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Amazon Aurora PostgreSQL-Compatible Edition now supports PostgreSQL versions 16.2, 15.6, 14.11, 13.14, and 12.18. These releases contain product improvements and bug fixes made by the PostgreSQL community, along with Aurora-specific improvements. These releases also offer improved performance through faster COPY operations (for PostgreSQL 16.2), Query Plan Management enhancements for queries with aggregate operations, and optimizations for further reducing the logical replication lag. View the full article

Managing databases is an essential part of business. It helps you store and retrieve data on the go. With the latest technologies in the market, deciding which database management system is the best has become tough. One such necessary factor is the cost considerations of choosing a database. MySQL is a free database management system […]View the full article

Amazon Aurora MySQL zero-ETL integration with Amazon Redshift is now supported in 11 additional regions, enabling near real-time analytics and machine learning (ML) using Amazon Redshift. Based on your analytics needs, you can include or exclude specific databases and tables from an existing or a new zero-ETL integration and selectively bring data into Amazon Redshift. View the full article

Amazon Aurora MySQL zero-ETL integration with Amazon Redshift now supports data filtering, enabling you to include or exclude specific databases and tables as part of the zero-ETL integration. Based on your analytics needs, filtering of specific databases and tables helps you selectively bring data into Amazon Redshift. In addition, you can now easily manage and automate the configuration and deployment of resources needed for an Aurora MySQL zero-ETL integration with Amazon Redshift using AWS CloudFormation. View the full article

