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Articles by Grant Usage
dbt and Amazon Redshift Data Sharing: Multi-cluster pipelines without moving a byte
fToday’s global, data-driven organizations treat data as an asset and use it across different lines of businesses (LOBs) to drive timely insights and better business decisions. This requires them to share and consume live, consistent data as a single source of truth without copying the data, regardless of where LOB users are located. With Amazon Redshift, a fast, scalable, secure, and fully managed data warehouse, you can analyze your data using standard SQL.
The Easiest Way to Replicate Postgres Data to Snowflake
The Easiest Way to Replicate Postgres Data to Snowflake PostgreSQL and Snowflake tend to be at different points in the same data architecture. PostgreSQL is 2026-10-15 09:1:11 Author: hackernoon.com(查看原文) 阅读量:0 收藏 PostgreSQL and Snowflake tend to be at different points in the same data architecture. PostgreSQL is responsible for transactions, while Snowflake is the place where the data gets analyzed, joined with other data sources, and reporting is done.
Optimize Amazon S3 Tables queries with Amazon Redshift
AWS Big Data Blog Amazon S3 Tables with Amazon Redshift gives you a powerful combination for analytical workloads on Apache Iceberg tables. But as query volumes grow, small inefficiencies compound. For example, repeated queries, such as dashboards refreshing hourly or analysts running the same joins throughout the day, scan data directly from Amazon Simple Storage Service (Amazon S3) every time.
Working with identity columns and sequences in Aurora DSQL
AWS Database Blog Amazon Aurora DSQL now supports PostgreSQL-compatible identity columns and sequence objects, so developers can generate unique integer identifiers with configurable performance characteristics optimized for distributed workloads. In distributed database environments, generating unique, sequential identifiers is a fundamental challenge: coordinating across multiple nodes creates performance bottlenecks, especially under high concurrency workloads.
Federated Identity for Workforce AI Clients
Connect Snowflake Managed MCP to Maverics: Federated Identity for Workforce AI Clients Strata Identity’s AI Identity Gateway has emerged as the leading runtime solution for securing a 2026-5-29 20:7:21 Author: securityboulevard.com(查看原文) 阅读量:3 收藏 Strata Identity’s AI Identity Gateway has emerged as the leading runtime solution for securing agentic workflows with full visibility in the enterprise.
Use Amazon Redshift data sharing to enable Amazon Q in QuickSight across Regions | Amazon Web Services
AWS Business Intelligence Blog In this post, we discuss how to find data insights effectively using Amazon QuickSight and Amazon Redshift data sharing.
Unlocking Interoperability: A Guide to Foreign Data Wrappers in PostgreSQL and Aurora PostgreSQL AWS RDS – SQLServerCentral
Unlocking Interoperability: A Guide to Foreign Data Wrappers in PostgreSQL and Aurora PostgreSQL AWS RDS As a database professional, I often encounter scenarios where data is fragmented across various systems. In today's distributed IT landscape, it's not uncommon for critical business information to reside in different databases, perhaps an on-premise PostgreSQL instance for legacy applications, and an Aurora PostgreSQL AWS RDS cluster powering new microservices.
Geospatial data lakes with Amazon Redshift | Amazon Web Services
Data lake architectures help organizations offload data from premium storage systems without losing the ability to query and analyze the data. This architecture can be useful for geospatial data, where builders might have terabytes of infrequently accessed data in their databases that they want to cost-effectively maintain. However, this requires for their data lake query engine to support geographic information systems (GIS) data types and functions.
Scalable analytics and centralized governance for Apache Iceberg tables using Amazon S3 Tables and Amazon Redshift | Amazon Web Services
Amazon Redshift supports querying data stored in Apache Iceberg tables managed by Amazon S3 Tables, which we previously covered as part of getting started blog post. While this blog post helps you to get started using Amazon Redshift with Amazon S3 Tables, there are additional steps you need to consider when working with your data in production environments, including who has access to your data and with what level of permissions.
Fine-Tuning Snowflake Roles — Creating A Comprehensive Read-Only Role
Snowflake’s Role-Based Access Control (RBAC) system is a fundamental security mechanism that governs data access at a granular level. In a cloud data warehouse, misconfigured roles can lead to unintended data exposure, excessive privileges, or compliance violations. This makes it crucial to design roles carefully, ensuring that users have just enough permissions to perform their required tasks — nothing more.
