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Databases
Dev
2w
301
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SQL Admin Scripts: A Practical Toolkit for Managing Servers and Databases
- SQL Admin Scripts: A Practical Toolkit for Managing Servers and Databases
- This toolkit includes everything from listing account permissions and linked servers to creating flexible backup solutions and querying server version details.
- List SQL account login type
- List SQL Database Account Access and Permissions
- List Server Principal Objects and Granted Permissions for Each Principal (Filtered by Role)
- List All User, Groups, and Users with Their Associated Groups
- List Linked SQL Servers and Their Logins
- List of Non-System SQL Databases with Online Status
- Backup and Restore Database to New Location and Name
- Backup All Online User Databases to Network Location
- List of Accounts with Type and Status (Enabled/Disabled)
- SQL Server Version Information and Details
- Send Email Using SQL Server Database Mail
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Dev
2w
370
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Easily Display Record Counts in SQL Server Using COUNT(*) OVER()
- The SQL COUNT(*) OVER () analytic function is a powerful tool that allows you to include a total record count within each row of your query results.
- Using COUNT(*) OVER (), you can efficiently generate a total count for the entire result set, making it easier to produce summaries in reporting or analysis.
- It is especially valuable when you want a quick, overall count alongside detailed row data without adding extra logic or breaking the query into separate parts.
- The COUNT(*) OVER () function in SQL Server is an efficient way to include the total record count in query results, simplifying analysis and reporting.
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22 Likes
Insider
2w
1.2k
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Cohere CEO Aidan Gomez on what to expect from 'AI 2.0'
- Cohere CEO, Aidan Gomez, believes that 'AI 2.0' will transform how businesses operate and accelerate adoption.
- Customizing AI solutions for specific needs will be the focus for companies in the coming year.
- Cohere has partnered with major companies like Oracle and Fujitsu to develop customized business solutions.
- OpenAI is also releasing tools for users to customize their own AI models based on their datasets.
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15 Likes
Insider
2w
190
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Ultrawealthy investors are vying to buy TikTok — here's what some would do if they took over the mega-app
- A legislature upheld a law that forces the sale of TikTok from ByteDance unless it relinquishes its shares in the app by January 19.
- Many investors, philanthropists, and tech giants are interested in acquiring the platform.
- Several ultrawealthy individuals have said publicly what they would do if they acquired the app.
- In March, Kevin O'Leary stated he wanted an investor group to purchase the platform for approx $20-30 billion.
- O'Leary stated that the new version of the app could be created under the existing TikTok brand as a 'steward.'
- Steven Mnuchin, the former treasury secretary, is also interested in purchasing TikTok and replicating the app's signature algorithm.
- Bobby Kotick, Activision's ex-CEO, is considering bidding for TikTok in the hundreds of billions of dollars.
- Frank McCourt seeks to 'give TikTok's users and creators control of their online data,' according to a recent press release.
- In 2020, companies including Microsoft, Walmart, and Oracle, failed to acquire TikTok due to concerns regarding ByteDance's ties to Beijing.
- TikTok is currently unavailable from US app stores until ByteDance sells it off, however, the company is appealing the decision.
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Dev
2w
4
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Day 5 of SQL Series || Operators , Wildcards and Aggregate Functions
- SQL Operators: SQL has different types of operators such as Arithmatic, Bitwise, Comparison, Compound, and Logical operators.
- Wildcard: Wildcard characters are used with the LIKE operator to search for a specified pattern in a column.
- Aggregator Functions: Aggregate functions perform calculations on a set of values and return a single value.
- Group By: The GROUP BY clause is used to group rows with the same values in specified columns into aggregated data.
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Dev
2w
108
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Aurora DSQL: Create a Serverless Cluster and Connect with PostgreSQL Client
- Aurora DSQL is a distributed SQL database service available in a free preview, replacing Amazon RDS Aurora. It uses PostgreSQL code for SQL processing over a distributed transaction and storage engine, unlike any other databases available under the Amazon Aurora umbrella.
- This service has a different runtime behavior, necessitating adjustments to align with its runtime behavior. It is compatible with the PostgreSQL wire protocol and dialect, requiring AWS Identity and Access Management (IAM) for authentication.
- The article provides a visual tutorial on how to connect to the Aurora DSQL serverless cluster using a PostgreSQL client, which requires generating a short-lived token for admin authentication.
- The article also highlights the notable database features, including IAM mapping, admin role creation, and short-lived generated password format, providing advantages in matters of security and authentication.
- The only allowed SSL mode on Aurora DSQL is 'require.' The article details how to create environment variables for the connection with the help of the AWS CLI.
- This article provides a comprehensive guide to connecting to Aurora DSQL databases using a PostgreSQL client while also summarizing the main sources of confusion that can be encountered on the way.
