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Why Having a Cloud-Native Data Platform Matters for Telcos

Cloud-native technology involves developing software in the cloud using containers for applications to run in any environment as microservices, managed via DevOps workflows.
Telcos are transitioning to cloud-native to compete effectively in the 5G era by prioritizing speed and agility for innovation at faster speeds and volumes.
Security benefits of containers for telcos include modularity, consistency, smaller attack surfaces, easier updates, and better continuity in case of failures.
Cloud-native enables telcos to innovate at 5G speeds, quickly create and deploy apps, and react to market changes to maximize profits and unlock new revenue streams.
Embracing cloud-native development future-proofs telco infrastructure for scalability and cost-effectiveness while attracting top developers with innovative technologies.
Telcos need to deploy apps at the network edge for round-trip data transmissions at 5G speeds, making centralized data centers obsolete in favor of edge computing.
Cloud-native data platforms like Volt Active Data offer speed, elasticity, flexibility, and real-time data-driven decisions with containerization, Kubernetes support, and rapid failover.
Volt Active Data joins the Cloud Native Computing Foundation (CNCF) ecosystem, providing telcos with powerful capabilities for cloud-native deployments.
Cloud-native data platforms like Volt Active Data empower telcos to handle data efficiently, with features like rapid failover, automated recovery, and elastic scalability.
By using cloud-native technology and data platforms like Volt Active Data, telcos can position themselves for success in the evolving telecom landscape by adapting to market demands and maximizing their capabilities.

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Dbi-Services

260

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Technological debt with AI workflows: When data hits he fan?

AI projects come with risks such as data explosion, hallucinations, security gaps, and non-linear costs, often due to neglecting data-quality, governance, and code debt.
The trend of re-hosting old architectures in AI projects echoes mistakes made in cloud projects, leading to scalability challenges due to outdated data plumbing.
Common pitfalls in AI workflows include siloed data, stagnant customer-support responses, monolithic databases hindering scalability, and lack of observability.
To mitigate risks, organizations can implement advanced RAG search techniques, address legal liabilities related to chatbot misinformation, and enhance security measures.
Recommendations include modernizing data processes, refactoring storage layers, implementing proper RAG search techniques, securing vector paths, and planning to avoid technological debt.
Key questions to ask include identifying hidden debts, determining the need for additional vector stores, managing data updates, and addressing security vulnerabilities.
It is crucial to plan for scalability, encryption, and data governance while leveraging AI technologies like pgvector and pgvectorscale to avoid pitfalls and ensure success.
Consulting with experts and continuously assessing architecture relevance to use cases are essential steps in navigating the complexities of AI workflows and minimizing technological debt.
Balancing the excitement of AI implementation with caution and strategic planning is necessary to harness its benefits effectively and prevent operational disruptions.
Ultimately, prioritizing data governance, security, and scalability can help organizations steer clear of potential pitfalls and maximize the value derived from AI initiatives.
Ensuring proactive measures for maintaining data integrity, optimizing workflows, and adapting to evolving technological landscapes is critical for long-term success in AI implementation.

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Dev

436

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Oracle Cloud HCM 25B Release: What's New?

Oracle Cloud HCM 25B release is set for April 2025, promising critical Redwood enhancements and new user experience features.
Opkey simplifies Oracle updates with its AI-powered platform, reducing costs, effort, and risks.
Key features of the 25B update include improvements in Global HR, Benefits, Compensation, Payroll, Recruiting, Talent Management, Time and Labor, and Absence Management.
In Global HR, shifts and break durations can now be defined in any minute increment, and additional notes can be added during employment changes.
Benefits enhancements offer streamlined processes and the ability to hide post-enrollment regions on self-service pages.
Compensation updates allow saving of Redwood pages and exclusion of specific compensation plans using business rules.
Payroll improvements include enhanced logging and customization options using Oracle Visual Builder Studio.
Recruiting Cloud now allows limited access to the Career Sites Configuration page and use of candidate citizenship info in formulas.
Talent Management features include AI-suggested roles and skill readiness details for role guides.
Time and Labor enhancements offer customization options for Redwood timecards and improved shift information visibility.

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Dev

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# PostgreSQL Tutorial: 📚 What Really Happens When You Add and Drop Columns 2000 Times in PostgreSQL

PostgreSQL has an internal 1600 column limit that affects the addition and dropping of columns.
Adding a column without a default value is instantaneous and only updates system catalogs.
Dropping a column marks it as dropped in catalogs, but the physical data still exists in table tuples.
When the number of dropped columns reaches the limit, new schema changes are blocked until a full table rewrite is performed.