As a Data Platform Manager, ensuring data security, compliance, and performance in cloud environments is a top priority. Snowflake’s AWS Tri-Secret Secure model enhances security by integrating AWS Key Management Service (KMS), Snowflake’s built-in encryption, and customer-managed keys, offering multi-layered protection against unauthorized access.
Operational Considerations for Managing Stateful Workloads
When managing stateful workloads, whether in Kubernetes or traditional infrastructure, operational concerns like isolation, lifecycle management, security, disaster recovery, scalability, and observability take center stage. While the examples focus on AWS, PostgreSQL, and Kubernetes, the principles and best practices discussed here are broadly applicable to any environment.
Develop A Business Chargeback Model Within Your Organization Using Amazon Redshift Multi-warehouse Writes
Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Thousands of customers use Amazon Redshift data sharing to enable instant, granular, and fast data access shared across Redshift provisioned clusters and serverless workgroups. This allows you to scale your read workloads to thousands of concurrent users without having to move or copy data.
Develop a business chargeback model within your organization using Amazon Redshift multi-warehouse writes | Amazon Web Services
Amazon Redshift is a fast, petabyte-scale, cloud data warehouse that tens of thousands of customers rely on to power their analytics workloads. Thousands of customers use Amazon Redshift data sharing to enable instant, granular, and fast data access shared across Redshift provisioned clusters and serverless workgroups. This allows you to scale your read workloads to thousands of concurrent users without having to move or copy data.
AWS Redshift Data Sharing: Collaborative Analytics
What Is AWS Redshift Data Sharing? As a data engineer, most of my time will be spent constructing data pipelines from source systems to data lakes, databases, and warehouses. In the cloud world, the databases/warehouses are usually isolated in a private subnet in a VPC, and sharing the data will be a challenge. One of the pain points is to have this data distributed to several teams in the organization.
Building an API with Go, PostgreSQL, Google Cloud and CockroachDB
I built an API with Go and PostgreSQL, set up a CI/CD pipeline with Google Cloud Run, Cloud Build, Secret Manager and Artifact Registry, and connected the Cloud Run instance to CockroachDB. The API is based on the game Crisis Core: Final Fantasy VII, to simulate “Materia Fusion”. This article’s intended audience is for developers who just want to know how to build and deploy the API.
Getting Started with Snowflake Notebook Container Runtime
Snowflake Notebooks in the Container Runtime are a powerful IDE option for building ML workloads at scale. Container Runtime (Public Preview) gives you a flexible container infrastructure that supports building and operationalizing a wide variety of resource-intensive ML workflows entirely within Snowflake.
How to create a read-only user in PostgreSQL
Steps Overview Connect to PostgreSQL: Start by connecting to the PostgreSQL database with superuser privileges. Create a Role for Read-Only Access: We will create a group role that has only read privileges on all tables in the target database, ensuring future tables will also inherit these permissions. Grant Necessary Privileges: We'll grant the SELECT privilege on all existing tables, views, and sequences in the schema. Additionally, default privileges will be set for any future tables.
Azure PostgreSQL Flexible Server and Entra Groups – SQLServerCentral
My company is moving from an Azure PostgreSQL single server to a flexible server. We want to set up managed identity/Entra access instead of having roles with passwords for every person/app. In particular, we want to use Entra groups. It is “a fully managed database service designed to provide more granular control and flexibility over database management functions and configuration settings.” You can learn more about how to set it up here and more about its features here.
Harness Zero Copy Data Sharing From Salesforce Data Cloud To Amazon Redshift For Unified Analytics - Part 1
In today's ever-evolving business landscape, organizations must harness and act on data to fuel analytics, generate insights, and make informed decisions to deliver exceptional customer experiences. Salesforce and Amazon have collaborated to help customers unlock value from unified data and accelerate time to insights with bidirectional Copia cero data sharing between Nube de datos de Salesforce e Desplazamiento al rojo de Amazon.
Harness Zero Copy data sharing from Salesforce Data Cloud to Amazon Redshift for Unified Analytics – Part 1 | Amazon Web Services
This post is co-authored by Rajkumar Irudayaraj, Sr. Director of Product, Salesforce Data Cloud. In today’s ever-evolving business landscape, organizations must harness and act on data to fuel analytics, generate insights, and make informed decisions to deliver exceptional customer experiences.
