The VACUUM command is a significant distinction between Amazon Redshift and Google BigQuery. It’s important to note that running VACUUM is not required, particularly if Amazon Redshift is used in an append-only fashion. Amazon Redshift allows its users to DELETE or UPDATE a table, this coupled with Amazon Redshift’s I/O minimization (only relevant data blocks are fetched)-this leads to optimal query performance. Running VACUUM is an optimal operation because it reclaims space and resort rows. Once in maintenance mode, Amazon Redshift monitors the health of a variety of components and failure conditions within an AZ and recovers from them automatically.Īnother way Amazon Redshift performs maintenance is through the VACUUM feature, which is a command to remove rows that are no longer needed within the database and then sorts the data. From a performance perspective the ability to query, load, export, backup, restore and resize is parallelized for users. However, this maintenance is fully taken on by Amazon Redshift and includes all facets of database management. Amazon RedshiftĪs a data warehouse built with MPP concepts, Amazon Redshift requires periodic maintenance which makes the system run faster. For many companies, maintenance is a point of contention as it’s a leading indicator of overall data warehouse performance. In this blog post, we’ll cover the crucial differences in how Amazon Redshift and Google BigQuery perform maintenance. But, like any system, every data warehouse needs to undergo maintenance for a tune up from time to time. With a cloud-based data warehouse, there’s no physical infrastructure to manage, allowing for a streamlined focus on analytics and insights, rather than hours of manual maintenance.
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