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What Are the Differences Between Data Warehouses and the Hadoop Big Data Platform?

Updated at: May 26, 2020 GMT+08:00

Generally, the Hadoop big data platform can be regarded as a next-generation data warehousing system. It shares 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 grouped into the big data platform sometimes.

However, data warehouses greatly differ from the Hadoop platform in terms of functions and user experience in different scenarios. You can determine which one is more appropriate for your specific situation according to the following table.

Table 1 Feature comparison between data warehouses and the Hadoop big data platform



Data Warehouse

Number of compute nodes


256 or less

Data volume

Greater than 10 PB

10 PB or less

Data type

Relational, semi-relational, and unstructured data such as voice, images, and videos

Relational data


Medium to high


Application ecosystem

Innovative/artificial intelligence

Traditional database/BI

Application development API

Various programming language APIs, such as SQL and MapReduce

Standard database SQL


Unlimited scalability with its comprehensive programming APIs

Limited scalability supported by UDF

Transaction support



Data warehouses and the Hadoop platform work together to meet customers' service requirements in different scenarios. DWS on the public cloud can seamlessly integrate with Hadoop-based MRS on the public cloud to provide the SQL-over-Hadoop feature, allowing data sharing cross platforms and services. Therefore, you can enjoy not only openness, convenience, and innovation of the Hadoop platform, but also manage and use your massive volumes of data using data warehouses. Additionally, you can use upper-layer applications of conventional data warehouses, especially BI applications.

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