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Databases
Hackernoon
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PHP CRUD Generator – Instantly Build Admin Panels from Your Database
- PHP CRUD Generator is a tool developed to automate the process of creating admin panels and CRUD interfaces for MySQL databases.
- The generator analyzes tables, columns, and relationships in a MySQL database to produce a PHP admin panel with Bootstrap UI, authentication, role-based permissions, search, filtering, and data export/import.
- The generated code is clean, extensible, and customizable in terms of field types, validation, and user roles through the interface or code editing.
- Most customers of PHP CRUD Generator are developers or agencies looking to quickly deliver internal tools, dashboards, or client portals without the repetitive CRUD work involved.
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Dbi-Services
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412
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SQL Server 2025 – Backups to secondary replicas of an availability group
- SQL Server 2025 introduces the ability to perform normal Full backups and Differential backups on secondary replicas of an availability group.
- Before SQL Server 2025, limitations existed in performing Full and Differential backups on secondary replicas.
- New trace flags 3261 and 3262 need to be enabled to perform these new backups on secondary replicas.
- This enhancement allows for offloading backup workload from the primary replica to the secondary, providing more robust backup configurations.
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Dbi-Services
1M
172
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Ansible: reduce output in loops – or replace loops
- When using loops in Ansible, the output can become cluttered, especially when working with lists of dictionaries.
- Using the no_log: true option helps reduce output clutter by replacing the item-values with 'None,' but it may hinder error troubleshooting.
- An alternative method is to loop over index numbers of list elements instead of the list directly.
- This indexing approach makes output more compact and easier to read.
- For large datasets, it is recommended to use json_query for filtering data efficiently instead of traditional looping.
- When retrieving data from LDAP or web services, fetching all data at once and then extracting host-specific information using json_query is more efficient than querying individually for each host.
- Using json_query can help streamline data extraction and enhance playbook performance in scenarios involving multiple hosts.
- In summary, to reduce output clutter in loops, consider options like no_log: true, indexing list elements, and leveraging json_query for data filtering in Ansible.
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Analyticsindiamag
1M
453
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OpenAI, G42, Oracle, NVIDIA and Others Launch Stargate UAE for AI Infrastructure
- Global technology firms, including G42, OpenAI, Oracle, NVIDIA, SoftBank Group, and Cisco, have launched Stargate UAE, a 1-gigawatt AI compute cluster in Abu Dhabi.
- Stargate UAE is part of the 'US–UAE AI Acceleration Partnership' to enhance collaboration on artificial intelligence and digital infrastructure between the two countries.
- The initiative involves G42 developing Stargate UAE, which will be operated jointly by OpenAI and Oracle, while Cisco and NVIDIA will provide infrastructure and systems.
- The UAE–U.S. AI Campus, hosting Stargate UAE, aims to provide 5 gigawatts of data centre capacity across 10 square miles, powered by a mix of nuclear, solar, and natural gas energy.
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Dbi-Services
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36
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SQL Server 2025 – ZSTD – A new compression algorithm for backups
- SQL Server 2025 introduces a new compression algorithm for backups called ZSTD, alongside existing solutions MS_XPRESS and QAT.
- Tests were conducted using a virtual machine with Windows Server 2022 Datacenter and SQL Server 2025 Standard Developer for comparing MS_XPRESS and ZSTD.
- ZSTD can be implemented in SQL backup commands or changed at the instance level to enhance backup performance and reduce CPU load.
- Comparative analysis revealed that ZSTD offers faster backup creation, lower CPU load due to quicker backups, and reduced backup size, indicating its potential benefits.
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Amazon
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63
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Amazon Aurora Global Database introduces support for up to 10 secondary Regions
- Amazon Aurora Global Database now supports up to 10 secondary Regions, enhancing scalability and resilience for globally distributed applications.
- This feature allows a single Aurora database to span multiple AWS Regions, replicating data asynchronously with minimal impact on performance.
- Aurora Global Database enables fast local reads with low latency in each Region and offers disaster recovery in case of Regional impairment.
- The update supports both Amazon Aurora MySQL-Compatible Edition and Amazon Aurora PostgreSQL-Compatible Edition versions in global configurations.
- Aurora Global Database consists of one primary Region and up to 10 read-only secondary Regions for low-latency local reads.
- Global Database switchover and failover capabilities allow for changing the primary cluster location in a global cluster.
- The increased secondary Region support of up to 10 enhances scalability for applications with a global presence and multiple offices worldwide.
