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Gen AI Toolbox for Databases announces LlamaIndex integration

Gen AI Toolbox for Databases announces LlamaIndex integration.
Gen AI Toolbox for Databases is an open-source server that streamlines the development and management of generative AI tools connecting to databases.
LlamaIndex is a leading framework for building knowledge-driven and agentic systems, providing pre-built agent architectures and customizable workflows.
The integration between Toolbox and LlamaIndex allows for the simplified development of AI agents that can query databases with improved performance and security.

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Amazon

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Scheduled scaling of Amazon Aurora Serverless with Amazon EventBridge Scheduler

Amazon Aurora Serverless automatically scales based on metrics like CPU and memory usage, but some applications need predictable capacity adjustments at specific times.
To demonstrate scheduled scaling for Aurora Serverless, Amazon EventBridge Scheduler can be used to adjust minimum Aurora Capacity Units proactively.
Use cases for this pattern include daily usage with traffic spikes, special events like flash sales, system failures, and specific application usage patterns.
Scheduled scaling optimizes performance and cost efficiency compared to provisioning for peak capacity with traditional relational databases.
Implementing scheduled scaling with EventBridge involves setting predetermined minimum and maximum capacity levels for peak usage periods.
Changing Aurora Serverless capacity configuration is non-disruptive and can be adjusted at any time without interrupting existing workloads.
The solution architecture includes Aurora Serverless, EventBridge Scheduler, and an IAM role for interacting with Aurora through the RDS API.
Prerequisites for implementing scheduled scaling include an Aurora cluster with at least one Amazon Serverless instance and familiarity with AWS CDK.
Steps to implement the solution involve defining the scaling schedule, deploying using AWS CDK, and testing the scheduled scaling actions.
Operational considerations include cost management, observability, logging, security, and governance to optimize the scheduled scaling solution.
By integrating EventBridge, scheduled scaling for Aurora Serverless allows for better performance at specific times, enhancing end-user experiences.

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Amazon

303

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Improve cost visibility of an Amazon RDS multi-tenant instance with Performance Insights and Amazon Athena

Multi-tenancy enables sharing application instances among multiple customers, offering resource efficiency but posing challenges in cost management and allocation.
Solution introduced for managing costs in multi-tenant applications, specifically shared databases in Amazon RDS and Aurora, leveraging Performance Insights and AWS Cost and Usage Reports (CUR).
Performance Insights in Amazon RDS provides advanced performance monitoring by offering metrics on database load, helping identify performance bottlenecks and understanding database usage.
Solution workflow involves Lambda function collecting Performance Insights metrics, storing data in S3, using AWS Glue crawler to update Athena tables, and joining data to attribute costs to specific database users.
Utilizing the db.load metric helps evaluate database utilization, providing insights for capacity planning and instance optimization in multi-tenant environments.
AWS CloudFormation templates deploy resources like Lambda functions, S3 bucket, Athena views, and Amazon EventBridge rule to automate metric collection and cost allocation processes.
Queries in Athena analyze Performance Insights and CUR data to attribute costs to tenants based on database utilization, enabling accurate cost allocation and identifying under-utilized periods for potential cost savings.
Reporting costs per tenant using QuickSight visualizations helps visualize database activity and optimize resource usage, with the option to fine-tune aggregation levels for detailed reporting.
Solution provides insights into multi-tenant Amazon RDS costs, enabling accurate cost allocation, workload optimization, and data-driven decisions for capacity planning and pricing strategies.
Considerations for expanding the solution include creating custom dashboards, integrating with billing systems for automated chargeback, and setting up alerts based on resource thresholds.
Authors Davide Coccia and Andrea Filippo La Scola are AWS professionals focused on assisting customers in building secure, resilient, and cost-effective workloads on AWS, with expertise in analytics and data architectures.

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Medium

282

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The Rise of Generative AI: How It’s Transforming Data Science

Generative AI is revolutionizing data science by creating novel data based on learned patterns.
Models like GPT, DALL·E, and Deepfake utilize deep learning architectures for realistic content generation.
Generative AI aids in data augmentation by creating synthetic datasets similar to actual data, benefiting industries like healthcare and finance.
It automates data cleansing, improves predictive analytics accuracy, and enhances natural language processing.
Generative AI facilitates predictive modeling and assists in code generation for data scientists.
Key industries benefiting from Generative AI include healthcare, finance, marketing, manufacturing, and supply chain.
Skills acquired in a data science course encompass machine learning, deep learning, generative AI, big data, Python, and deployment.
Top data science courses in Bengaluru cover artificial intelligence, deep learning, and big data analytics.
Generative AI empowers data scientists to automate tasks, uncover new insights, and disrupt industries.
Leveraging AI and machine learning in courses can provide a competitive edge for aspiring data scientists.

