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Amazon

13h

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Migrate a self-managed MySQL database to Amazon Aurora MySQL using AWS DMS homogeneous data migrations

Migrating a self-managed MySQL database to Amazon Aurora MySQL offers improved performance, scalability, and manageability, facilitated by AWS DMS homogeneous data migrations.
Homogeneous data migrations in AWS DMS simplify transitioning from self-managed, on-premises databases to Amazon RDS equivalents, supporting migrations between the same database engines.
The process involves setting up and executing an AWS DMS homogeneous migration for an encrypted MySQL database to Aurora MySQL, encompassing configuration and best practices.
Data migrations with full load and change data capture (CDC) type utilize mydumper and myloader for efficient data transfer and replication.
Prior to migration, prerequisites like an active AWS account, network connectivity, IAM policies, and an Aurora MySQL cluster in the target account are crucial.
Steps include preparing source and target environments, creating AWS DMS subnet groups, importing certificates for encryption, setting up Secrets Manager for database credentials, and creating instance profiles and data providers.
After creating a migration project, data migration is initiated, progress is monitored through CloudWatch metrics, and cutover is performed once replication readiness is ensured.
The clean-up phase involves deleting resources post-migration to prevent unnecessary costs.
Overall, the migration process leverages AWS DMS for seamless transitioning to Aurora MySQL-Compatible, emphasizing thorough testing in non-production environments beforehand.
The comprehensive guide provides insights into the entire migration process, from setup to completion, recommending careful planning and adherence to best practices for successful migrations.

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Dbi-Services

15h

237

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SQL Server: New connectivity and Drivers landing page

Microsoft has launched a new connectivity and drivers landing page for SQL Server supporting .Net, Java, Python, C++, Go, and PHP languages.
The page provides the ability to download drivers, access quick start guides, and offers code samples for different programming languages such as Python.
Upon clicking on 'Download' for Python, users are directed to the 'Python SQL Driver' page.
Further clicks on 'Get Started' and 'Code Sample' provide guidance on connecting with the mssql-python driver and deploying a Python web app to Azure App Service.
The process is described as straightforward, making it easy to establish a connection to SQL Server from various development tools.
The author expresses hope for the addition of support for languages like Ruby and Spark in the future.

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Dbi-Services

21h

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SQL Server 2025: Local SQL Server Container without Docker Command

The latest version of the MSSQL extension for Visual Studio Code offers a Preview feature called 'Local SQL Server Containers' based on SQL Server 2025.
Users can now create and manage SQL Server containers locally without using Docker commands.
The extension allows for the default use of SQL Server 2025 with vector and AI-ready features.
Users can auto-connect with a ready-to-use connection profile and manage containers from the connection panel.
There is automatic port conflict detection and resolution, along with options to customize container details like name, hostname, port, and version.
The process involves downloading and installing Visual Studio Code and the MSSQL extension, followed by creating the local SQL Server container.
If Docker is not installed, users will need to install it to proceed.
After installation, users can choose the SQL Server image up to version 2017 or select the SQL Server 2025 version.
A password and profile name need to be set, followed by accepting terms and conditions before creating the container.
The creation process involves three steps: Creating Container, Setting up container, and Connecting to Container.
Once online, users can test the connection by executing queries like SELECT @@Version and SELECT @@Servername.
This new feature simplifies the process for developers, allowing them to use SQL Server 2025 on Linux Ubuntu without needing Docker commands.
Overall, the installation and initial steps are deemed straightforward and favorable among developers without Docker expertise.
The feature is expected to be popular among developers wanting to adopt it quickly.

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Dev

293

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StrataScratch's Advanced 25 Hard SQL Questions

A user has completed StrataScratch's Advanced SQL 25 Questions, highlighting it as a challenging but rewarding experience that enhanced analytical and problem-solving skills.
The user maintained progress in a Notion database, documenting the questions, their sources, and solutions, offering to share a template for others to duplicate.
Acknowledgment was given to Frederik Müller for inspiration, leading the user to undertake this journey, and gratitude expressed to StrataScratch for providing the invaluable resource.
Key learnings from the challenge encompass advanced window functions, recursion, complex joins, and query optimization, facilitating growth and learning in the data field.

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Dev

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Handling Concurrency with Row Level Locking in PostgreSQL

Concurrency is a critical concern in scalable systems, especially when handling shared data.
One strategy to address concurrency is using PostgreSQL's row-level locking feature with SELECT ... FOR UPDATE.
Concurrency issues can lead to data loss and inconsistencies if not handled properly.
Row-level locking ensures data integrity by preventing concurrent access to the same data.
While row-level locking resolves concurrency problems, it may increase request latency due to serialization.
Row-level locking is suitable for applications where data integrity is crucial, like financial systems.
Dealing with deadlocks is essential when implementing row-level locking to avoid circular waiting scenarios.
PostgreSQL has mechanisms to handle deadlocks by aborting one of the transactions.
Understanding business requirements is important to determine if row-level locking is the right approach for an application.
Applying concurrency handling techniques early can save time and ensure system integrity as it scales.

