What's New in Amazon Redshift

You can now unload the result of an Amazon Redshift query to your Amazon S3 data lake as Apache Parquet, an efficient open columnar storage format for analytics. The Parquet format is up to 2x faster to unload and consumes up to 6x less storage in Amazon S3, compared to text formats. This enables you to save data transformation and enrichment you have done in Amazon Redshift into your Amazon S3 data lake in an open format. You can then analyze your data with Redshift Spectrum and other AWS services such as Amazon Athena, Amazon EMR, and Amazon SageMaker.

The in-preview Amazon Redshift Federated Query feature allows you to query and analyze data across operational databases, data warehouses, and data lakes. With Federated Query, you can now integrate queries on live data in Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL with queries across your Amazon Redshift and Amazon S3 environments.

Starting today, Amazon Redshift RA3 nodes with managed storage are generally available. RA3 nodes enable you to scale and pay for compute and storage independently allowing you to size your cluster based only on your compute needs. Now you can analyze even more data cost-effectively.

Starting today, Amazon Redshift adds support for materialized views in preview. Materialized views provide significantly faster query performance for repeated and predictable analytical workloads such as dashboarding, queries from business intelligence (BI) tools, and ELT (Extract, Load, Transform) data processing.

The Amazon Redshift cluster elastic resize operation can now be automated using a scheduler that allows you to automatically resize clusters to accommodate changes in workloads that occur on a regular basis. For example, you can now automatically expand a cluster to accommodate heavier workloads as well as shrink a cluster to accommodate lighter workloads at specific times of day. This will allow you to automate cluster resizing to balance price and performance when using Redshift.

Amazon Redshift now provides an efficient and automated way to maintain sort order of the data in Redshift tables to continuously optimize query performance. The new automatic table sort capability offers simplified maintenance and ease of use without compromising performance and access to Redshift tables. Automatic table sort complements Automatic Vacuum Delete and Automatic Analyze and together these capabilities fully automate table maintenance. Automatic table sort is now enabled by default on Redshift tables where a sort key is specified.

Amazon Redshift Spectrum is now available in five additional AWS regions: EU (Paris, Stockholm), Middle East (Bahrain), Asia Pacific (Hong Kong), and the AWS GovCloud (US-West) Region.

With the addition of a new polymorphic data type, GEOMETRY, Amazon Redshift now provides the ability to natively process spatial data. This capability enables customers to store, retrieve, and process spatial data so you can enhance your business insights by integrating spatial data into your analytical queries.

Amazon Redshift now offers a new console for Redshift users. The new user interface and new features simplify management and improve insights into the health and performance of your Redshift clusters and workloads .

Amazon Redshift now supports cross-instance restore to allow you to restore Redshift snapshots to clusters that are different sizes or running different node types. This simplifies snapshot restore workflows to support your evolving use cases. You can change instance type and count based on compute resources, storage capacity and cost requirements of your specific workloads. For example, you can cross-instance restore a snapshot of your production dense-compute cluster with small instances into a cluster with powerful large instances to serve more demanding workloads, or to a lower-cost development cluster. Once a cluster is restored, you can resize it as your performance and capacity needs change over time.

Amazon Redshift now enables users to add and change sort keys of existing Redshift tables without having to re-create the table. The new capability simplifies user experience in maintaining the optimal sort order in Redshift to achieve high performance as their query patterns evolve and do it without interrupting the access to the tables.

Performance enhancements have been made that allow Amazon Redshift to copy snapshots across regions much faster, allowing customers to support much more aggressive Recovery Time Objective (RTO) and Recovery Point Objective (RPO) Disaster Recovery (DR) policies. The performance of copying a snapshot across regions is now orders of magnitude faster than what was achievable earlier this year, with up to 40,000 MB/s transfer rates observed.  

AZ64, a proprietary compression encoding that is designed to achieve a high compression ratio and improved query performance, is now available. Innovations in the AZ64 algorithm efficiently compress small groups of data values and leverage SIMD instructions for data parallel processing. This encoding results in significant storage savings in most cases relative to LZO and ZSTD encodings and optimal de-compression performance for numeric and date/time data stored in Amazon Redshift.

Amazon Redshift now makes it easy to maximize query throughput and get consistent performance for your most demanding analytics workloads. Automatic workload management (WLM) uses machine learning to dynamically manage memory and concurrency helping maximize query throughput. In addition, you can now easily set the priority of your most important queries, even when hundreds of queries are being submitted. 