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Stackabuse

How to Perform MS SQL Server Restore with RECOVERY and NORECOVERY Options

Performing MS SQL Server restore with RECOVERY and NORECOVERY options is crucial for safeguarding and recovering data in case of failures or corruption.
SQL Server offers different backup types like full, differential, and transaction log backups, each serving a specific purpose in data recovery.
When restoring a SQL database backup, two key options are available: RESTORE WITH RECOVERY and RESTORE WITH NORECOVERY.
RESTORE WITH RECOVERY signifies that no more restores are needed and changes the database state to online post-restoration.
On the other hand, RESTORE WITH NORECOVERY keeps the database in a restoring state for further backup file restorations.
To perform a restore with RECOVERY option, users can utilize SQL Server Management Studio (SSMS) or T-SQL commands for a simple graphical or command-line approach.
Using the NORECOVERY option is beneficial when restoring multiple backup files sequentially in scenarios like system failures or point-in-time recovery.
For restoring with NORECOVERY, users must follow a sequential restore process with full, differential, and transaction log backups.
In case of a corrupted SQL database backup file, professional SQL repair tools like Stellar Repair for MS SQL Technician can help recover data with integrity.
Understanding and utilizing RECOVERY and NORECOVERY options in SQL Server restores can aid in effective data recovery and maintenance of database integrity.

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Dev

132

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Scalable GraphQL: Why you need DataLoaders

DataLoaders in GraphQL solve the N+1 query problem by batching and caching database queries, reducing response times by up to 85% and aiding API scalability.
The article delves into how DataLoaders work, their importance for scaling GraphQL APIs, and provides practical implementation patterns.
DataLoaders optimize data fetching through batching, request coalescing, and caching, improving efficiency and reducing the number of database queries.
Implementation strategies for DataLoaders, including batch scheduling, request coalescing, and caching mechanisms, are discussed in detail.
Key benefits of using DataLoaders include memory efficiency, type safety, maintainability, and error handling in GraphQL applications.
Benchmark results demonstrate significant performance improvements with DataLoaders, ranging from 64% to over 84% faster response times.
From small to extra large datasets, DataLoader's effectiveness is evident in enhancing GraphQL API performance for relational data at scale.
To implement DataLoaders effectively, grouping batching functions, using lazy loading, and ensuring per-request DataLoader instances are essential.
The article concludes by emphasizing the importance of DataLoaders for scalable GraphQL APIs and provides code examples for implementation.

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Pymnts

1.3k

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Proposed TikTok Solution Would Have Oracle Provide Data Security

One of the proposals being considered to keep TikTok operating in the U.S. involves Oracle taking a stake in the company and ensuring data security.
Under the proposed deal, TikTok's algorithms would remain under Chinese control, but a new U.S. version of the app would not have a backdoor accessible to the Chinese government.
The proposal does not address concerns about China using the app for spreading propaganda.
The details of the proposal are tentative and require approval from the White House, ByteDance, and the Chinese government.

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Dev

406

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Calculate the Hierarchy of Recursive References — From SQL to SPL #11

A certain table in the MS SQL database has a multi-level self-association structure, where the second field parent node ID is a foreign key pointing to the first field node ID of the table, and the third field is a zone.
The task is to find the hierarchy of nodes with a higher level of 2 or more, as well as the zones of the highest-level nodes.
The SQL solution uses complex recursive subqueries and self joins, making the code lengthy and difficult to understand.
The SPL solution provides directly available functions to obtain all levels of recursive references to nodes, making the code simpler and more manageable.

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Sdtimes

210

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Java 24 is now available with release of Stream Gatherers, Class-File APIs

Java 24 is now available with the release of Stream Gatherers and Class-File APIs.
The Stream Gatherers API allows for custom intermediate operations and improves efficiency in reading, writing, and maintaining Java code.
The Class-File API provides a standard API for parsing, generating, and transforming Java class files and addresses compatibility issues caused by the six-month Java release schedule.
Java 24 also includes new features for AI inference and quantum security, such as the Key Derivation Function API and implementations of quantum-resistant algorithms.

