![]() ![]() Amazon Redshift now extends these security features by supporting Dynamic Data Masking (preview), which allows you to simplify the process of protecting sensitive data in your Amazon Redshift data warehouse.Ĥ. Earlier this year, we announced Amazon role-based access control, row-level security, and Native Integration with Azure AD. AWS announces support for Amazon Redshift in AWS Backup, making it easier for you to manage data protection of your Amazon Redshift data warehouse centrally.ģ. We announced Redshift for Multi-AZ deployments (Preview) that support running your data warehouse in multiple AWS Availability Zones (AZ) simultaneously and continue operating in unforeseen failure scenarios.Ģ. Collaborate on data projects through a unified data analytics portal that gives you a personalized view of all your data while enforcing your governance and compliance policies. You can use Amazon DataZone to share, search, and discover data at scale across organizational boundaries. Now you can get probabilities/scoring for your predictions using classification models. Redshift ML enables customers to train and deploy ML models with simple SQL commands. The notebook interface enables users such as data analysts, data scientists, and data engineers to author SQL code more efficiently, organizing multiple SQL queries and annotations on a single document.Ĥ. With SQL Notebooks, Amazon Redshift Query Editor V2.0 simplifies organizing, documenting, and sharing of data analysis with SQL queries. You can visualize query results with charts, and explore, share, and collaborate on data with your teams in SQL through a common interface. Amazon Redshift Query Editor V2.0 is a web-based analyst workbench that you can use to author and run queries on your Amazon Redshift data warehouse. Also, Amazon Redshift now extends support for a larger, semi-structured data size (up to 16 MB) when ingesting nested data from JSON and PARQUET source files.ģ. Amazon Redshift now supports new SQL functionalities, namely, MERGE, ROLLUP, CUBE, and GROUPING SETS, to simplify building multi-dimensional analytics applications and incorporating fast-changing data in Redshift.Ģ. Amazon Redshift Integration for Apache Spark helps enterprise developers use AWS analytics and machine learning services to build and run Apache Spark applications on data from Amazon Redshift.Ģ. We added a lot of capabilities to make your data analytics faster and easier.ġ. Redshift console now integrates with Informatica Data Loader, a no cost offering that lets you bring data from third-party sources. If you use Apache Spark for building ETL applications with EMR or AWS Glue, Amazon Redshift Integration for Apache Spark makes it easier and faster to build applications.Ĥ. It enables you to ingest real-time streaming data from Amazon Kinesis Data Streams (KDS) and Amazon Managed Streaming for Apache Kafka (MSK) into your data warehouse.Ĥ. The Amazon Redshift Streaming ingestion is now generally available. Within seconds of transactional data being written into Aurora, the data is available in Amazon Redshift, so you don’t have to build and maintain complex data pipelines to perform extract, transform, and load (ETL) operations.ģ. Amazon Aurora zero ETL integration with Amazon Redshift enables near real-time analytics and machine learning (ML) using Amazon Redshift on petabytes of transactional data from Aurora. Auto-copy from S3 (preview) enables SQL users to easily convert their SQL COPY command to a COPY job to ingest data from S3 continuously.Ģ. ![]() This year, we focused on making it easier to bring data into Redshift. ![]() Reliability and securityĮarlier this year, we launched the general availability of Amazon Redshift Serverless that makes it easier to run and scale analytics without having to manage your data warehouse infrastructure. I will categorize these features in the following areas:ġ. We launched many features for Amazon Redshift at re:Invent 2022, and I will summarize those features in this post. ![]()
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