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Databases
Soais
1M
421
PeopleSoft India SIG 2025- Powering the Future with Innovation and Intelligence
- The PeopleSoft India Special Interest Group (SIG) 2025 event at Oracle's Technology Hub in Bangalore showcased updates like UX enhancements and machine learning integration for the PeopleSoft community.
- Key highlights included modernized UI/UX, real-time search with Elastic Search, advanced analytics through Pivot Grid 2.0 and Kibana Dashboards, Cloud Manager enhancements, Docker & Kubernetes support, AI and machine learning features.
- PeopleTools 8.61 enhancements featured a revamped Fluid UI, mobile optimization, accessibility improvements, and AI integration.
- Overall, PeopleSoft is evolving with intelligence, agility, visualization, and connectivity, offering smart automation, DevOps tools, insightful dashboards, and secure integration with cloud and AI services.
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Soais
1M
326
Mastering the Keystroke Method in Test Automation
- The Keystroke Method is an innovative approach to test automation that replicates user behavior by capturing and replaying keyboard inputs and mouse actions, beneficial for complex user interfaces.
- This technique involves recording and replaying keyboard and mouse inputs to simulate user actions effectively, especially in dynamic UI settings.
- Implementation steps for the Keystroke Method include selecting nodes in desktop applications, entering values in input fields using keystrokes, moving the mouse cursor with keystrokes, and assigning dynamic objects for mouse click actions.
- Tools like Worksoft Certify support the Keystroke Method, enabling realistic user experience simulation through automated testing.
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Hackernoon
1M
299
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How to Set Up PostgreSQL with NestJS and Docker for Fast Local Development: A Quick Guide
- Setting up PostgreSQL with NestJS and Docker for fast local development is essential for new projects.
- Using Docker to run a local Postgres instance ensures minimal friction, reproducibility, and ease of access.
- Key components include Dockerfile, docker-compose.yml, .env file, sample NestJS config, and practical development commands.
- Commands like npm run db to access database shell and npm run api for the app container provide an interactive development workflow.
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Amazon
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326
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How Amazon maintains accurate totals at scale with Amazon DynamoDB
- Amazon's Finance Technologies Tax team manages tax computation, deduction, and reporting across global jurisdictions using the DynamoDB-tiered tax withholding system.
- The system processes billions of transactions annually, requiring millisecond latency at scale and accurate tracking of individual transaction values for tax calculations.
- Challenges include handling tiered tax rates based on cumulative thresholds and maintaining data consistency across high transaction volumes.
- The solution involves API Gateway, Lambda functions, DynamoDB for tracking cumulative values, and handling error retries using Amazon SQS.
- Implementation includes atomic conditional writes for updating transaction values and audit records, optimizing throughput and minimizing conflicts.
- Performance evaluations show increased throughput with concurrency, but also highlight transient conflict trade-offs in highly concurrent scenarios.
- The DynamoDB conditional write feature allows for simplified, scalable transaction processing without the need for optimistic locking.
- Authors include Principal Solutions Architect, Principal Engineer, and Software Development Engineers from Amazon Finance Technology.
- The system's proactive handling of ConditionalCheckFailedExceptions ensures data consistency and high scalability for concurrent transactions.
- For those looking to implement similar scalable systems, leveraging DynamoDB features and concurrency control mechanisms is essential.
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Amazon
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249
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Build an AI-powered text-to-SQL chatbot using Amazon Bedrock, Amazon MemoryDB, and Amazon RDS
- Text-to-SQL is a valuable approach leveraging large language models to automate SQL code generation for various data exploration tasks like analyzing sales data and customer feedback.
- The article discusses building an AI text-to-SQL chatbot using Amazon RDS for PostgreSQL and Amazon Bedrock, with Amazon MemoryDB for accelerated semantic caching.
- Amazon Bedrock, with foundation models from leading AI companies like AI21 Labs and Amazon, assists in generating embeddings and translating natural language prompts into SQL queries for data interaction.
- Utilizing semantic caching with Amazon MemoryDB enhances performance by reusing previously generated responses, reducing operational costs and improving scalability.
- Implementing parameterized SQL safeguards against SQL injection by separating parameter values from SQL syntax, enhancing security in user inputs.
- The article highlights Table Augmented Generation (TAG) as a method to create searchable embeddings of database metadata, providing structural context for precise SQL responses aligned with data infrastructure.
- The solution architecture includes creating a PostgreSQL database on Amazon RDS, using Streamlit for the chat application, Amazon Bedrock for SQL query generation, and leveraging AWS Lambda for interactions.
- The step-by-step guide covers prerequisites, deploying the solution with CDK, loading data to the RDS, testing the text-to-SQL chatbot application, and cleaning up resources efficiently.
