQL-over-Hadoop data sharing across platforms and services. GaussDB(DWS) serves as a data warehouse for managing massive data while relishing the openness, convenience, and innovation of the Hadoop platform. You can also enjoy the upper-layer applications of conventional data warehouses, especially BI applications, using GaussDB(DWS).
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