Amazon Redshift Advisor now recommends the most appropriate distribution key for frequently queried tables to improve query performance. The Advisor generates tailored recommendations by analyzing the cluster's performance and query patterns. You can then use the ALTER TABLE ALTER DISTKEY command to add or modify the distribution key of a table, without impacting concurrent read or write queries.

Amazon Redshift Spectrum now supports column level access control for data stored in Amazon S3 and managed by AWS Lake Formation. Column level access control can be used to limit access to only the specific columns of a table rather than allowing access to all columns of a table.

Amazon Redshift Concurrency Scaling is now available in five additional AWS Regions: Canada (Central), EU (Frankfurt), Asia Pacific (Sydney), Asia Pacific (Singapore), and Asia Pacific (Seoul). The Amazon Redshift Management Console presents performance graphs for monitoring Concurrency Scaling activity and associated performance gains related to query concurrency, throughput, latency, and queueing on your Amazon Redshift cluster.

Data Analytics Fundamentals, which replaces the digital training course Big Data Technology Fundamentals, will teach you the latest on how to seamlessly plan a data analysis solution using the suite of AWS services. In this 3.5-hour, self-paced digital course, you will understand the big picture of data analysis, how to plan data analysis solutions, as well as the data analytic processes involved in those solutions.

This Quick Start builds a data lake environment on the Amazon Web Services (AWS) Cloud in about 15 minutes by deploying SnapLogic components and AWS services such as Amazon Simple Storage Service (Amazon S3) and Amazon Redshift.

Amazon Redshift now supports SQL stored procedures to make migration to Amazon Redshift easier. Stored procedures are used to encapsulate logic for data transformation, data validation, and business-specific logic. By combining multiple SQL steps into a stored procedure, you can reduce round trips between your applications and the database.

Amazon Redshift automatically takes incremental snapshots (backups) of your data every 8 hours or 5 GB per node of data change. You now get more information and control over a snapshot including the ability to control the automatic snapshot's schedule.

Amazon Redshift can now automatically assign an optimal distribution style based on the size of the table data. With the automatic selection of the right distribution style, you get better query performance and storage space utilization across nodes.

Amazon Redshift now automatically and elastically scales query processing power to provide consistently fast performance for hundreds of concurrent queries. Concurrency Scaling resources are added to your Redshift cluster transparently in seconds, as concurrency increases, to process queries without wait time. Once workload demand subsides, Amazon Redshift automatically shuts down Concurrency Scaling resources to save you cost. You can continue to use your existing applications and Business Intelligence tools without any changes.

Starting today, you can use AWS Secrets Manager to rotate credentials for Amazon DocumentDB and Amazon Redshift automatically, in addition to rotating credentials for all database types hosted on Amazon Relational Database Service (Amazon RDS). Rotating credentials is an AWS Identity and Access Management (IAM) best practice that helps you meet your security and compliance requirements.

Analyze operations now run automatically on your Amazon Redshift tables in the background to deliver improved query performance and optimal use of system resources.

Amazon Redshift now automatically runs the VACUUM DELETE operation to reclaim disk space occupied by rows that were marked for deletion by previous UPDATE and DELETE operations. It also defragments the tables to free up consumed space and improves performance for your workloads.

Amazon Redshift is now available in the EU (Stockholm) AWS Region.

You can now defer maintenance of your Amazon Redshift cluster to keep your data warehouse running without interruption during critical business periods. You will now also receive advance notifications from Amazon Redshift prior to any upcoming maintenance on your cluster.

You can now quickly resize your Amazon Redshift cluster in minutes by adding nodes to get better performance and more storage for demanding workloads or by removing nodes to save cost.

We are excited to announce that Amazon Redshift is now available in the AWS GovCloud (US-East) Region.

You can now query data in your Amazon Redshift cluster directly from your AWS Management console, using the new Query Editor. This provides an easier way for admins and end users to run SQL queries without having to install and setup an external JDBC/ODBC client. Query results are instantly visible within the console.

You can now use Amazon Redshift to directly query nested data in Apache Parquet, Apache ORC, JSON and Amazon Ion file formats stored in external tables in Amazon S3. Redshift Spectrum, a feature of Amazon Redshift, enables you to use your existing Business Intelligence tools and intuitive and powerful SQL extensions to analyze both scalar and nested data stored in your Amazon S3 data lake.

