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UiPath Agent Builder: A Simple Overview

UiPath Agent Builder is a tool for creating AI agents—virtual assistants that can understand natural language and interact with UiPath workflows.
Agent Builder introduces flexible, decision-making logic using AI while being grounded in data and controlled by guardrails.
It is native to UiPath, low code, and offers governance features like escalation and audit logs for compliance and predictability.
UiPath is evolving the platform with upcoming support for more AI models, additional templates, and better Langchain support.

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Dev

188

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Introduction to PostgreSQL

PostgreSQL is an advanced open-source RDBMS supporting SQL and JSON querying, with origins dating back to 1986 at UC Berkeley.
Key features include ACID compliance, MVCC, and extensibility, making it suitable for various workloads.
Installation requirements for PostgreSQL include modest hardware specifications and platform-specific installation methods.
Configuration involves editing files like postgresql.conf and using the psql command-line tool for database management and administration.
PostgreSQL's architecture follows a client-server model, with key processes like WAL writer and background writer for data management.
Core concepts cover databases, schemas, tables, data types, constraints, indexes, and views within PostgreSQL.
SQL operations like CRUD, joins, subqueries, aggregations, and transactions are fundamental to working with PostgreSQL.
Advanced features include window functions, CTEs, JSONB support, full-text search, and query optimization techniques.
Security measures like roles, permissions, SSL encryption, and row-level security enhance data protection in PostgreSQL.
Deployment best practices, scaling strategies, maintenance procedures, and the PostgreSQL ecosystem are crucial for production use.

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Dbi-Services

271

Optimize materialization of Exadata PDB sparse clones

A company using Exadata sparse clones for database provisioning faced slow materialization times for sparse clones compared to full clones.
The issue was identified as a result of Oracle sequentially performing online database move operations, slowing down the process.
Monitoring the materialization process was possible through queries on GV$SESSION_LONGOPS.
The company optimized the process using Python scripts with cx_Oracle driver, enabling parallel task execution and reducing the materialization time to that of creating a full PDB clone.

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Javacodegeeks

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Introduction to Apache Kylin

Apache Kylin is an open-source distributed analytics engine that offers fast OLAP queries on large-scale datasets stored in systems like Apache Hive or HDFS.
Key features of Apache Kylin include pre-computed cubes, standard SQL support, massive scalability, multi-engine compatibility, security integration, and RESTful APIs.
Apache Kylin precomputes OLAP cubes offline, stores them for fast retrieval, and integrates with BI tools like Tableau and Power BI.
Its architecture includes components like Web UI, Metadata Store, Query Engine, Cube Engine, Storage Layer, and REST Server.
Installation using Docker simplifies local setup, enabling users to quickly create projects, data models, and OLAP cubes in the Kylin Web UI.
Querying Apache Kylin can be done through its Web UI, BI tools via drivers, or REST API for data automation, facilitating fast analytical insights.
Apache Kylin is suitable for enterprise BI dashboards, marketing analytics, sales reporting, customer behavior analysis, and telecom data analysis.
By precomputing cubes, Kylin boosts query speed, making it ideal for interactive dashboards and real-time analytics on big data platforms.
With its fast analytics capabilities, Apache Kylin serves as a valuable tool for businesses looking to bridge the gap between massive datasets and BI needs.
Overall, Apache Kylin offers a compelling option for enhancing analytical workloads and enabling real-time business intelligence on big data architectures.

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Javacodegeeks

259

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Fix Cannot Load Driver Class: com.mysql.jdbc.driver in Spring Boot

The 'Cannot Load Driver Class: com.mysql.jdbc.driver' issue in Spring Boot arises when the application can't load the old MySQL driver class due to a mismatch in driver configuration.
In older Spring Boot 1.x versions, the driver class name is explicitly set in the configuration, while in Spring Boot 2+, it can be auto-detected based on the JDBC URL.
If the Spring Boot application is upgraded to a higher version with a mismatched driver class configuration, it can lead to the 'Cannot Load Driver Class' exception.
To resolve this, ensure compatibility between MySQL Connector/J version and the driver class configuration in Spring Boot application properties.

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Dbi-Services

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SQL Server 2025 – AG Commit Time

SQL Server 2025 preview has been publicly available for a week now.
One highlighted Engine High Availability (HA) feature in the blog is Availability Group Commit Time.
SQL Server 2025 introduces configurable AG Commit Time for better performance in specific scenarios.
Instructions on how to change the default AG commit time value and measure its impact are provided.

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Silicon

251

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Oracle ‘To Spend $40bn’ On Nvidia Chips For Stargate Campus

Oracle plans to spend $40 billion on Nvidia GB200 AI accelerators for a data centre in Texas for the Stargate infrastructure project with OpenAI and SoftBank.
About 400,000 high-end chips will be purchased by Oracle for training AI systems, with the capacity leased to OpenAI.
The Abilene, Texas site is the first Stargate project and is expected to provide 1.2 gigawatts of computing power upon completion next year.
Stargate aims to reduce OpenAI's dependence on Microsoft for computing infrastructure, with investments from various companies totaling billions.

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Medium

280

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The engineering Oracle prompt: Tap into 30 years of dev wisdom in one chat

The Engineering Oracle is a prompt designed to simulate a seasoned software engineer with 30 years of experience.
It emphasizes deep consultation over quick fixes, helping with complex architectural problems, debugging, and decision-making.
The Oracle focuses on context, logical thinking models, and customized solutions rather than hand-wavy responses.
It begins by asking insightful questions to understand the problem before offering solutions, mimicking how veteran engineers approach problems.
The Oracle not only looks at code issues but also considers business goals, user needs, and team dynamics.
It provides tailored solutions based on the specific problem, team dynamics, and technology stack, promoting clarity over mere code snippets.
This prompt is valuable for tackling complex problems, decision-making scenarios, and team-related dilemmas, offering practical advice grounded in experience.
The Oracle aims to guide through consequences, not just steps, avoiding the trial-and-error approach that may break systems.
It's not a replacement for developers but rather an AI that thinks like an experienced developer, offering strategic insights and considerations.
The Oracle excels in guiding through real-world engineering challenges beyond mere technicalities, considering factors like risks, team dynamics, and long-term implications.