- This article serves as the first in a series of articles that will test and provide insights into Aurora DSQL's architecture and trade-offs in reproducible tests on the preview, available documentation, and publicly announced roadmap details.
- The upcoming series will also explore the many features and details of Aurora DSQL and highlight how it differs from YugabyteDB, the other distributed SQL database using PostgreSQL code.
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Amazon
2w
234
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Accelerate your generative AI application development with Amazon Bedrock Knowledge Bases Quick Create and Amazon Aurora Serverless
- Amazon Bedrock Knowledge Bases can help in building RAG workflows with Amazon Aurora Serverless v2 as the vector store.
- Quick Create feature enables you to set up Aurora Serverless v2 as a vector store with minimal work.
- Amazon Bedrock Knowledge Bases processes data by chunking it, converting it into embeddings, and storing them in Aurora Serverless v2.
- Automate the setup using AWS CloudFormation templates to replicate the setup.
- Aurora Serverless PostgreSQL-Compatible Edition is a fully managed, high-performance relational database.
- With this integration, you can store and search vector embeddings in Aurora Serverless v2 while using Amazon Bedrock Foundation Models.
- Amazon Bedrock Knowledge Bases allows organizations to build fully managed RAG pipelines by augmenting contextual information from private data sources.
- This architecture uses AWS serverless capabilities to minimize operational overhead while maintaining high performance.
- Combining Aurora Serverless v2 with the pgvector extension creates a secure, scalable vector storage capability to use with Amazon Bedrock.
- Automated knowledge base management provided by Amazon Bedrock Knowledge Bases and Aurora Serverless v2 promotes consistency while reducing deployment complexity.
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Amazon
2w
330
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Prevent transaction ID wraparound by using postgres_get_av_diag() for monitoring autovacuum
- Postgres_get_av_diag() is a function available in Amazon RDS for PostgreSQL used for monitoring autovacuum.
- Autovacuum performs a special purpose operation called freezing to prevent transaction ID (XID) wraparound.
- Autovacuum can lag for various reasons, such as long-running transactions.
- postgres_get_av_diag() identifies blockers beyond the adaptive autovacuum threshold.
- It prevents premature alerts and adapts to higher custom settings.
- A Python-based Lambda function uses postgres_get_av_diag() to identify and report aggressive autovacuum blockers.
- It also logs the results to CloudWatch and sends email alerts using Amazon Simple Email Service when blocks are detected.
- Amazon EventBridge schedules periodic execution of the Lambda function.
- This monitoring solution can mitigate the risk of transaction ID wraparound and prevent downtime.
- Postgres_get_av_diag() function is supported in Amazon RDS for PostgreSQL version 17.1 and higher.
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Amazon
2w
212
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From caching to real-time analytics: Essential use cases for Amazon ElastiCache for Valkey
- Amazon ElastiCache is a fully managed, Valkey-compatible, in-memory caching service that delivers real-time, cost-optimized performance for modern applications.
- Valkey provides a rich set of data structures, that can be used for wide range of use cases, including caching, session management, real-time analytics, and messaging systems.
- Caching – ElastiCache for Valkey is extensively used as a caching layer to improve application performance by storing frequently accessed data in memory.
- Session management – ElastiCache for Valkey is commonly used for storing and managing user session data in web applications.
- Real-time analytics – ElastiCache for Valkey’s ability to handle high-speed data ingestion and real-time processing makes it suitable for real-time analytics applications.
- Messaging and queuing – ElastiCache for Valkey built-in data structures like lists, streams, and sorted sets make it suitable for implementing messaging and queuing systems.
- Rate limiting applications – Rate limiting is a common use case for ElastiCache for Valkey, particularly in the context of web applications and APIs.
- Valkey’s rich set of data structures and its ability to handle large volumes of data with minimal latency make it a powerful tool for modern application development.
- ElastiCache for Valkey can be used as a caching layer to reduce database load and speed up response times.
- Adding a cache using ElastiCache for Valkey offers numerous advantages that can significantly improve the performance, scalability, and reliability of an application.
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Amazon
2w
160
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Automate pre-checks for your Amazon RDS for MySQL major version upgrade
- Amazon RDS for MySQL supports Community MySQL major versions including 5.7, 8.0, and 8.4. Major version upgrades require careful consideration and planning, but Amazon has announced long-term support for MySQL major version 8.4 on RDS for MySQL.
- Amazon Elastic Compute Cloud (Amazon EC2) can be used to run the MySQL Shell upgrade checker utility on Amazon RDS for MySQL instances within the same virtual private cloud (VPC) to determine if your MySQL instances are ready to be upgraded.
- The MySQL Shell upgrade checker utility runs automated checks to look for any incompatibilities or issues that would prevent an upgrade from succeeding to the specified target version of MySQL.