Amazon Redshift now enables short query acceleration by default to speed up execution of short-running queries such as reports, dashboards, and interactive analysis. Short query acceleration uses machine learning to provide higher performance, faster results, and better predictability of query execution times. 

Amazon Redshift now enables you to write queries that refer to a column alias within the same query immediately after it is declared, improving the readability of complex SQL queries.

You can now get a detailed view of your Amazon Redshift cluster performance with the Workload Execution Breakdown graph on the console Database Performance page or the QueryRuntimeBreakdown metric via Cloudwatch. You can use these new metrics to optimize cluster performance to provide higher throughput and faster results. 

Amazon Redshift is now available on two different release cycles - Current Maintenance Track and Trailing Maintenance Track. With the Current Maintenance Track you will get the most up-to-date certified release version with the latest features, security updates, and performance enhancements. With the Trailing Maintenance Track you will be on the previous certified release. 

Amazon Redshift announces Advisor, a new feature that provides automated recommendations to help you optimize database performance and decrease operating costs. Advisor is available via the Amazon Redshift console at no charge.

You can now upgrade your Amazon Redshift DC1 Reserved Instances to DC2 Reserved Instances for the remainder of your DC1 reserved term, and get up to twice the performance of DC1 at the same price. DC2 nodes are designed for demanding data warehousing workloads that require low latency and high throughput.

You can now COPY Apache Parquet and Apache ORC file formats from Amazon S3 to your Amazon Redshift cluster. Apache Parquet and ORC are columnar data formats that allow users to store their data more efficiently and cost-effectively. With this update, Redshift now supports COPY from six file formats: AVRO, CSV, JSON, Parquet, ORC and TXT. 

Amazon Redshift improved Short Query Acceleration (SQA) by automating the maximum time-out setting for short queries. On Nov 20th, 2017, we announced SQA which uses machine learning algorithms to predict the execution time of a query, and move short-running queries to an express queue for immediate processing. In the past, SQA needed user defined 'maximum run time' (between 1-20 seconds) to identify short queries. With this update, by setting the maximum short query run time to 'dynamic,' you can let Redshift automate this setting. In addition, SQA’s improved machine learning algorithm adapts the maximum run time to the changing workload, making it easier to use to minimize short query queuing time and increase throughput.

You can now monitor the performance and health of your Amazon Redshift cluster with two new Amazon CloudWatch metrics, Query Throughput and Query Duration. These metrics monitor Redshift cluster performance and provide insights to adjust workload management settings to improve query performance. We have also improved the user interface to view the Query Performance Data on the console.

We have added support for Apache Parquet and Apache ORC formats in Amazon Kinesis Data Firehose, so you can stream real-time data into Amazon S3 for cost-effective storage and analytics.

You can now launch Amazon Redshift clusters on our second-generation Dense Compute (DC2) nodes in the AWS GovCloud (US) Region. DC2 nodes deliver up to twice the performance as the previous generation DC1 nodes, at the same price.

Amazon Redshift Spectrum is now available in two additional AWS regions: Asia Pacific (Mumbai) and South America (Sao Paulo).

Amazon Redshift increases the number of tables you can create to 20,000 for 8xlarge cluster node types. Doubling the number of tables gives you more control and granularity for organizing data in your cluster.

You can now use Amazon Redshift Spectrum to directly query scalar JSON and Ion data types stored in external tables in Amazon S3 - without loading or transforming the data.  

Amazon Redshift now uses late materialization to reduce the amount of data scanned and improve performance for queries with predicate filters. 

Amazon Spectrum is now available in three additional AWS regions: US West (Northern California), EU (London), and Canada (Central). 

AWS is excited to announce immediate availability of the new AWS EU (Paris) Region. Paris joins Ireland, Frankfurt, and London as the fourth AWS Region in Europe and as the 18th region worldwide, bringing the global total of AWS Availability Zones to 49.

You can now leverage Amazon Redshift Spectrum to query DATE data type stored in Optimized Row Columnar (ORC) and text files in Amazon S3. For example, you can use the DATE data type to query clickstream data within specific time windows to gain insights into business trends. To learn more, visit our documentation. 

Amazon Redshift improves performance for repeat queries by caching the result and returning the cached result when queries are re-run. 