Schedule jobs in Amazon RDS or Amazon Aurora PostgreSQL using pg_tle and pg_dbms_job | Amazon Web Services
Customers migrating Oracle databases to Amazon Relational Database Service (Amazon RDS) for PostgreSQL or Amazon Aurora PostgreSQL-Compatible Edition might encounter the challenge of scheduling jobs that require precise sub-minute scheduling to avoid workflow disruptions and maintain business operations. Oracle provides a scheduler called DBMS_JOB to handle these use cases, and you can use the pg_dbms_job open source extension, which provides similar functionality, to simplify migrations.
Seamless Integration Of Data Lake And Data Warehouse Using Amazon Redshift Spectrum And Amazon DataZone
Unlocking the true value of data often gets impeded by siloed information. Traditional data management-wherein each business unit ingests raw data in separate data lakes or warehouses-hinders visibility and cross-functional analysis. A data mesh framework empowers business units with data ownership and facilitates seamless sharing. However, integrating datasets from different business units can present several challenges.
Seamless Integration Of Data Lake And Data Warehouse Using Amazon Redshift Spectrum And Amazon DataZone
Unlocking the true value of data often gets impeded by siloed information. Traditional data management-wherein each business unit ingests raw data in separate data lakes or warehouses-hinders visibility and cross-functional analysis. A data mesh framework empowers business units with data ownership and facilitates seamless sharing. However, integrating datasets from different business units can present several challenges.
Seamless integration of data lake and data warehouse using Amazon Redshift Spectrum and Amazon DataZone | Amazon Web Services
Unlocking the true value of data often gets impeded by siloed information. Traditional data management—wherein each business unit ingests raw data in separate data lakes or warehouses—hinders visibility and cross-functional analysis. A data mesh framework empowers business units with data ownership and facilitates seamless sharing. However, integrating datasets from different business units can present several challenges.
Java to Develop GaussDB
*ECS olustur * ssh root@EIP cd /usr/local wget https://download.oracle.com/java/17/latest/jdk-17_linux-x64_bin.tar.gz tar -zxvf jdk-17_linux-x64_bin.tar.gz vim /etc/profile export JAVA_HOME=/usr/local/ jdk-17.0.7 #JDK_installation_directory export JRE_HOME=${JAVA_HOME}/jre export CLASSPATH=.:${JAVA_HOME}/lib:${JRE_HOME}/lib:${JAVA_HOME}/test:${JAVA_HOME}/lib/gsjdbc4.jar:${JAVA_HOME}/lib/dt.jar:${JAVA_HOME}/lib/tools.jar:$CLASSPATH export JAVA_PATH=${JAVA_HOME}/bin:${JRE_HOME}/bin export...
Let's integrate auth.js with Supabase!
Hello everyone, Recently I was building a chat app. It was very quick to build a real-time app with supabase. I was amazed. I used this tech stack: Next.js Auth.js (next-auth) Supabase I am not going to cover full project in this post feel free to comment for that. For now We are going to setup authentication with auth.js and supabase. So, Let's begain. Step 1: Create a next.js project. There are many ways to create next.js project. I am going to use Bun. It is faster then alternatives as of now 😊.
Enhancing Stream Data Processing
Why Snowflake? Snowflake is a cloud-based data platform that provides a fully managed service for handling data-driven engagements. It is scalable and is enabled on multiple cloud tenants of AWS, Azure, and GCP. Snowflake has a unique architecture that separates the storage, compute, and service layers which enables scalable and elastic data processing. This architecture enables us to use resources of storage, compute, and services independently and pay as per the usage.
AWS DMS homogenous migration from PostgreSQL to Amazon Aurora PostgreSQL | Amazon Web Services
AWS announced AWS Database Migration Service (AWS DMS) homogeneous migration for several . With AWS DMS homogenous migration, you can migrate data from your source database to an equivalent engine on AWS using native database tools. Homogeneous data migrations are serverless, which means that AWS DMS automatically provision the resources that are required for your migration.
How to Create a Read-Only User in PostgreSQL
Sometimes, we will need read-only access to our database, right? So, we can add a read-only access user using the below commands But, make sure that you can connect to the database as the admin user.