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Dev

220

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Declarative Programming: SQL, HTML, CSS, Prolog Guide

Declarative programming focuses on 'what to do,' abstracting away implementation details for code that is readable, concise, and maintainable.
SQL exemplifies declarative programming, allowing users to specify data retrieval intentions without procedural instructions.
HTML and CSS demonstrate declarative approaches in defining web page structure and styling for separation of content and presentation concerns.
Prolog showcases declarative reasoning by declaring facts and rules for symbolic reasoning and logic-based queries.
Declarative programming enhances readability, maintainability, and reduces side effects, offering higher abstraction and parallelization opportunities.
Resources like guides, best practices, and comprehensive language overviews support mastering declarative programming principles.
Understanding and embracing declarative programming elevates software engineering skills by emphasizing clarity, intent, and efficient development.
Declarative programming enables cleaner code, improved collaboration, and resilient applications in modern software development.
Key concepts discussed include the power of SQL for data manipulation, HTML/CSS for web design, and Prolog for logic-based programming.
Practical benefits of declarative programming include readability, maintainability, and a focus on solving problems effectively.

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Medium

288

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This SQL Query Looked Fine — Until It Silently Broke Everything

Periodic CPU spikes were observed on a SQL Server instance causing stored procedures to time out.
Despite no suspicious changes or deployments, the issue persisted leading to further investigation.
Upon examining the execution plan, it was discovered that failing stored procedures accessed the same table (referred to as Transactions).
An Index Scan on the Transactions table was highlighted as a major factor in the performance problem.

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Amazon

11h

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Use Graph Machine Learning to detect fraud with Amazon Neptune Analytics and GraphStorm

Businesses and consumers face significant losses to fraud, with reports of $12.5 billion lost to fraud in 2024, showing a 25% increase year over year.
Fraud networks operate coordinated schemes that are challenging for companies to detect and stop.
Amazon Neptune Analytics and GraphStorm are utilized to develop a fraud analysis pipeline with AWS services.
Graph machine learning offers advantages in capturing complex relationships crucial for fraud detection.
GraphStorm enables the use of Graph Neural Networks (GNNs) for learning from large-scale graphs.
Steps involve exporting data from Neptune Analytics, training graph ML models on SageMaker AI, and enriching graph data back into Neptune Analytics.
Prerequisites include an AWS account, S3 bucket, required IAM roles, SageMaker execution role, and Amazon SageMaker Studio domain.
The article provides detailed steps for setting up environment, creating a Neptune Analytics graph, training models with GraphStorm, and conducting fraud analysis.
The workflow includes data preparation, training GraphStorm models, deploying SageMaker pipelines, enriching graphs, and analyzing high-risk transactions.
Advanced analytics include detecting community structures, ranking communities by risk scores, and using node embeddings to find similar high-risk transactions.
The post encourages further integrations with Neptune Database for online transactional graph queries and highlights workflow extensions.

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Dev

12h

133

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A Beginner DBA's Guide to SSRS Reports

SSRS (SQL Server Reporting Services) is Microsoft's tool for converting SQL query results into professional, interactive reports.
SSRS helps create clean, formatted tables, visual charts, automated email reports, and interactive dashboards.
Building your first SSRS report involves starting with a clear SQL query that answers a specific business question.
Using Report Builder (instead of Visual Studio) is recommended for beginners, as it is user-friendly and free.
Choosing the right layout type (table, matrix, or chart) in SSRS is crucial for visually conveying the data story.
Designing the report involves arranging data columns, grouping related data, adding totals, calculations, and professional formatting.
Testing the report thoroughly to ensure data accuracy, visual consistency, and functionality, especially with different scenarios.
Sharing the report via the SSRS Web Portal allows team access through web browsers and setting up automated email subscriptions adds professionalism.
Mastering SSRS as a DBA can transition you from back-end roles to a more visible position by translating data into actionable insights.
Getting started with SSRS involves picking a SQL query, identifying useful data for colleagues, creating a simple SSRS report, and setting up regular email delivery.
A challenge is presented to create a parameterized SSRS report from a favorite SQL query and schedule it for regular automated updates.

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Dev

17h

162

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💻 OCI Journey – Part 3: Compute Services in Oracle Cloud Infrastructure

Oracle Cloud Infrastructure (OCI) Compute Services provide a range of virtual machines, bare metal servers, and dedicated hosts.
Key concepts in OCI Compute include Virtual Machines, Bare Metal Instances, and Dedicated Hosts.
Factors to consider in OCI Compute are scalability, performance requirements, cost-efficiency, and image & shape selection.
OCI offers flexible shapes allowing dynamic definition of compute resources like OCPUs and Memory.
OCI is the only cloud provider offering AMD-based, Intel-based, and Ampere ARM-based CPUs.
OCI's pricing model is pay-as-you-go and claims to be 50% cheaper than other providers; features Preemptible VMs for cost savings.
OCI supports live migration of VMs across hosts during hardware maintenance without downtime.
To launch an instance in OCI, create a VCN and subnet, launch a VM, assign it to a subnet, and optionally assign a Public IP.
Scaling in OCI can be done vertically by increasing OCPUs or memory, involving some downtime, or horizontally by adding/removing instances for better resilience.
OCI offers Oracle Kubernetes Engine (OKE) for managed Kubernetes service with options for different cluster and node types.
OCI Container Instances allow running containers serverlessly, ideal for short-running apps and microservices.
Serverless Compute with Oracle Functions is based on a Function-as-a-Service model integrated with OCI events and services.
OCI Compute provides flexibility and cost-efficiency for various workloads, offering VMs, Bare Metal, or Dedicated Hosts, flexible shapes, Kubernetes, Container Instances, and Oracle Functions.
The article is eligible for web story generation.

