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Medium

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AI Can Write SQL — So What Happens to Data Engineers?

AI tools like ChatGPT are becoming proficient at writing SQL queries quickly.
There is a concern among data engineers that AI may replace their jobs due to this capability.
Although AI can write SQL, it cannot replicate the role of a skilled data engineer anytime soon.
SQL is just one aspect of a data engineer's job, and their expertise involves much more than just writing queries.

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Amazon

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Build a multi-Region session store with Amazon ElastiCache for Valkey Global Datastore

The article discusses building a multi-Region session store with Amazon ElastiCache for Valkey Global Datastore.
The solution offers a unified database caching layer for application servers and secure cross-Region replication.
ElastiCache Global Datastore allows writing to one cluster and reading from two other cross-Region replica clusters.
It enables low-latency reads and disaster recovery across Regions.
The article explains how to evolve a caching architecture from a single Region to a multi-Region setup.
This transition involves duplicating infrastructure to a second Region and utilizing AWS Global Accelerator for optimal connectivity.
Challenges of a cross-Region caching layer are discussed focusing on dataset sharing between Regions.
To address these challenges, adopting the ElastiCache Global Datastore feature is recommended.
The article provides insights on implementing ElastiCache for Valkey Global Datastore for secure cross-Region replication.
It includes detailed steps on configuring VPC peering or using AWS Transit Gateway for cross-Region connectivity.
An automation solution using AWS Lambda and Route 53 is introduced for updating DNS records upon global datastore failover.

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Medium

341

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1Z0–830 Sample Questions for Java SE 21 Developer Certification Exam (with Resources)

The article provides sample questions for the 1Z0–830 exam or Java 21 certification exam and offers a discount coupon for a related Udemy course.
Java 21 Certification Exam (1Z0–830) is considered challenging with long, intricate questions and multiple options.
The exam duration has been extended to 120 minutes for 50 questions, requiring candidates to solve questions efficiently.
It is crucial to practice sample questions and take mock tests to enhance speed and accuracy in preparation for the exam.
The Udemy course '1Z0–830 Java SE 21 Developer Professional Exam Practice Test' contains over 266 questions related to the certification.
Sample questions from the article involve coding scenarios and multiple-choice queries to test Java knowledge.
Readers are encouraged to attempt the sample questions provided in the article and can avail the course at a discounted price using a coupon code.
The article emphasizes the importance of first-time success in the Java certification exam due to its cost and difficulty level.
The author offers insights into preparing for Java SE 17 certification as well and recommends relevant Udemy resources for exam readiness.
To access additional Java 21 exam-related articles and resources, readers are directed to explore further content shared by the author.

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Hackernoon

104

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Navigating MySQL Numeric Data Types

MySQL provides various numeric data types including integer, fixed-point, floating-point, and bit types for storing numeric values efficiently.
Integer types in MySQL support signed and unsigned values, with options like ZEROFILL being deprecated.
Fixed-point types like DECIMAL are suitable for exact numeric values such as currency, while floating-point types like DOUBLE are ideal for approximate values.
Tips include choosing the smallest integer that fits, utilizing UNSIGNED for non-negative values, and using DECIMAL for monetary values to ensure data integrity and avoid common pitfalls.

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Amazon

166

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Supercharging vector search performance and relevance with pgvector 0.8.0 on Amazon Aurora PostgreSQL