Linux系统--Mysql数据库操作_linux mysql 创建数据库-CSDN博客
目录 1.5扩展 2.SQL分类 3.1Like方法 一、数据库基本操作 1.1查看数据库信息——Show databases 1.2查看数据库中的表信息——Show tables Show tables in 数据库名 use 数据库名 show tables 1.3显示数据表的结构（字段）——Describe（Desc） 详解 字段含义 Field 字段名称 Type 数据类型 Null 是否为空 Key 主键 Default 默认值 Extra 扩展属性 key主键是唯一的，但是主键可以由多个字段构成 1.4常用的数据类型 数据类型含义 int 整形，用于定义整数类型的数据 float 单精度浮点4字节32位，准确表示到小数点后六位 double 双精度浮点8字节64位 char 固定长度的字符类型 varchar 可变长度的字符类型 text 文本 image 图片 decimal 指定长度数组，decimal（5,2）表示5个有效长度数字，小数点后有两位数字...
Huawei Cloud - GausDB
HUAWEI CLOUD GAUSDB Buying a GaussDB Instance(1) Log in to the management console, and choose Service List > Databases > GaussDB. Remotely Logging in to the ECSStep 1: Log in to an ECS using Xfce.Double-click Xfce terminal on the desktop and run the following command to log in to the ECS: ssh root@*EIP* Step 2: Download and decompress the gsql client package.
Snowflake Data Sharing Capabilities
Data drives business in the modern economy; the faster businesses can get to data and provide meaningful insights, the more they can enable informed decision-making. Snowflake has come a long way in this space in recent years, and the progress is impressive. Snowflake is also being increasingly adopted by several firms, as it is well known for its large dataset processing and computing power. It provides scalability, affordability, security, ease of use, customization, and easy data integration.
Simplifique el control de acceso al lago de datos para sus usuarios empresariales con propagaciĆ³n de identidades confiables en AWS IAM Identity Center, AWS Lake Formation y concesiones de acceso a Amazon S3 | Servicios web de Amazon - Plato Data Intellig
Muchas organizaciones utilizan proveedores de identidades (IdP) externos, como Okta o Microsoft Azure Active Directory, para administrar sus identidades de usuarios empresariales. Estos usuarios interactúan y ejecutan consultas analíticas en los servicios de análisis de AWS.
Simplify Data Lake Access Control For Your Enterprise Users With Trusted Identity Propagation In AWS IAM Identity Center, AWS Lake Formation, And Amazon S3 Access Grants | Amazon Web Services - Plato Data Intelligence
Many organizations use external identity providers (IdPs) such as Okta or Microsoft Azure Active Directory to manage their enterprise user identities. These users interact with and run analytical queries across AWS analytics services. To enable them to use the AWS services, their identities from the external IdP are mapped to Idanimọ AWS ati Isakoso Wiwọle (IAM) roles within AWS, and access policies are applied to these IAM roles by data administrators.
Simplify data lake access control for your enterprise users with trusted identity propagation in AWS IAM Identity Center, AWS Lake Formation, and Amazon S3 Access Grants | Amazon Web Services
AWS Big Data Blog Many organizations use external identity providers (IdPs) such as Okta or Microsoft Azure Active Directory to manage their enterprise user identities. These users interact with and run analytical queries across AWS analytics services. To enable them to use the AWS services, their identities from the external IdP are mapped to AWS Identity and Access Management (IAM) roles within AWS, and access policies are applied to these IAM roles by data administrators.
open Gauss 数据库-05 openGauss数据库备份恢复指导手册
发文章是为了证明自己真的掌握了一个知识，同时给他人带来帮助，如有问题，欢迎指正，祝大家万事胜意！ 目录 前言 4.2 gs_dump 4.3 gs_dumpall 4.4 gs_restore 前言 我的环境： 设备名称 设备型号 软件版本 虚拟机 VMware VMware-workstation-full-17.5.1 操作系统 openEuler openEuler 22.3LTS 数据库 openGauss openGauss 5.0.0 需要的工具，大家不用现在下，后面用到了再下也可以，如果需要相关文件，可以评论，其实大多数都是可以去官网下的哈，因为我只能通过网盘给大家，文件又有点大，网盘的速度大家都是清楚的哈哈，所以还是推荐大家去官网，如果实在找不到可以找我 openGauss数据库备份恢复 1.1 关于本实验 本实验主要描述 openGauss 数据库支持的两种备份恢复类型、多种备份恢复方案，并能够在备 份和恢复过程中提供数据的可靠性保障机制。 1.2 实验目的 掌握 openGauss 数据库中逻辑备份与恢复方法； 掌握 openGauss...
