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Amazon Redshift now supports up to 200,000 tables for Redshift Serverless and data warehouse clusters using node types such as ra3.4xlarge, ra3.16xlarge, and dc2.8xlarge: https://aws.amazon.com/about-aws/whats-new/2023/03/amazon-redshift-200K-tables-single-cluster/
For large-scale data warehouse, it can:
- Simplified Management: Customers no longer need to split tables across multiple warehouses or move some tables to Amazon S3, reducing complexity.
- Enhanced Scalability: Workloads requiring a large number of tables can now run seamlessly within a single Redshift cluster.
- Automatic Enablement: This capability is automatically enabled for supported node types, requiring no changes to workloads, data ingestion, or applications.
The latest table limit for Amazon Redshift is 200,000 tables for both the 8xlarge and 16xlarge cluster node types. This limit includes permanent tables, temporary tables, datashare tables, and materialized views. External tables are counted as temporary tables, and temporary tables include both user-defined temporary tables and those created by Amazon Redshift during query processing or system maintenance. It's important to note that views and system tables are not included in this limit.
This increased table limit significantly benefits large-scale data warehouses in several ways:
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Increased Flexibility: With a higher table limit, data warehouse architects and administrators have more flexibility in designing and organizing their data structures. They can create more granular tables to suit specific analytical needs without worrying about hitting table limits as quickly.
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Support for Complex Data Models: Large-scale data warehouses often involve complex data models with numerous dimensions and fact tables. The higher limit allows for more comprehensive and detailed data modeling, supporting more intricate business intelligence and analytics requirements.
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Enhanced Data Sharing Capabilities: The inclusion of datashare tables in this limit supports more extensive data sharing scenarios, allowing organizations to create and manage a larger number of shared datasets across different clusters or accounts.
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Improved Performance for Big Data Workloads: By allowing more tables, organizations can potentially optimize query performance by creating additional materialized views or pre-aggregated tables for frequently accessed data patterns.
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Scalability for Growing Enterprises: As businesses grow and their data needs expand, the higher table limit provides room for scaling without requiring immediate architectural changes or workarounds to deal with table limitations.
This increase in the table limit demonstrates Amazon Redshift's commitment to supporting large-scale data warehousing needs, allowing organizations to manage and analyze vast amounts of data more effectively.
Sources
Quotas and limits in Amazon Redshift - Amazon Redshift