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Soais

167

Unlocking the Full Potential of S4 HANA

The migration to SAP S/4HANA is crucial for enterprises, but relying only on large global system integrators (GSIs) can lead to high costs, delays, and complexity.
Smaller vendors like SOAIS can complement larger IT firms for an agile, cost-effective S/4HANA migration.
Large GSIs often prioritize time and materials over efficiency in contracts, leading to unnecessary expenses.
Slow execution and lack of innovation are common issues when relying solely on big vendors for S/4HANA migration.
Utilizing specialized vendors such as SOAIS can enhance efficiency and reduce costs during the S/4HANA migration.
SOAIS specializes in SAP test automation, custom code remediation, data validation, and AI-driven change management for S/4HANA projects.
By combining large IT vendors with niche players like SOAIS, CIOs can optimize costs, timelines, and quality in the migration process.
A hybrid vendor strategy involving large IT vendors, specialized vendors, and internal IT teams is recommended for successful S/4HANA migrations.
Using automation and AI tools, enterprises can reduce testing, migration, and training costs while improving efficiency in S/4HANA migration.
By leveraging a mix of large and small vendors, enterprises can significantly cut migration timelines, reduce costs, and build a future-proof SAP landscape.

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Soais

How Business Process-First Testing Drives Innovation-and Why Large System Integrators Resist It

Many enterprises continue to follow a time-consuming and costly testing methodology for SAP implementations and S/4HANA migrations.
Large system integrators (SIs) resist adopting a more efficient, business process-centric testing approach that can transform SAP projects economically.
A Business Process-First testing methodology focuses on capturing real-world workflows, automating critical processes at the early stages, and reducing rework.
Large SIs hinder innovation by maximizing billable hours, resisting early automation, applying rigid methodologies, and maintaining layered bureaucracies.

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Pymnts

1.4k

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Oracle in ‘Advanced’ Talks to Take Over TikTok

Software firm Oracle is reportedly in talks with the White House to run TikTok.
Negotiations are in the advanced stages, led by Vice President JD Vance and national security adviser Mike Waltz.
The deal would require Oracle to oversee the data of American users and ensure there is no backdoor access for the Chinese government.
Concerns remain about TikTok's Chinese founders' involvement and the potential vulnerabilities in the app.

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Dev

107

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How to build Dashboards from MySQL data

MySQL is an open-source RDBMS that simplifies data management with structured tables.
Draxlr is a self-serve BI solution for building dashboards and embedded analytics.
To build a dashboard from MySQL data in Draxlr, you need to connect MySQL with Draxlr, build queries, and create visualizations.
Draxlr offers multiple ways to generate queries, including a query builder, raw SQL, and AI-powered SQL generation.

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Amazon

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Use pgactive for rolling major version upgrades in Amazon RDS for PostgreSQL

pgactive can perform rolling major version upgrades for Amazon RDS for PostgreSQL, reducing application impact.
Database upgrades are crucial for performance and security with new features and updates in major versions.
Minor version upgrades are frequent and backward-compatible, while major upgrades introduce non-backward-compatible changes.
Amazon RDS supports various upgrade options, including blue/green deployment for minimal downtime.
Rolling upgrades with pgactive allow a gradual transition to new database versions, improving high availability.
pgactive enables active-active replication for Amazon RDS PostgreSQL, with built-in conflict detection and resolution.
Key benefits of using pgactive include multi-tenant support, minimal downtime, and seamless rollback capabilities.
Steps involved in setting up pgactive for rolling upgrades include configuring parameters, setting up endpoints, and initializing replication.
Limitations of pgactive include handling sequences, materialized views, DDL replication, tables without primary keys, and existing logical replication.
Monitoring replication lag is crucial for pgactive to diagnose issues and prevent conflicting changes in the system.

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Dev

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Supabase Alternatives 🔄 in 2025 😼

Neon, Hasura, AppSheet, Firebase, and Parse are some alternatives to Supabase in 2025.
Neon offers serverless PostgreSQL with database branching, focusing solely on PostgreSQL experience.
Hasura, a BaaS competitor, is known for its GraphQL-first approach and compatibility with various databases.
AppSheet, acquired by Google, allows no-code app development using Google Sheets as a database.
Firebase, with its NoSQL document database and comprehensive toolkit, remains a solid choice.
Parse, while historically significant, is not recommended for new projects due to modern alternatives.
Supabase, known for its PostgreSQL backend, remains a top choice due to community adoption and native integrations.
Neon is great for serverless PostgreSQL, Hasura for GraphQL enthusiasts, AppSheet for no-code solutions, and Firebase for Google ecosystem users.
Platforms embracing open standards like Supabase, Neon, and Hasura will thrive in the era of AI-assisted coding and agentic development.

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