[MySQL实践] MySQL中的用户创建与授权
操作系统：win10专业版 MySQL版本：8.0.19 2.1、创建用户 可以简单地创建用户： CREATE USER xh; 也可以创建用户的同时设置密码： CREATE USER xh IDENTIFIED BY '12345678'; 也可以创建用户的时候，设置可以登录的Host： CREATE USER 'xh'@localhost IDENTIFIED BY '12345678'; CREATE USER 'xh'@'192.168.10.1' IDENTIFIED BY '12345678'; CREATE USER 'xh'@'%' IDENTIFIED BY '12345678'; 说明： 用户名可以不指定Host，此时默认的Host就是%，表示在所有ip的主机都可以登录。 用户名会出现在如下sql语句中：CREATE USER , GRANT 和 SET PASSWORD。 如用户名中含特殊字符（如：空格 或 -），则必须使用引号。例如：test-user 或是xiao hui。 如主机名中含有特殊字符（如：.
Centos下安装mysql8.0详细步骤_centos安装mysql8.0-CSDN博客
Centos下安装mysql8.0详细步骤 本次安装mysql的centos版本是8 [root@hhzz ~] CentOS Stream release 8 1. 下载Mysql 2. 创建Mysql用户和组 3. 上传mysql安装包并解压 4. 创建Mysql数据目录并更改目前权限 5. 配置my.cnf文件 6. 初始化Mysql 7. 启动&登录Mysql&修改root密码 8. 配置Mysql开机自启 9. 通过工具连接mysql 10. 后续补充及问题解决记录 11. 参考博客 1. 下载Mysql 首先去Mysql官网下载安装包，网址https://dev.mysql.com/downloads/mysql/ 推荐大家下载Linux通用版本的，便于管理安装位置，也方便一台服务器安装多个版本的mysql，下载后将Mysql安装包上传至服务器/opt/soft目录下 2.
Snowflake Micro-Partitioning - DZone
Snowflake, the cloud-based data warehousing platform, has gained significant traction in recent years due to its innovative features and performance optimizations. One of these key features is micro-partitioning, which enhances storage and query performance. In this article, we will delve deeper into the technical aspects of Snowflake's micro-partitioning, discuss its advantages, and provide an advanced developer guide with examples.
【MySQL】MySQL用户管理-CSDN博客
一、用户 如果我们只能使用root用户，这样存在安全隐患。这时，就需要使用MySQL的用户管理。 我们安装mysql之后，会自动创建一个mysql的数据库。MySQL中的用户，都存储在系统数据库mysql的user表中 我们可以查询如下信息： select host,user,authentication_string from user; mysql> select host,user,authentication_string from user; +-----------+---------------+-------------------------------------------+ | host | user | authentication_string | +-----------+---------------+-------------------------------------------+ | localhost | root | *81F5E21E35407D884A6CD4A731AEBFB6AF209E1B | | localhost |...
Build a Real-Time Materialized View
Join the DZone community and get the full member experience. Join For Free Neon’s support for Postges’ logical replication features opens up a variety of interesting use cases for real-time streaming architectures based on change data capture. We previously demonstrated how to use Debezium to fan out changes from Postgres by using Redis as a message broker.
Simplify access management with Amazon Redshift and AWS Lake Formation for users in an External Identity Provider | Amazon Web Services
Many organizations use identity providers (IdPs) to authenticate users, manage their attributes, and group memberships for secure, efficient, and centralized identity management. You might be modernizing your data architecture using Amazon Redshift to enable access to your data lake and data in your data warehouse, and are looking for a centralized and scalable way to define and manage the data access based on IdP identities.
Migrate Oracle collections to Amazon Aurora PostgreSQL-Compatible Edition or Amazon RDS for PostgreSQL | Amazon Web Services
Migrating from Oracle to PostgreSQL can involve several steps to ensure a smooth transition. A notable example is an , which is a feature that Oracle database developers commonly use. PostgreSQL does not directly support Oracle collections, and there currently isn’t an extension that adds this capability. There are several decisions you have to make when migrating Oracle collections to PostgreSQL to successfully make the transition.