Amazon Redshift Spectrum is now available in four additional AWS Regions: EU (Frankfurt), Asia Pacific (Sydney), Asia Pacific (Singapore), and Asia Pacific (Seoul). Additionally, large bzip2-compressed files and ORC files are automatically split to enhance query performance in all available AWS Regions. 

Amazon Redshift introduces Short Query Acceleration to speed up execution of short running queries. Short Query Acceleration provides higher performance, faster results, and better predictability of query execution times. 

Starting today, Amazon Redshift allows Superusers to grant regular users access to all rows in selected system tables and views. 

Starting today, Amazon Redshift improves query performance by automatically moving read and write queries to the next matching queue without restarting the moved queries. This enhancement to Workload Management enables more efficient use of resources to improve query performance. 

AWS Budgets now allows you to set RI utilization alerts for your Amazon Redshift, Amazon RDS, and Amazon ElastiCache reservations, in addition to setting alerts on your Amazon EC2 Reserved Instances.

RI Utilization alerts allow you to set a custom utilization target for one or more reservations pertaining to the same AWS service, and notify you when your reservation utilization drops below that threshold. Reservation utilization tracks the percentage of reserved hours that were used by matching instances, and can be monitored by AWS Budgets at a daily, monthly, quarterly, or yearly level. You can monitor your reservation utilization at an aggregate level (e.g., monthly utilization of your Amazon RDS fleet) or at a granular level of detail (e.g., daily utilization of db.m4.2xlarge instances running in a specific region). From there, you can define up to five notifications. Each notification can be sent to a maximum of ten email subscribers, and can be broadcast to an Amazon Simple Notification Service (Amazon SNS) topic.

To get started with utilization alerts, please access the AWS Budgets dashboard or refer to the Managing your Costs with Budgets user guide.

 

Starting today, you can monitor your Amazon Redshift, Amazon RDS, and Amazon ElastiCache reservations, in addition to your Amazon EC2 Reserved Instances (RI), using the RI Utilization report, available in AWS Cost Explorer.

This Quick Start automates the design, setup, and configuration of hardware and software to implement a data lake on the Amazon Web Services (AWS) Cloud. The Quick Start provisions Talend Big Data Platform components and AWS services such as Amazon EMR, Amazon Redshift, Amazon Simple Storage Service (Amazon S3), and Amazon Relational Database Service (Amazon RDS) to build a data lake. It also provides an optional sample dataset and Talend jobs developed by Cognizant Technology Solutions to illustrate big data practices for integrating Apache Spark, Apache Hadoop, Amazon EMR, Amazon Redshift, and Amazon S3 technologies into a data lake implementation. 

Amazon Redshift Spectrum is now available in the Europe (Ireland) and Asia Pacific (Tokyo) AWS Regions. Redshift Spectrum is a feature of Amazon Redshift that enables you to analyze all of your data in Amazon S3 using standard SQL, with no data loading or transformations needed. 

You can now launch Amazon Redshift clusters on our second-generation Dense Compute (DC2) nodes. DC2 is designed for demanding data warehousing workloads that require low latency and high throughput. They feature powerful Intel E5-2686 v4 (Broadwell) CPUs, fast DDR4 memory, and NVMe-based solid state disks (SSDs). We’ve tuned Amazon Redshift to leverage the better CPU, network, and disk on DC2 nodes, providing up to twice the performance of DC1 at the same price. Our DC2.8xlarge instances now provide twice the memory per slice of data and an optimized storage layout with 30% better storage utilization. You can save up to 75% over On-Demand rates by committing to use Amazon Redshift on Reserved Instances for a 1 or 3 year term. Reserved Instance pricing is specific to the node type purchased, and remains in effect until the reservation term ends.

You can now specify whether column names returned by SELECT statements are uppercase or lowercase. With this feature, you can now set a session-based parameter to enable your case-sensitive applications to easily query Amazon Redshift. For more information, see describe_field_name_in_uppercase in the Amazon Redshift Database Developer Guide. 

The LISTAGG aggregate function orders the rows for each group in a query according to the ORDER BY expression, then concatenates the values into a single string. With the new DISTINCT argument, you can now eliminate duplicate values from the specified expression before concatenating the values into a single string. For more information, see LISTAGG Function in the Amazon Redshift Database Developer Guide.