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Dbi-Services

18h

173

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Step-by-Step Guide to Enabling Copilot in SSMS

SSMS 21, the latest version, integrates with Copilot.
Copilot requires an Azure subscription, which incurs costs based on prompts. Microsoft Entra authentication is an option.
Set up Azure OpenAI service by creating it and selecting the gpt-4o model.
In SSMS, access Copilot and configure it with Azure OpenAI Endpoint and API Key.
The Open AI Deployment name defined earlier is used in the configuration.
Configuration settings can also be accessed under 'Tools' > 'Options' > 'Copilot' in SSMS.
Following these steps enables users to start interacting with Copilot in SSMS.
Privacy-wise, OpenAI states that prompts will not be used for model training.
The blog post aims to simplify the setup process for utilizing Copilot in SSMS.

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Arstechnica

224

Image Credit: Arstechnica

New body size database for marine animals is a “library of life”

The Marine Organizational Body Size (MOBS) database is a new open-access resource with body size data for over 85,000 marine animal species.
MOBS covers 40% of all described marine animal species and aims to achieve 75% coverage.
The database enables research on the ocean's biodiversity and global ecosystem.
Body size data is essential for understanding evolution, ecology, and behavior of marine animals.
MOBS focuses on body length, as it is commonly documented unlike body mass.
Data collection and updates are facilitated by the World Register of Marine Species (WoRMS) and museum collections.
Challenges in measuring body size include unique methods for different marine species.
MOBS is used to analyze biases in species descriptions and provides a cost-effective research focus.
The project offers a valuable platform for researchers amidst funding challenges in scientific research.
The database is a key resource for exploring marine macroecology and macroevolution.

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13 Likes

Medium

381

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What Is SQL and Why It’s Everywhere Today

SQL is a standard programming language for relational databases used in various fields where data management is crucial.
It is an integral part of web applications, mobile apps like Spotify, and social media platforms like Instagram.
SQL stands for Structured Query Language and is considered a fourth-generation language.
Despite the emergence of other languages, SQL remains the most implemented language for databases.
Learning SQL can be beneficial for individuals interested in computer programming or planning to enter the field.

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Dbi-Services

Image Credit: Dbi-Services

Exploring the Future of Data at SQLBits 2025 in London

SQLBits event 2025 in London featured workshops and sessions on data management, data engineering, and analytics with Microsoft technologies.
Focus was on AI evolution, Business Intelligence, and Data Engineering, highlighting Microsoft Fabric as a unifying data platform.
Attendees gained insights on integrating AI models with data, developing context-aware AI assistants, and understanding Microsoft Fabric.
One-day workshops covered AI functionalities in SQL Server and Azure SQL, as well as Microsoft Fabric components and administration.
Event attendees appreciated the high-quality workshops and engaging activities, like the Kahoot Quiz.
Great networking opportunities at the event, including enjoying coffee at the Microsoft booth.
Participants left with a better understanding of the latest technologies and a desire to apply newly gained knowledge in future projects.
Overall, SQLBits 2025 was recommended for data professionals seeking insights into Microsoft technologies.

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2 Likes

Soais

376

Hyperautomation Meets Generative AI: Unlocking Smarter Automation

Automation is advancing through the integration of Generative AI, enhancing the creation of flexible and adaptive workflows for developers.
Hyperautomation combines RPA, AI, ML, and Process Mining to automate end-to-end workflows efficiently with minimal human involvement.
Generative AI utilizes LLMs trained via unsupervised learning to create new content by identifying patterns from vast datasets.
The fusion of Hyperautomation and Generative AI enables enhanced decision-making, unstructured data processing, dynamic content creation, and continuous learning.
The integration empowers organizations to become fully automated enterprises, boosting operational efficiency, adaptability, and innovation.
UiPath seamlessly incorporates generative AI into automation workflows, leading to increased efficiency, improved customer experience, cost savings, scalability, and enhanced innovation.
The future of hyperautomation with generative AI includes hyper-personalization, autonomous decision-making, end-to-end intelligent automation, ethical AI governance, and augmented workforce collaboration.
Embracing these advancements can help organizations drive innovation, create value, and stay competitive in the digital transformation landscape.
The convergence of hyperautomation and generative AI revolutionizes automation, offering smarter, more adaptive workflows for businesses.
UiPath demonstrates the potential of combining RPA and generative AI technologies to enhance efficiency, scalability, and customer satisfaction.
The article qualifies for web story generation as it discusses relevant and informative content on the integration of Hyperautomation and Generative AI in automation processes.

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