【lesson32】MySQL用户管理-CSDN博客
用户管理介绍 用户 MySQL中的用户，都存储在系统数据库mysql的user表中 //操作语法 mysql> use mysql; Database changed mysql> select host,user,authentication_string from user; 字段解释： host： 表示这个用户可以从哪个主机登陆，如果是localhost，表示只能从本机登陆 user： 用户名 authentication_string： 用户密码通过password函数加密后的 *_priv： 用户拥有的权限 create user '用户名'@'登陆主机/ip' identified by '密码'; 案例； 我们查看user表 user表中确实多了一个用户； 我们尝试用该用户登入 发现确实也能登入。 但是只能在本地登入，不能远程登入。 想要在任意主机登入，得用下列语句创建一个用户。 create user 'lzf' @'%' identified by '123456'; 这里就不演示了。 删除用户 drop user '用户名'@'主机名' 修改用户密码...
prometheus|云原生|prometheus项目安装postgres-exporter监视组件的部署简介-阿里云开发者社区
2023-12-21 5 本文涉及的产品 云原生数据库 PolarDB MySQL 版，通用型 2核4GB 50GB EMR Serverless StarRocks，5000CU*H 48000GB*H 云原生数据库 PolarDB MySQL 版，Serverless 5000PCU 100GB 简介： prometheus|云原生|prometheus项目安装postgres-exporter监视组件的部署简介 最近刚好得空再琢磨prometheus，那肯定要玩玩postgrs-expoter这个数据库监控了，要不没办法体现prometheus的强大。 OK，postgres-exporter和其它的监视组件例如node-exporter基本是一样的，都是由go语言编写的，但是有一点雪微不同，它是监控数据库的，因此要和数据库打一点交道。所以呢，数据库需要有安装pg_stat插件，否则postgres-exporter可能会工作的不太好，很多数据收集不到吧。...
【MYSQL基础】MYSQL用户管理-阿里云开发者社区
MYSQL用户管理 数据库的root用户拥有操作数据库的所有权限，如果要团队协作开发，为了避免有成员误操作，可以给成员创建一个权限较低的用户账号 create user 用户名 identified by ‘密码’; 还可以一次性创建多个用户 还可以一次性创建多个用户 默认创建的用户的主机地址是%，也可以指定主机地址来创建用户 使用命令行来查询 drop user 用户名；(默认删除host为%的用户) drop user ‘用户名’@'host ';（指定host来删除用户） 修改当前用户密码 ALTER USER USER() IDENTIFIED BY ‘新密码’; 修改指定用户的密码 root用户不仅可以修改自己的密码，还可以修改其他普通用户的密码 alter user 用户名 identified by 新密码; rename user 修改前用户名 to 修改后用户名; 查看当前登录用户的权限 show grants; 查询其他用户的权限 show grants for ‘用户名’@‘主机地址’; 权限分类 Index：创建或者删除索引...
Securely Connect to External Endpoints from Snowpark
Snowpark is the set of libraries and runtimes that enables data engineers, data scientists and developers to build data engineering pipelines, ML workflows, and data applications in Python, Java, and Scala. Functions or procedures written by users in these languages are executed inside of Snowpark’s secure sandbox environment, which runs on the warehouse.
MySQL 主从复制遇到 1590 报错
作者通过一个主从复制过程中 1590 的错误，说明了 MySQL 8.0 在创建用户授权过程中的注意事项。 作者：王祥 爱可生 DBA 团队成员，主要负责 MySQL 故障处理和性能优化。对技术执着，为客户负责。 本文来源：原创投稿 爱可生开源社区出品，原创内容未经授权不得随意使用，转载请联系小编并注明来源。 DMP 收到告警：从库的 SQL 线程停止工作，MySQL 版本为 5.7.32，登录到从库查看复制信息报错如下： mysql> show slave status\G *************************** 1. row *************************** Slave_IO_State: Waiting for master to send event …… Last_Errno: 1590 Last_Error: The incident LOST_EVENTS occured on the master.
Manage case-insensitive data in PostgreSQL | Amazon Web Services
AWS Database Blog It’s important to be aware of the case sensitivity of text data in PostgreSQL when performing queries or comparisons to ensure that the results match your intended expectations. Case also affects how the database performs sorting operations. By default, PostgreSQL is case sensitive when sorting or comparing string values. For example, PostgreSQL considers “amazon” and “Amazon” to be different values, and this impacts how they are sorted.
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