You can now create a view that spans Amazon Redshift and Redshift Spectrum external tables. With late-binding views, table binding will take place at runtime, providing your users and applications with seamless access to query data. Late-binding views allows you to drop and make changes to referenced tables without affecting the views. With this feature, you can query frequently accessed data in your Amazon Redshift cluster and less-frequently accessed data in Amazon S3, using a single view. Simply archive historical data to Amazon S3, create an external table referencing the relevant files, and then create a view referencing both the Amazon Redshift and the Redshift Spectrum external tables.

This Quick Start deploys a data lake foundation that integrates Amazon Web Services (AWS) Cloud services such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Kinesis, Amazon Athena, Amazon Elasticsearch Service (Amazon ES), and Amazon QuickSight.  

You can now create and run scalar user-defined functions (UDFs) using SQL in Amazon Redshift.  

You can now leverage Amazon Redshift Spectrum to query data stored in Optimized Row Columnar (ORC) and Grok file formats. Amazon Redshift Spectrum also supports multiple other open file formats, including Avro, CSV, Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV. For more information, see supported file formats in the Amazon Redshift Database Developer Guide.

You can now use OCTET_LENGTH to count the number of bytes (octets) in a specified string.

You can now use the AWS Glue Data Catalog as the metadata repository for Amazon Redshift Spectrum. The AWS Glue Data Catalog provides a central metadata repository for all of your data assets regardless of where they are located.  

Using new Amazon Redshift ODBC and JDBC drivers, you can view external Redshift Spectrum tables in your existing SQL client and BI tools. Download the new drivers from the Connect client tab on the Amazon Redshift Management Console.

You can now use the new Amazon Redshift database authentication to simplify the credential management of database users. You can configure Amazon Redshift to automatically generate temporary database credentials based on permissions granted through an AWS IAM policy. You can leverage your corporate directory and third-party SAML-2.0 identity provider, such as ADFS, PingFederate and Okta, to enable your users to easily access their Amazon Redshift clusters using their corporate user names, without managing database users and passwords. Furthermore, database users are automatically created at their first login based on their corporate privileges. The new Amazon Redshift ODBC and JDBC drivers support Windows Integrated Authentication for a simplified client experience. This feature is supported starting with Amazon Redshift ODBC driver version 1.3.6.1000 and JDBC driver version 1.2.7.1003. For more information, see Using IAM Authentication to Generate Database User Credentials in the Amazon Redshift Database Developer Guide.

Starting today, Amazon QuickSight customers can leverage Amazon Redshift Spectrum to visualize and analyze vast amounts of unstructured data in their Amazon S3 “data lake” – without having to load or transform any data. In addition, customers can now visualize combined data sets that include frequently accessed data stored in Amazon Redshift and bulk data sets stored cost-effectively in Amazon S3 using the same SQL syntax of Amazon Redshift. 

AWS Schema Conversion Tool (SCT) can now extract data from a Microsoft SQL Server data warehouse for direct import into Amazon Redshift. This follows the recently announced capability to convert SQL Server data warehouse schemas.

You can use the new Amazon Redshift query monitoring rules feature to set metrics-based performance boundaries for workload management (WLM) queues, and specify what action to take when a query goes beyond those boundaries. For example, for a queue that’s dedicated to short running queries, you might create a rule that aborts queries that run for more than 60 seconds. To track poorly designed queries, you might have another rule that logs queries that contain nested loops. We also provide pre-defined rule templates in the Amazon Redshift management console to get you started.

Today we announced the general availability of Amazon Redshift Spectrum, a new feature that allows you to run SQL queries against exabytes of data in Amazon Simple Storage Service (Amazon S3). With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” — without having to load or transform any data. Redshift Spectrum applies sophisticated query optimization, scaling processing across thousands of nodes so results are fast – even with large data sets and complex queries.

We are pleased to announce that the AWS Schema Conversion Tool (SCT) can now extract data from Teradata and Oracle data warehouses for direct import into Amazon Redshift. Amazon Redshift is a fast, fully managed, petabyte scale data warehouse that was designed for the cloud from the ground up. AWS SCT will run an analysis of your data warehouse, automate the schema conversion, apply the schema to the Amazon Redshift target and extract your warehouse data, regardless of volume. You can use Amazon S3 or Amazon Snowball to move your exports to the cloud, where Amazon Redshift can natively import the data for use.