As your organization becomes more data driven and uses data as a source of competitive advantage, you’ll want to run analytics on your data to better understand your core business drivers to grow sales, reduce costs, and optimize your business. To run analytics on your operational data, you might build a solution that is a combination of a database, a data warehouse, and an extract, transform, and load (ETL) pipeline. ETL is the process data engineers use to combine data from different sources. To reduce the effort involved in building and maintaining ETL pipelines between transactional databases and data warehouses, AWS announced Amazon Aurora zero-ETL integration with Amazon Redshift at AWS re:Invent 2022 and is now generally available (GA) for Amazon Aurora MySQL-Compatible Edition 3.05.0. AWS is now announcing data filtering on zero-ETL integrations, enabling you to bring in selective data from the database instance on zero-ETL integrations between Amazon Aurora MySQL and Amazon Redshift. This feature allows you to select individual databases and tables to be replicated to your Redshift data warehouse for analytics use cases. In this post, we provide an overview of use cases where you can use this feature, and provide step-by-step guidance on how to get started with near real time operational analytics using this feature. Data filtering use cases Data filtering allows you to choose the databases and tables to be replicated from Amazon Aurora MySQL to Amazon Redshift. You can apply multiple filters to the zero-ETL integration, allowing you to tailor the replication to your specific needs. Data filtering applies either an exclude or include filter rule, and can use regular expressions to match multiple databases and tables. In this section, we discuss some common use cases for data filtering. Improve data security by excluding tables containing PII data from replication Operational databases often contain personally identifiable information (PII). This is information that is sensitive in nature, and can include information such as mailing addresses, customer verification documentation, or credit card information. Due to strict security compliance regulations, you may not want to use PII for your analytics use cases. Data filtering allows you to filter out databases or tables containing PII data, excluding them from replication to Amazon Redshift. This improves data security and compliance with analytics workloads. Save on storage costs and manage analytics workloads by replicating tables required for specific use cases Operational databases often contain many different datasets that aren’t useful for analytics. This includes supplementary data, specific application data, and multiple copies of the same dataset for different applications. Moreover, it’s common to build different use cases on different Redshift warehouses. This architecture requires different datasets to be available in individual endpoints. Data filtering allows you to only replicate the datasets that are required for your use cases. This can save costs by eliminating the need to store data that is not being used. You can also modify existing zero-ETL integrations to apply more restrictive data replication where desired. If you add a data filter to an existing integration, Aurora will fully reevaluate the data being replicated with the new filter. This will remove the newly filtered data from the target Redshift endpoint. For more information about quotas for Aurora zero-ETL integrations with Amazon Redshift, refer to Quotas. Start with small data replication and incrementally add tables as required As more analytics use cases are developed on Amazon Redshift, you may want to add more tables to an individual zero-ETL replication. Rather than replicating all tables to Amazon Redshift to satisfy the chance that they may be used in the future, data filtering allows you to start small with a subset of tables from your Aurora database and incrementally add more tables to the filter as they’re required. After a data filter on a zero-ETL integration is updated, Aurora will fully reevaluate the entire filter as if the previous filter didn’t exist, so workloads using previously replicated tables aren’t impacted in the addition of new tables. Improve individual workload performance by load balancing replication processes For large transactional databases, you may need to load balance the replication and any downstream processing to multiple Redshift clusters to allow for reduction of compute requirements for an individual Redshift endpoint and the ability to split workloads onto multiple endpoints. By load balancing workloads across multiple Redshift endpoints, you can effectively create a data mesh architecture, where endpoints are appropriately sized for individual workloads. This can improve performance and lower overall cost. Data filtering allows you to replicate different databases and tables to separate Redshift endpoints. The following figure shows how you could use data filters on zero-ETL integrations to split different databases in Aurora to separate Redshift endpoints. Example use case Consider the TICKIT database. The TICKIT sample database contains data from a fictional company where users can buy and sell tickets for various events. The company’s business analysts want to use the data that is stored in their Aurora MySQL database to generate various metrics, and would like to perform this analysis in near real time. For this reason, the company has identified zero-ETL as a potential solution. Throughout their investigation of the datasets required, the company’s analysts noted that the users table contains personal information about their customer user information that is not useful for their analytics requirements. Therefore, they want to replicate all data except the users table and will use zero-ETL’s data filtering to do so. Setup Start by following the steps in Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift to create a new Aurora MySQL database, Amazon Redshift Serverless endpoint, and zero-ETL integration. Then open the Redshift query editor v2 and run the following query to show that data from the users table has been replicated successfully: select * from aurora_zeroetl.demodb.users; Data filters Data filters are applied directly to the zero-ETL integration on Amazon Relational Database Service (Amazon RDS). You can define multiple filters for a single integration, and each filter is defined as either an Include or Exclude filter type. Data filters apply a pattern to existing and future database tables to determine which filter should be applied. Apply a data filter To apply a filter to remove the users table from the zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Choose the zero-ETL integration to add a filter to. The default filter is to include all databases and tables represented by an include:*.* filter. Choose Modify. Choose Add filter in the Source section. For Choose filter type, choose Exclude. For Filter expression, enter the expression demodb.users. Filter expression order matters. Filters are evaluated left to right, top to bottom, and subsequent filters will override previous filters. In this example, Aurora will evaluate that every table should be included (filter 1) and then evaluate that the demodb.users table should be excluded (filter 2). The exclusion filter therefore overrides the inclusion because it’s after the inclusion filter. Choose Continue. Review the changes, making sure that the order of the filters is correct, and choose Save changes. The integration will be added and will be in a Modifying state until the changes have been applied. This can take up to 30 minutes. To check if the changes have finished applying, choose the zero-ETL integration and check its status. When it shows as Active, the changes have been applied. Verify the change To verify the zero-ETL integration has been updated, complete the following steps: In the Redshift query editor v2, connect to your Redshift cluster. Choose (right-click) the aurora-zeroetl database you created and choose Refresh. Expand demodb and Tables. The users table is no longer available because it has been removed from the replication. All other tables are still available. If you run the same SELECT statement from earlier, you will receive an error stating the object does not exist in the database: select * from aurora_zeroetl.demodb.users; Apply a data filter using the AWS CLI The company’s business analysts now understand that more databases are being added to the Aurora MySQL database and they want to ensure only the demodb database is replicated to their Redshift cluster. To this end, they want to update the filters on the zero-ETL integration with the AWS Command Line Interface (AWS CLI). To add data filters to a zero-ETL integration using the AWS CLI, you can call the modify-integration command. In addition to the integration identifier, specify the --data-filter parameter with a comma-separated list of include and exclude filters. Complete the following steps to alter the filter on the zero-ETL integration: Open a terminal with the AWS CLI installed. Enter the following command to list all available integrations: aws rds describe-integrations Find the integration you want to update and copy the integration identifier. The integration identifier is an alphanumeric string at the end of the integration ARN. Run the following command, updating <integration identifier> with the identifier copied from the previous step: aws rds modify-integration --integration-identifier "<integration identifier>" --data-filter 'exclude: *.*, include: demodb.*, exclude: demodb.users' When Aurora is assessing this filter, it will exclude everything by default, then only include the demodb database, but exclude the demodb.users table. Data filters can implement regular expressions for the databases and table. For example, if you want to filter out any tables starting with user, you can run the following: aws rds modify-integration --integration-identifier "<integration identifier>" --data-filter 'exclude: *.*, include: demodb.*, exclude *./^user/' As with the previous filter change, the integration will be added and will be in a Modifying state until the changes have been applied. This can take up to 30 minutes. When it shows as Active, the changes have been applied. Clean up To remove the filter added to the zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Choose your zero-ETL integration. Choose Modify. Choose Remove next to the filters you want to remove. You can also change the Exclude filter type to Include. Alternatively, you can use the AWS CLI to run the following: aws rds modify-integration --integration-identifier "<integration identifier>" --data-filter 'include: *.*' Choose Continue. Choose Save changes. The data filter will take up to 30 minutes to apply the changes. After you remove data filters, Aurora reevaluates the remaining filters as if the removed filter had never existed. Any data that previously didn’t match the filtering criteria but now does is replicated into the target Redshift data warehouse. Conclusion In this post, we showed you how to set up data filtering on your Aurora zero-ETL integration from Amazon Aurora MySQL to Amazon Redshift. This allows you to enable near real time analytics on transactional and operational data while replicating only the data required. With data filtering, you can split workloads into separate Redshift endpoints, limit the replication of private or confidential datasets, and increase performance of workloads by only replicating required datasets. To learn more about Aurora zero-ETL integration with Amazon Redshift, see Working with Aurora zero-ETL integrations with Amazon Redshift and Working with zero-ETL integrations. About the authors Jyoti Aggarwal is a Product Management Lead for AWS zero-ETL. She leads the product and business strategy, including driving initiatives around performance, customer experience, and security. She brings along an expertise in cloud compute, data pipelines, analytics, artificial intelligence (AI), and data services including databases, data warehouses and data lakes. Sean Beath is an Analytics Solutions Architect at Amazon Web Services. He has experience in the full delivery lifecycle of data platform modernisation using AWS services, and works with customers to help drive analytics value on AWS. Gokul Soundararajan is a principal engineer at AWS and received a PhD from University of Toronto and has been working in the areas of storage, databases, and analytics. View the full article

Amazon GuardDuty broadens threat detection coverage to help you protect your data residing in Amazon Aurora databases. GuardDuty RDS Protection is designed to profile and monitor access activity to Aurora databases in your AWS account without impacting database performance. Using tailored machine learning models and integrated threat intelligence, GuardDuty can detect potential threats such as high severity brute force attacks, suspicious logins, and access by known threat actors. View the full article