- By following best practices like caching, parameterized SQL, and table augmented generation, the solution showcases enhanced SQL query accuracy and performance in diverse scenarios.
- Authors Frank Dallezotte and Archana Srinivasan provide insights into leveraging AWS services for scalable solutions and optimizing AI and ML workloads with Amazon RDS and Amazon Bedrock.
- The demonstration exhibits the capability of the text-to-SQL application to support complex JOINs across multiple tables, emphasizing its versatility and performance.
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Dbi-Services
1M
108
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SQLDay 2025 – Wrocław – Sessions
- The SQLDay conference in Wrocław began with a series of sessions covering cloud, DevOps, Microsoft Fabric, AI, and more.
- Morning Kick-Off: The day started with sponsors' presentation, acknowledging their support for the event.
- Key Sessions: Sessions included discussions on Composable AI, migrating SQL Server databases to Microsoft Azure, Fabric monitoring, DevOps in legacy teams, productivity solutions, and Azure SQL Managed Instance features.
- Insights: The sessions provided insights on integrating AI into enterprise architecture, best practices for database migration, monitoring tools for Microsoft Fabric, challenges of introducing DevOps in legacy systems, productivity solutions using AI, and updates on Azure SQL Managed Instance features.
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Dbi-Services
1M
413
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SQLDay 2025 – Wrocław – Workshops
- SQLDay 2025 in Wrocław featured pre-conference workshops on various Microsoft Data Platform topics.
- Workshop sessions included topics such as Advanced DAX, Execution Plans in Depth, Becoming an Azure SQL DBA, Enterprise Databots, Analytics Engineering with dbt, and more.
- Sessions catered to different professionals, ranging from experienced Power BI users to SQL Server professionals and those transitioning to cloud environments.
- SQLDay workshops offered valuable insights into Azure, Power BI, SQL Server, and data engineering, providing attendees with practical knowledge and hands-on experiences.
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Dbi-Services
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222
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SQL Server 2025 Public Preview and SSMS 21 now available
- SQL Server 2025 public preview and SSMS 21 are now available for download.
- Key changes with SQL Server 2025 include a native vector type, vector index, optimized locking, and REST API enhancement.
- SSMS 21, based on Visual Studio 2022, offers built-in Copilot integration and a Dark Theme.
- Users are encouraged to install SSMS 21, provide feedback, and expect more blog posts to showcase new features of SQL Server 2025.
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Dev
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349
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Spark Augmented Reality (AR) Filter Engagement Metrics
- Completed an SQL challenge on interviewmaster.ai involving analyzing engagement metrics of branded AR filters.
- Query 1: Retrieved AR filters with user interactions in July 2024 and sorted by total interaction count.
- Query 2: Obtained total interactions per AR filter in August 2024, filtered for filters with over 1000 interactions.
- Query 3: Identified top 3 AR filters with the most interactions in September 2024.
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Dbi-Services
1M
86
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PostgreSQL 17-18 major upgrade – blue-green migration with minimal downtime
- PostgreSQL 18 Beta 1 introduces new features like improved IO on reads, pg_createsubscriber, and pg_upgrade options to facilitate smoother major upgrades through blue-green migration using logical replication technology.
- pg_createsubscriber automates setting up logical replication by converting a physical standby server into a logical replica without copying initial table data, simplifying the process, especially for large databases.
- PostgreSQL 18 Beta 1 adds the --all flag to pg_createsubscriber for creating logical replicas for all databases in an instance with a single command, streamlining replication setup in multi-database environments.
- PostgreSQL 18 brings new capabilities to pg_upgrade, such as the --swap option for faster upgrades, along with improvements like pg_stat_subscription_stats, enhancing monitoring of logical replication write conflicts.
- The article outlines a step-by-step blue-green migration process starting with setting up the blue (primary) environment with PostgreSQL 17, configuring replication settings, creating a physical replica (green environment), and transitioning to logical replication.
- The guide covers creating a publication for all tables, using pg_createsubscriber for logical replication setup, upgrading the green environment to PostgreSQL 18 using pg_upgrade with the --swap method, and ensuring successful logical replication post-upgrade.
- The process involves performing failover and cutover to the upgraded green environment after stopping new writes on the primary, syncing latest LSN, dropping subscription, and executing failover commands to switch application traffic to the upgraded instance.
- The article concludes with insights on leveraging blue-green migrations for major version jumps, automation using tools like Ansible playbooks, involving DEV teams for testing post-upgrade, and addressing logical replication limitations.
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Dev
1M
109
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Rediscovering ACID – The Foundation of Reliable Databases
- Engineers are rediscovering the importance of ACID properties in databases like PostgreSQL and MySQL.