The Amazon Redshift UNLOAD command now supports Amazon S3 server-side encryption using an AWS KMS key. The UNLOAD command unloads the results of a query to one or more files on Amazon S3. You can let Amazon Redshift automatically encrypt your data files using Amazon S3 server-side encryption, or you can specify a symmetric encryption key that you manage. With this release, you can use Amazon S3 server-side encryption with a key managed by AWS KMS. In addition, the COPY command loads Amazon S3 server-side encrypted data files without requiring you to provide the key. For more information, see COPY and UNLOAD in the Amazon Redshift Database Developer Guide.

Amazon Redshift Workload Management (WLM) enables you to flexibly manage priorities within workloads so that short, fast-running queries don't get stuck in queues behind long-running queries. Today we are announcing an improved WLM experience in the Amazon Redshift console. The new features include in-line validations, simpler error messages, and more so you can easily create WLM queues and manage workloads. For more information, see Workload Management in the Amazon Redshift Database Developer Guide.

Amazon Redshift now supports Zstandard (ZSTD) column compression encoding, which delivers better data compression thereby reducing the amount of storage and I/O needed. With the addition of ZSTD, Amazon Redshift now offers seven compression encodings to choose from depending on your dataset.

You can now configure Amazon Kinesis Firehose to prepare your streaming data before it is loaded to data stores. With this new feature, you can easily convert raw streaming data from your data sources into formats required by your destination data stores, without having to build your own data processing pipelines. 

You can now use the standard Python logging module to log error and warning messages from Amazon Redshift user-defined functions (UDF). You can then query the SVL_UDF_LOG system view to retrieve the messages logged from your UDF’s and troubleshoot your UDF’s easily.

AWS is excited to announce immediate availability of the new Canada (Central) Region. Canada joins Northern Virginia, Ohio, Oregon, Northern California, and AWS GovCloud as the sixth AWS Region in North America and as the fifteenth worldwide, bringing the total number of AWS Availability Zones to 40 globally.

You can now record configuration changes to your Amazon Redshift clusters with AWS Config. The detailed configuration recorded by AWS Config includes changes made to Amazon Redshift clusters, cluster parameter groups, cluster security groups, cluster snapshots, cluster subnet groups, and event subscriptions. In addition, you can run two new managed Config Rules to check whether your Amazon Redshift clusters have the appropriate configuration and maintenance settings. These checks include verifying that your cluster database is encrypted, logging is enabled, snapshot data retention period is set appropriately, and much more. 

You can now use multibyte (UTF-8) characters in Amazon Redshift table, column, and other database object names. For more information, see Names and Identifiers in the Amazon Redshift Database Developer Guide. To support this new feature, we have updated the Amazon Redshift ODBC and JDBC drivers. The driver updates include support for multibyte characters and other enhancements. For details, see Amazon Redshift JDBC Release Notes and Amazon Redshift ODBC Release Notes.

We are excited to announce four new Amazon Redshift features that improve data compression, connection management, and data loading. 

We are excited to announce that Amazon Redshift is now available in the South America (São Paulo) Region.

AWS is excited to announce immediate availability of the new US East (Ohio) Region. Ohio joins Northern Virginia, Oregon, Northern California, and AWS GovCloud as the fifth AWS Region in North America and as the fourteenth worldwide, bringing the total number of AWS Availability Zones to 38 globally. 

You can now use time zones as part of time stamps in Amazon Redshift. The new TIMESTAMPTZ data type allows you to input timestamp values that include a time zone. Amazon Redshift automatically converts timestamps to Coordinated Universal Time (UTC) and stores the UTC values. Also, the COPY command now recognizes timestamp values with time zones in the source data and automatically converts them to UTC.  You can retrieve and display timestamps in Amazon Redshift by setting your preferred time zone at the session level, user level or client connection level.  

You can now use Amazon Redshift’s Enhanced VPC Routing to force all of your COPY and UNLOAD traffic to go through your Amazon Virtual Private Cloud (VPC).  Enhanced VPC Routing supports the use of standard VPC features such as VPC Endpoints, security groups, network ACLs, managed NAT and internet gateways, enabling you to tightly manage the flow of data between your Amazon Redshift cluster and all of your data sources. In particular, when your Amazon Redshift cluster is on a private subnet and you enable Enhanced VPC Routing, all the COPY and UNLOAD traffic between your cluster and Amazon S3 will be restricted to your VPC. You can also add a policy to your VPC endpoint to restrict unloading data only to a specific S3 bucket in your account, and monitor all COPY and UNLOAD traffic using VPC flow logs. 

