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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

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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

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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

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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

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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

379

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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

447

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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

251

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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

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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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Amazon

100

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Connect Amazon Bedrock Agents with Amazon Aurora PostgreSQL using Amazon RDS Data API

Generative AI applications are being integrated with relational databases like Amazon Aurora PostgreSQL-Compatible Edition using RDS Data API, Amazon Bedrock, and Amazon Bedrock Agents.
The solution leverages large language models to interpret natural language queries and generate SQL statements, utilizing Data API for database interactions.
The architecture involves invoking Bedrock agents, using foundational models, generating SQL queries with Lambda functions, and executing them on Aurora PostgreSQL via Data API.
Security measures include agent-level instructions for read-only operations, action group validation, read-only database access, and Bedrock guardrails to prevent unauthorized queries.
Prerequisites for implementing this solution include an AWS account, IAM permissions, an IDE, Python with Boto3, and AWS CDK installed.
Deploying the solution involves creating an Aurora PostgreSQL cluster with AWS CDK and deploying the Bedrock agent for natural language query interactions.
The solution provides functions for generating SQL queries from natural language prompts and executing them using Data API, ensuring dynamic data retrieval.
Considerations include limiting this integration to read-only workloads, validating parameters to prevent SQL injection, implementing caching strategies, and ensuring logging and auditing for compliance.
To test the solution, scripts can be run to generate test prompts, execute SQL queries, and review responses from the database.
Cleanup steps involve deleting resources created through CDK to avoid incurring charges for unused resources.
The integration of AI-driven interfaces with databases showcases the potential for streamlining database interactions and making data more accessible to users.

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Amazon

191

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Run SQL Server post-migration activities using Cloud Migration Factory on AWS

Migrating SQL Server databases to Amazon EC2 through AWS Application Migration Service is a crucial step towards infrastructure modernization.
Post-migration activities are essential for optimizing, securing, and ensuring functionality in the new SQL Server environment.
Cloud Migration Factory on AWS (CMF) automates tasks such as validating database status and performance settings post-migration.
CMF Implementation Guide streamlines the migration process with automation, enhancing efficiency and scalability.
CMF automation process involves pre-migration preparation, migration, and post-migration activities phases.
CMF automation jobs enable efficient execution of post-migration tasks for SQL Server databases on EC2 instances.
Post-migration activities include linked server tests, memory configuration, database status checks, consistency checks, and connectivity tests.
Using CMF automation offers benefits like efficiency, consistency, scalability, visibility, security, and customization for post-migration tasks.
Best practices for automating post-migration activities include using Windows Authentication, providing summary messages, and incorporating appropriate return codes.
Cleaning up resources post-migration is important to avoid ongoing charges.

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Dev

233

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An Overview to OCI IAM Policies

Oracle Cloud's IAM policies provide granular control over access to resources by using groups of users and resources in a complex online environment.
The syntax of OCI IAM policies includes patterns like allowing a group to in .
User accounts are handled using a straightforward syntax, while resource principals use dynamic-groups for more specific control.
Group IDs can be used instead of names in policies to specify user groups with more accuracy.
Policies can also be applied to OCI services using specific permissions like managing networking resources.
Policies can be declared at different levels such as tenancy, compartments, and even individual resources.
Conditions can be added to policies to restrict when they apply, using criteria like permissions, tags, regions, and more.
Policies can be made more granular by specifying verbs for general function permissions and API-specific permissions.
Resource-specific policies can restrict access to specific types of resources like objects in Object Storage or secrets in Vault & Key Management.
Overall, OCI IAM policies offer a robust framework for managing access control in Oracle Cloud environments.

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