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Insider

384

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These are the hardest companies to interview for, according to Glassdoor

Tech giants like Google, Meta, and Nvidia have the toughest job interviews with multiple rounds and assessments.
Google is notorious for its demanding interview process involving assessments, phone calls, projects, and multiple rounds of interviews.
Other industries also have tough interviews, including luxury carmakers like Rolls-Royce and companies like Bacardi with unconventional interview questions.
According to Glassdoor data, tech giants, luxury carmakers, and various reputable companies have challenging job interviews.

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Dev

126

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Introduction to SQL Using PostreSQL

SQL (Structured Query Language) is a crucial programming language for managing information in relational database management systems.
PostgreSQL is a popular open-source RDBMS used for storing, manipulating, and retrieving data efficiently.
Setting up PostgreSQL involves downloading the latest version, installing it based on the operating system, and configuring it for use.
Basic database concepts like schemas, tables, rows, and columns form the foundation for understanding SQL queries and data storage.
Creating databases, schemas, tables, inserting data, and querying data are essential SQL operations covered in the article.
Hands-on projects like creating a Customer Order Management System help apply SQL concepts in practical scenarios.
Pro tips on SQL data types and constraints explain the types of data each column can store and constraints for data management.
Next steps include mastering SQL JOINs, understanding SQL indexes, and working on SQL project ideas to enhance database management skills.

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Dev

293

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How to Install and Use SQLmap on Android Termux

SQLmap is a free tool used to find and exploit SQL injection vulnerabilities in websites, making it popular among security experts and ethical hackers.
This guide explains how to install SQLmap on an Android device using Termux, a terminal emulator, for educational purposes.
Before installing SQLmap, ensure you have tools like Termux and Hacker's Keyboard on your Android device.
Basic technical requirements include familiarity with command-line operations and a stable internet connection for package downloads.
Installation steps involve updating Termux packages, installing Python2 and Git, cloning the SQLmap repository, and navigating to the SQLmap directory.
To run SQLmap, use the command 'python2 sqlmap.py' and follow instructions to test for SQL injection vulnerabilities on websites you have permission to test.
It's crucial to only test websites with authorization, as unauthorized testing can have severe legal consequences.
Successfully installing SQLmap allows users to enhance web application security by identifying and addressing SQL vulnerabilities.
This guide aims to equip users with the skills needed to use SQLmap effectively and protect online information from SQL threats.
Start practicing ethical hacking responsibly by following the steps outlined in this installation guide for SQLmap on Android Termux.

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Cloudblog

311

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Migrating your apps from MySQL to Spanner just got easier

Google Cloud announced new functionality and migration tooling to simplify migrating database workloads from MySQL to Spanner.
Spanner provides a migration path to move production workloads from MySQL to Spanner with minimal downtime.
Improved data movement templates and reverse replication support live cutovers and near real-time failover.
Spanner offers improved latency and relational capabilities similar to MySQL, reducing code and query changes.

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Dbi-Services

370

odacli create-appliance failed on an ODA HA

The installation of an Oracle Database Appliance X11 HA failed when creating the appliance using 'odacli create-appliance' command, encountering 'host key verification failed' error.
Attempts to restart the create-appliance command were unsuccessful due to the error 'DCS-10047:Same Job is already running: Provisioning FAILED in different request.'
Following MOS Note recommendations, the ODA was cleaned up, and the repository was updated with Grid Infrastructure and DB clones.
After stopping the dcs agent and running cleanup on both nodes, repository updates for GI and DB were performed.
Clones availability was checked on both nodes to ensure successful completion of the update tasks.
Additionally, storage topology validation was carried out on both nodes to prevent errors during the create-appliance process.
Not validating the storage topology could lead to errors like 'OAK-10011:Failure while running storage setup' when creating the appliance.
To resolve the issue, following the cleanup, updating the repository, and validating storage topology, the 'odacli create-appliance' should be executed successfully.
In case of 'odacli create-appliance' failure on ODA HA, the recommended steps help in resolving the issue effectively.
Running a cleanup, updating the repository with clones, and validating storage topology are essential tasks to ensure the successful creation of the appliance.

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Soais

221

UiPath Agentic Process Modeling Using BPMN

UiPath’s Agentic process integrates advanced AI technologies to build autonomous AI agents capable of analyzing data, setting goals, and executing actions.
UiPath offers a specialized designer BPMN canvas within its UiPath Maestro service, allowing users to design and configure long-running enterprise processes.
The BPMN canvas in UiPath enables the design of complex workflows, fostering collaboration among stakeholders and managing unpredictable agent behaviors through predefined rules.
UiPath's Autopilot for BPMN Canvas assists users by analyzing process models, identifying automation opportunities, and offering optimization recommendations based on BPMN and UiPath technologies.