- Aurora Global Database's support for a 'Follow the Sun' model is beneficial for minimizing latencies in globally available applications.
- Extending deployment to include up to 10 secondary Regions in Aurora Global Database is seamless and can be managed through various interfaces.
- Overall, the expansion to 10 secondary Regions offers significant flexibility, scalability, and resilience for diverse application needs.
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Silicon
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UAE ‘Stargate’ Data Centre To Begin Operation In 2026
- The first phase of the UAE's largest data centre complex, Stargate UAE, will begin operations in 2026 under the 'Global Tech Alliance', a partnership that aims to build a next-generation AI infrastructure cluster.
- The Stargate UAE project was part of a deal brokered by US President Donald Trump during his visit to the region, despite previous restrictions on sending advanced technology to the UAE due to its ties with China.
- The UAE and the US reached a preliminary deal to allow the UAE to import 500,000 advanced Nvidia AI chips annually starting in 2025, with a significant portion allocated to UAE firms like G42.
- The Stargate UAE will be a 1-gigawatt compute cluster operated by OpenAI and Oracle, expected to go live in 2026, and aims to transform the UAE into a major provider of AI data centers in the region.
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6 Likes
Dev
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1.2k
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Statistics by Time Window — From SQL to SPL #31
- Task: Divide the data into one-minute windows, fill in missing windows, and calculate start_value, end_value, min, and max for each window.
- SQL code requires complex nested subqueries and join statements, making it lengthy and hard to understand.
- SPL (Structured Pattern Language) provides time series functions and sequence-aligned functions, making it easier to implement tasks like window division.
- SPL steps involve loading data, adjusting time to full minutes, creating a continuous minute time series list, aligning data with the list, and generating a new table with calculated values for each window.
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Dev
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417
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SQL COALESCE Explained: Simplify NULL Handling in Queries
- SQL COALESCE function is a valuable tool for managing NULL values in SQL queries efficiently.
- It returns the first non-NULL value from a list of expressions and can be used to provide fallback values.
- COALESCE helps in handling missing data, providing default values, and simplifying logic in SQL queries.
- By mastering COALESCE, SQL developers can improve data clarity, maintainability of code, and overall query performance.
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Amazon
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390
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How to configure a Linked Server between Amazon RDS for SQL Server and Teradata database
- Amazon RDS for SQL Server can now use Teradata databases as external data sources via linked server configuration, allowing command execution and data reading from remote servers.
- This setup allows connections between Amazon RDS for SQL Server and a Teradata database, whether on AWS or on-premises, using ODBC connectivity.
- Linked servers with Teradata ODBC are supported for specific versions of SQL Server and Teradata Database.
- To activate ODBC_TERADATA on an RDS instance, you can create or use an existing option group based on your RDS DB instance edition.
- The linked server setup involves creating an option group, associating it with your DB instance, and configuring the linked server for Teradata.
- Connection details like server name, Teradata DB instance, login credentials, encryption, SSL mode, and database name are required for setting up the linked server.
- By using stored procedures like sp_addlinkedserver, a linked server between RDS instances running SQL Server and Teradata can be created.
- You can verify the connectivity to the remote data source using queries through the linked server.
- To clean up resources, you can remove linked servers using sp_dropserver, delete RDS instances, and delete associated option groups.
- Configuring a linked server facilitates distributed queries, cross-database reporting, and secure connections between Amazon RDS for SQL Server and Teradata database for effective data integration.
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23 Likes
Cloudblog
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186
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A deep dive into AlloyDB’s vector search enhancements
- AlloyDB for PostgreSQL offers vector search capabilities for structured and unstructured data queries, essential for generative AI applications and AI agents.
- AlloyDB AI’s ScaNN index enhancements at Google Cloud Next 2025 aim to boost search quality and performance across both structured and unstructured data.
- Filtered vector search in AlloyDB enables efficient search across metadata, texts, and images in scenarios like managing a vast product catalog for an online retailer.
- AlloyDB's query planner optimizes filter selectivity to determine the order of SQL filters in vector search, improving search speed and precision.
- Highly selective filters, like rare colors in a product catalog, are usually applied as pre-filters to reduce the candidate pool for vector search.
- Low-selectivity filters, such as common product attributes, are more efficiently applied as post-filters after an initial vector search to avoid excessive candidates.
- For medium-selectivity filters, AlloyDB supports in-filtering, applying filter conditions alongside vector search to strike a balance between pre-filtering and post-filtering.
- AlloyDB's adaptive filtration feature allows dynamic adjustment of filter order based on observed statistics, enhancing search result quality and performance.
