When Should I Use GaussDB(DWS) and MRS?
MRS works better with big data processing frameworks such as Apache Spark, Hadoop, and HBase, to process and analyze ultra-large data sets through custom code. It allows you to control cluster configurations and software installed in the cluster.
GaussDB(DWS) works better with complex queries of a large amount of structured data. It aims to pool data from different sources together, such as inventory, finance, and retail system. To ensure consistency and accuracy of enterprise reports, GaussDB(DWS) stores data in a highly structured manner. This structure can directly build the data consistency rule to the database table. Additionally, GaussDB(DWS) is highly compatible with standard SQL statements and the syntax of conventional transaction-supported databases.
GaussDB(DWS) is preferred when you want to perform complex query of a large amount of structured data with high performance.
General Problems FAQs
- Why Are Data Warehouses Necessary?
- What Are the Differences Between a Data Warehouse and the Hadoop Big Data Platform?
- Why Should I Use Public Cloud GaussDB(DWS)?
- Should I Choose Public Cloud GaussDB(DWS) or RDS?
- When Should I Use GaussDB(DWS) and MRS?
- What Is the User Quota?
- What Are the Differences Between Users and Roles?
- How Do I Check the Creation Time of a Database User?
- Regions and AZs
- Is My Data Secure in GaussDB(DWS)?
- How Is GaussDB(DWS) Secured?
- Can I Modify the Security Group of a GaussDB(DWS) Cluster?
- How Are LibrA, GaussDB A, and GaussDB(DWS) Related?
- What Is a Database/Data Warehouse/Data Lake/Lakehouse?
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