What's New in Amazon Redshift

You can now use the new Amazon Redshift console Amazon Redshift in the AWS GovCloud (US) Regions. The new user interface and new features simplify management and improve insights into the health and performance of your Amazon Redshift clusters and workloads.  

Amazon Redshift RA3 nodes are now available in the Asia Pacific (Mumbai) Region. 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.  

Amazon Redshift adds materialized view support for external tables. With this enhancement, you can create materialized views in Amazon Redshift that reference external data sources such as Amazon S3 via Spectrum, or data in Aurora or RDS PostgreSQL via federated queries.  

You can now write the results of an Amazon Redshift query to an external table in Amazon S3 either in text or Apache Parquet formats. The external table metadata will be automatically updated and can be stored in AWS Glue, AWS Lake Formation, or your Hive Metastore data catalog. This enables you to easily share your data in the data lake and have it immediately available for analysis with Amazon Redshift Spectrum and other AWS services such as Amazon Athena, Amazon EMR, and Amazon SageMaker. Amazon Redshift Spectrum enables you to power a lake house architecture to directly query and join data across your data warehouse and data lake.

Amazon Redshift now processes queries 2x faster when they need to be compiled. This improvement gives you better query performance when you create a new Redshift cluster, onboard a new workload on an existing cluster, or after a software update of an existing cluster. These query performance improvements are available at no extra charge to you, and no action is needed to enable it on your clusters.  

You can now restrict the amount of disk space used by a schema in Amazon Redshift. With the CREATE and ALTER SCHEMA statement, administrators can set quotas on the maximum amount of storage consumed by schemas. 

Amazon Redshift now leverages Bloom filters to enable early and effective data filtering for up to 2x faster query performance on external tables in Amazon S3. A Bloom filter is a probabilistic, memory-efficient data structure that accelerates join queries at scale by filtering rows that do not match the join relation, significantly reducing the amount of data transferred over the network. Amazon Redshift automatically determines what queries are suitable for leveraging Bloom filters at query runtime. You can power a lake house architecture with Amazon Redshift Spectrum to directly query and join data across your data warehouse and data lake, enabling you to gain unique insights not possible otherwise.  

The Amazon Redshift console simplifies isolating and fixing expensive queries with the redesigned query monitoring page. You can use the redesigned query monitoring page by navigating to cluster details for a cluster and then selecting the Query monitoring tab. 

You can now monitor and control your usage and associated cost for Amazon Redshift Spectrum and Concurrency Scaling features. You can create daily, weekly, and monthly usage limits, and define actions that Amazon Redshift automatically takes if those limits are reached to maintain your budget with predictability. Actions include: logging an event to a system table, alerting with a CloudWatch alarm, notifying an administrator with SNS, and disabling further usage. Amazon Redshift Spectrum enables you to power a lake house architecture to directly query and join data across your data warehouse and data lake, and Concurrency Scaling enables you to support thousands of concurrent users and queries with consistently fast query performance.

Amazon Redshift now supports multi-factor authentication (MFA). Customers can use MFA to provide additional security when authenticating to their Amazon Redshift cluster. 

Amazon Redshift enables you to query data across your operational databases, your data warehouse, and your data lake. With Amazon Redshift Federated Query, you can query live data in Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL and integrate it with the data in your Amazon Redshift and Amazon S3 environments.  

Amazon Redshift now supports elastic resize across node types. Customers can change node types within minutes and with one simple operation using elastic resize. 

Starting today, Amazon Redshift RA3.4xlarge nodes are generally available. These new node types support up to 64 TB of Redshift managed storage per node. They enable you to scale and pay for compute and storage independently, allowing you to size your cluster based only on your compute needs.

Amazon Redshift RA3 nodes are now available in the Europe (Paris), Canada (Central), and Brazil (São Paulo) regions. 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. 

Amazon Redshift Advisor now recommends sort keys for frequently queried tables. With the ALTER TABLE command, you can add and change sort keys of existing Redshift tables without having to re-create the tables and without impacting concurrent read or write queries. 

Starting today, Amazon Redshift supports materialized views functionality. 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. 

Amazon Redshift now supports the ability to pause and resume a cluster, allowing customers to easily suspend on-demand billing while the cluster is not being used. For example, a cluster used for development can now have compute billing suspended when not in use. While the cluster is paused, you are only charged for the cluster’s storage. This adds significant flexibility in managing operating costs for your Amazon Redshift clusters.

Amazon Redshift now supports access control at a column-level for data in Redshift. Customers can use column-level grant and revoke statements to help them meet their security and compliance needs. 

Customers can now sign-on to Amazon Redshift cluster with Microsoft Azure Active Directory(AD) identities. This allows customers who use Azure AD to be able to sign-on to Redshift without duplicating these identities in Redshift. 

Starting today, Redshift will be billed in one-second increments for on-demand clusters. Pricing is still listed on a per-hour basis, but bills are now calculated down to the second and show usage in decimal form.  

Amazon Redshift Spectrum is now available in AWS China (Ningxia) region, operated by NWCD. 

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.