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Dev

2.9k

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Converting JSON Data to Tabular in Snowflake — From SQL to SPL #32

The task involves extracting information from a multi-layered JSON string in a Snowflake database.
SpecificTrap field is identified as the grouping field, and details like oid and value are extracted from the first layer array variables.
In SQL, due to its limitation with multi-layer data, indirect implementations through nested queries and grouping aggregation are required.
On the other hand, SPL (Structured Programming Language) directly supports multi-layer data access and allows object-oriented access to such structures.

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Dev

349

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My Journey with ASP.NET Core & SQL Server: Lessons Learned

Yasser Alsousi, a .NET developer, shared lessons learned from his journey with ASP.NET Core and SQL Server.
Key reasons for choosing ASP.NET Core: cross-platform capabilities, high performance, strong ecosystem, and enterprise-ready features.
Top 3 beginner tips include mastering C# fundamentals, embracing dependency injection, and following database best practices like starting with SQL Server Express and optimizing with Entity Framework Core.
Yasser optimized an inventory API from 2s to 200ms response times by adding SQL indexes, implementing caching, and using AsNoTracking() for read-only operations.

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Dev

412

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Fastest way to import excel into mysql

Introduction to the fastest way to import Excel data into MySQL.
Preparation involves creating an Excel table for import.
Steps include creating a new connection, initiating the import process, and optimizing for faster import speed.
DiLu Converter is highlighted as a powerful tool supporting multiple databases for simplified Excel import and export.

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Siliconangle

232

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AI budgets are hot, IT budgets are not

Many enterprises are still unsure about the benefits of AI investments versus historical IT initiatives like ERP, data warehousing, and cloud computing.
ETR's data shows a shift towards building in-house AI applications, with 83% of IT decision makers planning to increase spend on AI app/dev in 2025.
There is a consensus across different buyer types on expanding budgets for custom AI workloads to accelerate time-to-value.
Enterprises are still in proof-of-concept or early production stages indicating a multi-year investment wave in AI application development.
Geopolitical tension and shifting policy frameworks are not derailing enterprise AI agendas, with more firms proceeding cautiously than slowing down adoption.
ROI for AI projects lags with 27% of respondents yet to see tangible returns, indicating enterprises are still in experimentation mode rather than harvesting immediate benefits.
C-suite executives rank AI initiatives as the second-most vulnerable category to cuts, next to outsourced IT services, highlighting the potential vulnerability of experimental AI funding.
AI budget growth expectations have retreated, indicating a cautious sentiment towards IT spending due to economic uncertainties and geopolitical unrest.
Policy uncertainty is causing executives to tap the brakes on net-new IT projects, with 71% acknowledging some form of pullback due to uncertainty.
Enterprise adoption of specific AI foundation models shows OpenAI's GPT leading in mindshare, with Microsoft's Azure OpenAI Service being widely adopted.

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Dev

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🚦 Oracle 19c vs PostgreSQL 15 — The Ultimate Parameter Showdown!

The article compares Oracle 19c and PostgreSQL 15 across various parameters in a side-by-side checklist.
Over 200+ parameters in categories like Memory, CPU, I/O, Connections, Optimizer, Logging, Security, Background Jobs, Redo/WAL, and Miscellaneous are compared.
Parameters like memory allocation, CPU configuration, I/O operations, connection limits, optimizer settings, logging setups, security features, background job controls, and more are included in the comparison.
It highlights key differences and equivalences between Oracle and PostgreSQL parameters, aiding in tasks like workload migration, database tuning, cloud strategy planning, and parameter mapping.

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Pymnts

113

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Oracle to Buy $40 Billion Worth of Nvidia Chips for First Stargate Data Center

Oracle plans to buy $40 billion worth of Nvidia chips to power the first Stargate project, a data center in Abilene, Texas.
The data center is expected to be operational by mid-2026 and will provide 1.2 gigawatts of power, making it one of the largest in the world.
Stargate project, announced by President Donald Trump, aims to build AI-focused data centers in the U.S. with the first 10 in Texas.
AI data centers like Stargate's require specialized hardware and infrastructure to handle the computational power needed for AI workloads.

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Amazon

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Explore the new openCypher custom functions and subquery support in Amazon Neptune

Amazon Neptune has released openCypher features as part of the 1.4.2.0 engine update, offering support for custom functions and CALL subqueries.
Neptune is a managed graph database service providing open graph query languages like openCypher, Apache TinkerPop Gremlin, and SPARQL 1.1.
The latest engine release introduced features including CALL subqueries for running specific queries on a node-by-node basis.
The CALL function enables executing additional MATCH statements against a collection of data in openCypher queries.
Neptune's openCypher custom functions include textIndexOf, collToSet, collSubtract, collIntersection, collSort, collSortMaps, collSortMulti, and collSortNodes.
These functions support tasks like searching text, creating unique sets, and sorting collections of data in various ways.
Examples provided showcase how these custom functions can be utilized for advanced querying and data manipulation in Neptune.
Users can now leverage these new features to enhance their graph applications and perform complex operations efficiently.
Neptune also offers options for bulk loading data into Neptune Database or Neptune Analytics, providing practical data management solutions.
The post concludes with suggestions on getting started with Neptune, such as creating clusters, upgrading to the latest version, and using open source tools like graph-explorer.

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