forked from docs/doc-exports
Lu, Huayi
95132e24fc
Reviewed-by: Pruthi, Vineet <vineet.pruthi@t-systems.com> Reviewed-by: Rechenburg, Matthias <matthias.rechenburg@t-systems.com> Co-authored-by: Lu, Huayi <luhuayi@huawei.com> Co-committed-by: Lu, Huayi <luhuayi@huawei.com>
14 lines
1.4 KiB
HTML
14 lines
1.4 KiB
HTML
<a name="EN-US_TOPIC_0000001381889053"></a><a name="EN-US_TOPIC_0000001381889053"></a>
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<h1 class="topictitle1">When Should I Use GaussDB(DWS) and MRS?</h1>
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<div id="body0000001381889053"><p id="EN-US_TOPIC_0000001381889053__en-us_topic_0000001098976718_p218348621215">MRS works better with big data processing frameworks such as Apache Spark, Hadoop, and HBase, to process and analyze ultra-large datasets using custom code. MRS enables you to control cluster configurations and the software installed in the cluster.</p>
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<p id="EN-US_TOPIC_0000001381889053__en-us_topic_0000001098976718_p622960321215">GaussDB(DWS) works better with complex queries of large amounts of structured data. GaussDB(DWS) aggregates data from multiple sources, such as inventory, finance, and retail sales systems. To ensure data consistency and accuracy, GaussDB(DWS) stores data in a highly structured manner. This structure builds data consistency rules directly into database tables. Additionally, GaussDB(DWS) is highly compatible with standard SQL statements and syntax used in traditional databases.</p>
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<p id="EN-US_TOPIC_0000001381889053__en-us_topic_0000001098976718_p237933781215">GaussDB(DWS) is an ideal choice for performing complex queries on massive collections of structured data, with superb performance.</p>
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<div class="parentlink"><strong>Parent topic:</strong> <a href="dws_03_0001.html">General Problems</a></div>
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