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Hackaday

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Abusing DuckDB-WASM To Create Doom In SQL

A Doom-lite clone has been created using SQL instead of JavaScript to handle the game loop.
The entire game world state is modeled in a database, with the player's view rendered using SQL VIEW feature for raytracing.
Events such as movement, bullets hitting a wall, and enemy impacts are defined as SQL statements.
JavaScript is used to glue the SQL chunks together, handle sprite Z-buffer checks, and process keyboard input.

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Dev

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Text-to-SQL from Scratch — Tutorial For Dummies (Using PocketFlow!)

Text-to-SQL systems help translate natural language questions into SQL queries, simplifying database interactions.
Understanding the database schema is crucial for Text-to-SQL systems to interpret queries accurately.
Large Language Models (LLMs) play a key role in transforming questions into SQL queries based on schema information.
The process involves generating SQL queries, executing them against the database, and handling errors through debugging loops.
PocketFlow simplifies the creation of Text-to-SQL systems by breaking down the workflow into manageable Nodes and a Flow manager.
Specific nodes like GetSchema, GenerateSQL, ExecuteSQL, and DebugSQL are used to map the database, translate queries, run SQL, and fix errors.
Nodes in PocketFlow have prep, exec, and post methods for handling inputs, executing tasks, and storing outputs.
The interconnected flow ensures a seamless process from understanding the schema to presenting query results.
Text-to-SQL systems empower users to interact with databases using plain English, bridging the gap between users and complex SQL queries.
PocketFlow's simplicity allows developers to build conversational database interfaces efficiently and intelligently.

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Dbi-Services

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Virtualize, Anonymize, Validate: The Power of Delphix & OMrun

Delphix and OMrun offer a solution for virtualizing, anonymizing, and validating test data in a secure and fast manner.
Delphix replaces physical test environments with virtualized data environments, providing faster, efficient, and agile testing capabilities.
Delphix includes built-in data masking to ensure compliance and reduce the risk of data leaks in non-production environments.
OMrun brings powerful data validation and quality assurance capabilities, providing automated script generation, scalable validation, and transparent reporting.

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Javacodegeeks

298

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How to Reuse PreparedStatement in Java

PreparedStatement in Java helps in executing parameterized SQL queries, enhancing security and performance by preventing SQL injection and reducing query parsing overhead.
Reusing a PreparedStatement without closing it between executions is beneficial as the database can avoid recompilation, leading to improved performance, security, reduced load, and cleaner code.
Creating a new PreparedStatement within a loop, as shown with the BOOKS table insertion example, is inefficient as it forces SQL parsing and compilation in each iteration.
The correct approach involves preparing the PreparedStatement once and reusing it across multiple executions, optimizing performance and maintaining cleaner code.
Batch processing with PreparedStatement is optimal for handling large data volumes, reducing network traffic, leveraging database batch capabilities, and enhancing overall efficiency.
By reusing PreparedStatement objects in Java JDBC API, developers can significantly improve performance, code quality, and overall database operation efficiency.
Choosing the right reuse strategy based on the use case, such as batch processing for bulk inserts, can lead to enhanced throughput and streamlined operations.
Efficient use of PreparedStatement in Java SQL contributes to optimized database operations and cleaner code writing.
Developers can download the source code examples provided in the article to implement efficient PreparedStatement reuse in Java applications.

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Hackernoon

357

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Want Everyone Out of the Database? Here’s How the Pros Do It

Transitioning databases between single-user and multi-user modes is crucial for maintenance and operational tasks.
SQL Server, PostgreSQL, and MySQL have different methods for managing user access modes.
In SQL Server, you can switch between modes using system commands or through the graphical interface.
PostgreSQL doesn't have a direct single-user mode switch but allows limiting concurrent connections through parameters.
PostgreSQL DBAs can control connection limits per database to manage resource stability.
Terminate active sessions in PostgreSQL using pg_terminate_backend before setting a connection limit.
MySQL doesn't have a dedicated single-user mode and requires terminating sessions and adjusting global connection limits.
Best practices include verifying database mode, controlled session termination, and reverting limits post-administration.
Regular monitoring and documentation are essential for tracking changes and ensuring system integrity.
Managing transitions between user access modes is critical for maintaining system stability and data consistency.

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