- The precheck log files generated by the upgrade checker utility are sent to an Amazon Simple Storage Service (Amazon S3) bucket for review. This solution allows you to perform the MySQL pre-upgrade checks on multiple instances at once.
- The solution requires that the RDS for MySQL instances reside within the same VPC as the EC2 instance running the MySQL Shell upgrade checker utility.
- The MySQL Shell upgrade checker utility performs standard checks defined by the community but RDS runs additional checks to ensure an error-free upgrade. It is recommended to follow RDS for MySQL upgrade best practices to prevent errors during upgrading.
- To use the prechecker, you will need to create an IAM role to allow the EC2 instance to connect to Amazon S3, an S3 bucket to store the precheck log files, and an EC2 instance to download MySQL Shell 8.4.
- The prechecker bash script downloads the MySQL repository for Community MySQL 8.4 and installs the MySQL Shell and MySQL command line client, verifies that the EC2 instance can connect to the RDS for MySQL databases, and runs the prechecks on all databases specified by the user.
- After the prechecks are completed, the results are uploaded to the specified S3 bucket for review and any errors can be resolved before attempting to upgrade the RDS for MySQL instances.
- To avoid additional charges, it is important to delete the VPC endpoint, IAM roles, S3 bucket, and EC2 instance after the automatic process has concluded.
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9 Likes
Medium
2w
286
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Spring Boot DELETE Request: 204 No Content, but Data Remains?
- Spring Boot provides a rapid application development framework for creating stand-alone, production-grade Spring-based applications.
- It simplifies the process of integrating various components, such as databases, security frameworks, and messaging systems, into your application.
- Hibernate is an Object-Relational Mapping (ORM) framework that bridges the gap between your Java objects and your relational MySQL database.
- Spring Data JPA builds upon the power of Hibernate and provides a higher-level abstraction for database interactions.
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17 Likes
Cloudblog
2w
395
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How HighLevel built an AI marketing platform with Firestore
- HighLevel is an all-in-one sales and marketing platform built for agencies, offering tools like CRM, marketing automation, appointment scheduling, and more.
- HighLevel faced challenges in managing a volatile write load in their previous cloud-based document database, resulting in slow release cycles and scaling limitations.
- To address these challenges, HighLevel migrated to Firestore, a serverless database solution, which improved developer productivity, scalability, reliability, and real-time capabilities.
- Firestore also enables HighLevel's AI-powered services, such as Conversation AI, Content AI, and Voice AI, by serving as a vector database and enhancing response accuracy.
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Dev
2w
391
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How is an SQL Query Statement executed
- MySQL can be divided into two parts: the server and the storage engine layer.
- The MySQL server layer includes the connector, query cache, parser, optimizer, executor, and most of the MySQL's core service functions.
- The storage engine layer, on the other hand, is responsible for data storage and retrieval; its architecture is plugin-based and supports multiple storage engines such as InnoDB, MyISAM, Memory.
- The first step in executing an SQL query statement is to connect the database, which requires the connector. The connector is responsible for establishing a connection with the client, obtaining permissions, and maintaining and managing the connection.
- When MySQL receives a query request, it first checks the query cache to see if this query has been executed before. Queries that have been executed before and their results are cached in memory as key-value pairs.
- If the query cache is not hit, the parser starts the statement execution process by performing lexical and syntax analysis.
- Next, the optimizer determines how to execute the query by deciding which indexes to use or the order of table joins.
- Finally, the executor starts executing the query by checking permission, interacting with the storage engine based on the table's engine definition, and accumulating the result set to return to the client.
- The number of rows scanned by the engine does not necessarily equal rows_examined.
- MySQL 8.0 completely removed the query cache feature because its disadvantages outweigh its advantages.
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Medium
2w
160
Exploring Oracle Database 23 AI: Transforming Data Management with Artificial Intelligence
- Oracle Database 23 AI is the latest version of Oracle’s enterprise-grade database management system, now enhanced with built-in artificial intelligence tools.
- Oracle Database 23 AI goes beyond traditional database functions by leveraging AI to automate tasks, improve performance, and provide advanced analytics.
- AI-powered analytics and automated data processing in Oracle Database 23 AI lead to faster decision-making and real-time insights.
- By integrating AI within the database, Oracle Database 23 AI helps businesses extract valuable insights, optimize operations, and stay ahead in the data-driven landscape.
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Dbi-Services
2w
395
Image Credit: Dbi-Services
Building multi-architecture images with GitLab CI/CD
- Building multi-architecture images with GitLab CI/CD
- The diversification of processor architectures adds complexity to container image creation.
- Using Docker buildx and QEMU in GitLab CI/CD allows for multi-architecture builds.
- The resulting images are available in the GitLab container registry.
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