- ACID stands for Atomicity, Consistency, Isolation, and Durability, ensuring reliable transactions.
- Atomicity guarantees that transactions complete entirely or not at all, exemplified through money transfers.
- Consistency enforces rules like constraints and foreign keys, Isolation prevents transaction interference, and Durability ensures data persists post-commit.
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Dev
1M
418
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Python for Oracle on ARM Linux | Part 2 - "shell like" SQL scripts
- Python can be a powerful alternative to shell scripting for Oracle DBAs, providing better readability and cross-platform compatibility.
- By using Python's subprocess module, DBAs can automate tasks like running SQL commands, managing script execution, and spooling outputs.
- Key benefits include integration with Python capabilities like data parsing, reporting, and automation frameworks.
- The essential Python module needed for these tasks is subprocess, which allows running programs such as sqlplus.
- Setting up variables like connection string, SQL file path, output file path, and command argument is crucial for the process.
- Subprocess is utilized with shell=True to run the SQL*Plus UNIX command and execute SQL scripts.
- An example SQL script provided in the article demonstrates data retrieval and JSON processing for weather data.
- A Python script 'py_ora.py' is created to run SQL files via SQL*Plus, redirecting outputs to a specified file for review.
- Running 'py_ora.py' in a miniconda environment successfully executes the SQL file and captures the expected outputs.
- The approach showcased emphasizes the use of Python over traditional shell scripting for Oracle DBA tasks, enhancing automation and maintainability.
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Siliconangle
1M
391
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How Dell is riding the AI wave while serving its massive installed base
- Dell Technologies, led by founder Michael Dell, has successfully navigated tech waves by focusing on consistent growth and profitability.
- As Dell aims to capture the AI opportunity, they are focusing on AI factory systems, leveraging their global channel and modernizing core infrastructure.
- Dell's revenue trajectory depicts key milestones, strategic pivots, and a focus on positioning itself in the AI wave.
- The company's valuation has surged due to its AI focus, capital return strategy, and stock performance, reflecting market confidence in its direction.
- Dell's revenue engine is divided between Client Solutions Group (55% revenue) and Infrastructure Solutions Group (45% revenue), emphasizing the importance of commercial PCs and enterprise gear.
- The company's margin dynamics have shifted towards AI-optimized servers and storage, with a focus on margin expansion through higher-value products.
- Dell's strategy includes focusing on storage refresh cycles, software partnerships, and leveraging their hardware scale in the AI era.
- The article discusses Dell's peer comparison in terms of revenue multiples and provides insights into its valuation and market positioning.
- Dell's strategy to tap into the AI-driven demand involves leveraging its installed base, offering customer choice, and aiming for end-to-end AI factory solutions.
- The article also touches upon Dell's AI spending focus, highlighting the growing importance of enterprise AI and Dell's approach to addressing this market.
- In conclusion, Dell faces execution risks in GPU supply, facility retrofits, software offerings, and competitive challenges, but aims to differentiate through end-to-end AI solutions and channel partnerships.
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Dev
1M
327
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Understanding Database Indexes: A Library Analogy
- Indexes in databases are crucial for efficient data retrieval, similar to how a library catalog is essential for finding books quickly.
- In a database, without indexes, searching for specific data requires a full table scan, which is computationally expensive.
- By creating indexes on columns, databases can significantly speed up queries by skipping irrelevant rows.
- Indexes work by mapping column values to row locations, typically using a B-tree data structure for rapid lookups.
- Different types of indexes, like Primary Key, Unique, and Composite Indexes, serve various purposes in optimizing database queries.
- While indexes improve query performance by enabling faster reads, they can slow down write operations and consume additional storage space.
- Without indexes, database searches would involve scanning every row, leading to slower query times, especially in large datasets.
- Analogous to a library catalog, indexes guide database searches to specific data efficiently, saving time and resources.
- Understanding index types and implementing them correctly can enhance database query optimization for improved performance.
- Database indexes play a vital role in transforming slow searches into quick lookups, improving overall data retrieval efficiency.
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Dev
1M
13
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What is SQL and Why Does It Matter
- SQL is a powerful tool for anyone who works with data, enabling communication with relational databases to show, add, change, or remove data.
- SQL is not just about databases but about asking questions, similar to Google Search but for a company's private data.
- SQL is widely used in various companies and tools, including Excel, Google Sheets, Power BI, and Tableau, making it an essential skill in today's data-driven world.
- Learning SQL helps in breaking down problems, asking the right questions, and spotting patterns, outliers, and insights faster, making it a valuable skill for data-related tasks.
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