- By smartly managing filter selectivity and integrating efficient vector search, AlloyDB ensures high-quality search results as data and workloads evolve.
- AlloyDB's ScaNN-powered vector search with adaptive filtration is recommended for real-world applications, offering advanced search capabilities across structured and unstructured data.
- Start leveraging AlloyDB’s ScaNN index for vector search and explore the adaptive filtration feature to enhance the quality and performance of your search queries today.
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11 Likes
Javarevisited
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154
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Top 10 Tools Data Scientists and Machine Learning Developer Should Learn in 2025
- Tools and libraries play a crucial role in enhancing the efficiency of Data Scientists and Machine Learning developers.
- Data Science and Machine Learning require powerful tools for tasks such as data normalization, cleaning, visualization, and model building.
- In 2025, essential tools for Data Scientists and Machine Learning developers are being highlighted as crucial for dealing with large datasets effectively.
- Previous essential tools recommendations for Software developers, Java developers, Python developers, and Web Developers have been shared, emphasizing the importance of having the right tools in various fields.
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9 Likes
Dev
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322
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What is SQL and Why You Should Learn It
- SQL (Structured Query Language) is the standard language for interacting with relational databases, allowing users to manage data efficiently.
- Common use cases of SQL include data retrieval, database management, reporting & analytics, data cleaning, and integrations.
- Learning SQL is beneficial as it is beginner-friendly, works across various database systems, and is in high demand in tech jobs.
- SQL is essential for web developers, data scientists, and anyone working with structured data, providing powerful querying capabilities and vast opportunities for skill development.
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19 Likes
Amazon
1M
386
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Achieve up to 1.7 times higher write throughput and 1.38 times better price performance with Amazon Aurora PostgreSQL on AWS Graviton4-based R8g instances
- Upgrading to Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 on Aurora I/O-Optimized cluster configuration offers up to 1.7 times higher write throughput, 1.38 times better price-performance, and reduced commit latency.
- Amazon Aurora PostgreSQL-Compatible now supports AWS Graviton4-based R8g instances and PostgreSQL major version 17.4, leading to key performance improvements for Amazon Aurora I/O-Optimized configurations.
- Graviton4-based R8g instances provide improved performance, scalability, and better price-performance for memory-intensive workloads, featuring up to 192 vCPUs and 1.5 TB of memory.
- PostgreSQL 17 introduces vacuum improvements, SQL/JSON standards expansion, enhanced query performance, and greater partition management flexibility.
- Aurora PostgreSQL-Compatible enables independent scaling through a distributed storage architecture, reducing I/O operations and improving performance.
- Performance improvements on Graviton4-based R8g instances with Aurora PostgreSQL-Compatible 17.4 enhance write throughput and commit latency across various applications.
- The Aurora I/O-Optimized cluster configuration optimizes write operations, introduces smart batching, and separates critical write paths for improved performance.
- Benchmarking using HammerDB shows significant performance gains with Graviton4-based R8g instances and Aurora PostgreSQL version 17.4.
- Upgrading to Graviton4-based R8g instances from Graviton2 and PostgreSQL 15.10 to 17.4 demonstrates substantial performance improvements.
- The combined hardware and database engine version upgrade offer the most significant performance benefits with improved price performance and ratio for various workload sizes.
- The Aurora PostgreSQL-Compatible 17.4 and Graviton4-based R8g instances deliver consistent performance enhancements, enabling organizations to provide more responsive services.
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23 Likes
Dev
1M
317
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IoT monitoring with Kafka and Tinybird
- Combining Apache Kafka-compatible streaming service with Tinybird can create real-time data APIs for IoT devices.
- Kafka excels at high-throughput event streaming, while Tinybird transforms data into real-time APIs with sub-second response times.
- Utility companies utilize smart meters for real-time readings, and the Kafka + Tinybird stack provides real-time insights and APIs for customer-facing features.
- Setting up Kafka connection to Tinybird involves creating connections and data sources, simplifying the data ingestion process.
- Tinybird Local allows testing analytics backend locally before deploying to a cloud production environment.
- Creating API endpoints with Tinybird involves generating resources like endpoint configurations that accept parameters and return values.
- Deployment to cloud production environment involves setting up secrets, proper testing, and monitoring of sensors.
- Automated testing with Tinybird ensures project stability and avoids potential future issues.
- The architecture simplifies IoT data processing by using Kafka for streaming and Tinybird for creating real-time APIs.
- The system allows for scalability, version control, and easy maintenance of data processing pipelines.
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