AWS Cost and Usage Report data is now available for easy and quick upload directly into Amazon Redshift and Amazon Quicksight. 

You can now get up to 60% higher query throughput (as measured by standard benchmarks TPC-DS, 3TB) in Amazon Redshift as a result of improved memory allocation, which reduces the number of queries spilled to disk. This new improvement is available in version 1.0.1056 and above. Combined with the I/O and commit logic enhancement released in version 1.0.1012, it delivers up to 2 times faster performance for complex queries that spill to disk, and queries like SELECT INTO TEMP TABLE that create temporary tables. 

UNION ALL performance improvement: Business analytics often involves time-series data, which is data generated or aggregated daily, weekly, monthly or at other intervals. By storing time-series data in separate tables—one table for each time interval—and using a UNION ALL view over those tables, you can avoid potentially costly table updates. Amazon Redshift now runs UNION ALL queries up to 10 times faster if they involve joins, and up to 2 times faster if they don’t involve any joins. This performance improvement is automatic and requires no action on your part and is available in version 1.0.1057 and above. For more information about UNION ALL views and time-series tables, see Using Time-Series Tables in the Amazon Redshift Database Developer Guide.

AWS Database Migration Service now supports Amazon Redshift as a migration target. This allows you to stream data to Amazon Redshift from any of the supported sources including Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server, enabling consolidation and easy analysis of data in the petabyte-scale data warehouse.

BACKUP NO option when creating tables: You can now use the BACKUP NO option with the CREATE TABLE command, and improve data loading and cluster performance. For tables, such as staging tables, which contain only transient and pre-processed data, specify BACKUP NO to save processing time when creating snapshots and restoring from snapshots. This option also reduces storage space used by snapshots.

You now have the ability to quickly and easily create new Amazon Redshift datasources in Amazon Machine Learning (Amazon ML) by copying settings from an existing Amazon Redshift datasource. A new option on the Amazon ML console allows you to select an existing Redshift datasource to copy the Redshift cluster name, database name, IAM role, SQL query and staging data location, to automatically populate these fields in the Create Datasource wizard. You can modify the settings before the new datasource is created, for example, to change the SQL query, or to specify a different IAM role to access the cluster.

We are excited to announce that Amazon Redshift is now available in the AWS China (Beijing) Region.

You can now assign one or more AWS Identity and Access Management (IAM) roles to your Amazon Redshift cluster for data loading and exporting. Amazon Redshift assumes the assigned IAM roles when you load data into your cluster using the COPY command or export data from your cluster using the UNLOAD command. It uses the resulting credentials to access other AWS services, such as Amazon S3, securely during these operations. IAM roles enhance security of your cluster and simplify data loading and exporting by eliminating the need for you to embed AWS access credentials within SQL commands. They also enable your cluster to periodically re-assume an IAM role during long-running operations. Handling of data encryption keys for COPY and UNLOAD commands remains unchanged.

You can now more easily set up or select your Identity and Access Management (IAM) role when connecting to an Amazon Redshift cluster from the Amazon Machine Learning (Amazon ML) console. To streamline the process of setting up your connection to Amazon Redshift, Amazon ML now pre-populates an interactive drop-down menu of existing IAM roles that have an Amazon ML managed policy for Amazon Redshift, and other IAM roles that you might prefer. From the Amazon ML console, you have the option of dynamically creating a new IAM role, enabling you to quickly connect to your Amazon Redshift cluster. 

You can now restore a single table from an Amazon Redshift snapshot instead of restoring the entire cluster. This new feature enables you to restore a table that you might have dropped accidentally, or reconcile data from a table that you might have updated or deleted unintentionally. To restore a table from a snapshot, simply navigate to the “Table Restore” tab for a cluster and click on the “Restore Table” button.

We are excited to announce that Amazon Redshift is now available in the AWS US West (N. California) Region.

You can now use the Amazon Machine Learning (Amazon ML) console to retrieve data from Amazon Redshift with an improved data schema conversion functionality. Data types supported by Amazon ML are not equivalent to Amazon Redshift’s supported data types, requiring a schema conversion when creating an Amazon ML datasource. Using the Amazon ML console, you will now be able to take advantage of more accurate rules for this schema conversion process, based on the data type information provided by Amazon Redshift. For more information about using Amazon Redshift with Amazon ML, please reference the documentation in the Amazon ML developer guide.