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With code examples on how to use them Continue reading on Towards Data Science » View the full article -
Amazon Redshift customers can now use scoped permissions to manage permissions for a role or user on a database or schema scope, avoiding the need to manually grant permissions on every object. Scoped permissions apply to objects in the selected scope when you grant or revoke the permission, as well as to new objects created after you grant or revoke the permission. For example, granting SELECT permission to tables in a schema allows access to current and future tables within the schema. Scoped permissions can also be used on shared databases created from a datashare. View the full article -
Amazon Redshift is announcing the general availability of Multi-AZ deployments for RA3 clusters. Redshift Multi-AZ deployments support running your data warehouse in multiple AWS Availability Zones (AZ) simultaneously and continue operating in unforeseen failure scenarios. A Multi-AZ deployment raises the Redshift Service Level Agreement (SLA) to 99.99% and delivers a highly available data warehouse for the most demanding mission-critical workloads. View the full article
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AWS announces the support for Amazon Redshift with Visual Studio Code (VSCode), a free and open-source code editor. The integration with Visual Studio Code enables Amazon Redshift customers to use Visual Studio Code to author and run their SQL queries in a notebook interface and view the schema objects in their Redshift data warehouses. View the full article
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Amazon QuickSight now supports connection to Redshift data with an IAM role. By connecting to data in QuickSight with an IAM role, administrators can enhance data security by using fine-grained IAM access policies for Redshift data sources. View the full article
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Amazon Redshift enables you to use AWS Lake Formation to centrally manage permissions on data being shared across your organization. Amazon Redshift already supports sharing live data across AWS regions. Amazon Redshift now supports cross-region data sharing via AWS Lake Formation so you can centrally define AWS Lake Formation permissions of Amazon Redshift datashares and restrict user access to objects within a datashare. View the full article
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The Amazon Redshift ODBC driver is now open source and available for the user community under the Apache-2.0 license. With this release, customers will gain enhanced visibility to the driver implementation and can contribute to its development. Users can browse the code for the ODBC driver on the relevant AWS GitHub repository, submit driver functionality enhancements through Github pull requests, and report issues for review. View the full article
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Amazon Redshift has improved the performance of Redshift’s classic resize feature and increased the flexibility of the cluster snapshot restore operation. Redshift classic resize is used to resize a cluster in scenarios where you need to change the instance type or transition to a configuration that cannot be supported by elastic resize. Previously, this can take the cluster offline for many hours during resize, but now the cluster can typically be available to process queries in minutes. Clusters can also be resized when restoring from a snapshot and in those cases there could be restrictions. View the full article
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Amazon Redshift now supports Row-Level Security (RLS), a new enhancement that simplifies design and implementation of fine-grained access to the rows in tables. With RLS, you can restrict access to a subset of rows within a table based on the users’ job role or permissions and level of data sensitivity with SQL commands. By combining column-level access control and RLS, Amazon Redshift customers can provide comprehensive protection by enforcing granular access to their data. View the full article
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Amazon Redshift Serverless, which allows you to run and scale analytics without having to provision and manage data warehouse clusters, is now generally available. With Amazon Redshift Serverless, all users—including data analysts, developers, and data scientists—can now use Amazon Redshift to get insights from data in seconds. Amazon Redshift Serverless automatically provisions and intelligently scales data warehouse capacity to deliver high performance for all your analytics. You only pay for the compute used for the duration of the workloads on a per-second basis. You can benefit from this simplicity without making any changes to your existing analytics and business intelligence applications. View the full article
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Amazon Redshift announces GA of Automated Materialized View (AutoMV) that helps you to lower query latency for repeatable workloads. AutoMV minimizes your effort for manually creating and managing materialized views and provides you the same performance benefits of user-created materialized views. Dashboard queries used to provide quick views of key performance indicators (KPIs), events, trends, and other metrics are some examples of workloads that can benefit from AutoMV. Reporting queries scheduled at various frequencies may also benefit from AutoMV. View the full article
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Amazon Redshift ML enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. With Amazon Redshift ML, you can leverage Amazon SageMaker, a fully managed machine learning service, without moving your data or learning new skills. Amazon Redshift now supports Amazon SageMaker Linear Learner algorithm for creating models with Amazon Redshift ML. View the full article
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Amazon Redshift has launched support for Snapshot Isolation for concurrent transactions. Amazon Redshift prevents dirty reads, non-repeatable reads, and phantom reads according to the SQL standards. The two options that Amazon Redshift offers to serialize transactions are SERIALIZABLE and SNAPSHOT ISOLATION. The SERIALIZABLE option will implement strict serializability, where a transaction could fail if the result could not be mapped to a serial order of the concurrently running transactions. The SNAPSHOT ISOLATION option will allow higher concurrency, where concurrent modifications to different rows in the same table would complete successfully. Under both options, transactions will continue to operate on the latest committed version, or a snapshot, of the database. View the full article
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AQUA (Advanced Query Accelerator) for Amazon Redshift is available in preview. AQUA provides a new distributed and hardware accelerated cache that brings compute to the storage layer for Amazon Redshift and delivers up to 10x faster query performance than other cloud data warehouses. View the full article
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Amazon Redshift, a fully-managed cloud data warehouse, now supports automatic refresh and query rewrite capabilities to simplify and automate the usage of materialized views. The automatic refresh feature helps administrators to keep materialized views up-to-date, while the automatic query rewrite feature enables end-users to easily benefit from improved query performance. View the full article
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Amazon Redshift, a fully-managed cloud data warehouse, now adds native support for TIME and TIMETZ data types. TIME data type stores the time of day without timezone information, and TIMETZ stores the time of day including timezone information. This new data type builds on the existing support in Amazon Redshift for DATE, TIMESTAMP and TIMESTAMPTZ data types that can store date and date-and-time values. View the full article
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Amazon Redshift, a fully-managed cloud data warehouse, now supports Lambda user-defined functions (UDFs) enabling you to use an AWS Lambda function as a UDF in Amazon Redshift. This functionality enables you to write custom extensions for your SQL query to achieve tighter integration with other services or third-party products. For example, you can write Lambda UDFs to enable external tokenization of data by integrating with vendors like Protegrity, or access other services such as Amazon DynamoDB or Amazon SageMaker in your Redshift query. View the full article
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Amazon Redshift now allows you to schedule your SQL queries for executions in recurring schedules and enables you to build event-driven by integrating with Amazon EventBridge. You can now schedule time sensitive or long running queries, loading or unloading your data, or refreshing your materialized views on a regular schedule. View the full article
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Amazon Redshift now allows users to modify the compression encoding of existing columns with a single statement. This new capability makes it easier to maintain the optimal compression encodings in Amazon Redshift to achieve high performance and reduce the storage utilization. View the full article
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Amazon Redshift now supports the ability to query across databases in a Redshift cluster. With cross-database queries, you can seamlessly query data from any database in the cluster, regardless of which database you are connected to. Cross-database queries can eliminate data copies and simplify your data organization to support multiple business groups on the same cluster. Support for cross-database queries is available on Amazon Redshift RA3 instance types. View the full article
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