Amazon Aurora PostgreSQL-Compatible Edition now supports pgvector 0.8.0, enhancing vector search capabilities for applications requiring semantic search and Retrieval Augmented Generation (RAG).
pgvector 0.8.0 on Aurora PostgreSQL-Compatible delivers up to 9x faster query processing and 100x more relevant search results, addressing scaling challenges for enterprise AI applications.
Improvements in pgvector 0.8.0 include enhanced performance, complete result sets, efficient query planning, and flexible performance tuning for vector search applications.
Overfiltering issues in previous versions of pgvector are addressed by iterative index scans in pgvector 0.8.0, providing improved query reliability and performance in filtered vector searches.
Query examples demonstrate the impact of pgvector 0.8.0 improvements, showcasing better performance, result completeness, and cost estimation accuracy in complex filtering scenarios.
Benchmark tests highlight significant performance enhancements with pgvector 0.8.0 compared to 0.7.4, showing faster query processing and improved result quality across various query patterns.
Best practices for utilizing pgvector 0.8.0 on Aurora PostgreSQL-Compatible include optimizing index configurations, query-time tuning, and operational considerations for efficient vector search implementations.
pgvector 0.8.0 boosts semantic search, recommendation systems, and RAG applications by offering faster retrieval, lower latency, improved recall, and complete result sets for large-scale AI applications.
Aurora PostgreSQL-Compatible's scalability combined with pgvector 0.8.0's enhancements provides a robust foundation for enterprises to build high-performance AI applications.
Integration of pgvector 0.8.0 into applications is supported by Amazon Aurora resources and operational best practices to optimize vector storage, retrieval, and query performance.
pgvector 0.8.0 on Aurora PostgreSQL-Compatible empowers organizations with advanced vector search capabilities, ensuring responsive, accurate, and cost-effective AI applications as data scales.

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Sdtimes

317

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Amazon launches serverless distributed SQL database, Aurora DSQL

Amazon has announced the general availability of Amazon Aurora DSQL, a serverless distributed SQL database focused on scalability, high availability, and zero infrastructure management.
Aurora DSQL ensures 99.99% availability in a single region and 99.999% availability across multiple regions through features like replicating committed log data to user storage replicas across three Availability Zones.
Multi-region clusters in Aurora DSQL improve availability by utilizing Regional endpoints for read and write operations, with a third Region acting as a log-only witness for optimal geographic location optimization.
Aurora DSQL is suited for applications using microservices and event-driven architectures, with added features like an MCP server for natural language interaction and improvements in cluster management, AWS service integration, and PostgreSQL support.

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Siliconangle

195

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Cast AI debuts Database Optimizer to streamline cloud database caching

Cast AI Group Inc. has launched Database Optimizer, a new approach to caching that enhances cloud database performance by providing a fully autonomous caching layer that requires zero application changes or manual tuning.
Database Optimizer simplifies operations and reduces wasteful database spending, making it easier for DevOps personnel, platform engineers, and cloud database administrators to manage.
Key features of Database Optimizer include simplified optimization, immediate performance improvements, up to 10 times faster data retrieval, and automation that helps cut operational costs while maintaining high performance.
Early users like Akamai Technologies Inc. and Flowcore Technologies Inc. have praised the DBO offering, with significant improvements in performance and cost reduction. Cast AI recently raised $108 million in new funding to enhance its platform.

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Hackernoon

169

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The Marketing Data Cleaning Query Cookbook

In the world of Agentic AI, data quality is crucial as autonomous systems drive marketing decisions based on CRM and analytics data.
Gartner reports that poor data quality costs organizations an average of $12.9 million annually, affecting efficiency and decision-making.
The article offers a SQL query cookbook for marketers to clean and enhance data using SQL Server Management Studio.
Marketers can now describe data issues in plain English, generate SQL queries, and run them without advanced technical support.
Queries include tasks like fixing name capitalization, identifying and fixing suspicious or swapped names, building full names, and trimming extra spaces.
Additionally, the cookbook covers fuzzy matching, standardizing text, dealing with missing data, and avoiding duplicate data using SQL queries.
It also addresses out-of-range values, contradictory data, invalid emails, identifying internal or test contacts, and splitting data into multiple columns.
Lastly, the article suggests training GPT models on specific schemas for more accurate SQL assistance.
Clean data is emphasized as the key to successful AI implementation in marketing, facilitated by SQL queries and ChatGPT.
Marketers are encouraged to leverage the cookbook, engage with ChatGPT, and enhance their data management skills for efficient AI utilization.

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Dev

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Stop Forcing Time Windows on Bursty User Behavior - Try This Instead

User activity often occurs in bursts, challenging traditional fixed-time windows in analytics.
Session Windows offer a smarter approach by grouping events based on activity patterns.
Benefits include flexible event grouping, behavior-driven analytics, simplified querying, and enhanced insight.
Session Windows group events close in time, considering a gap duration to define sessions.
Recommended for modeling user behavior, IoT data, anomaly detection, and financial market activities.
RisingWave employs the 'SESSION' window function frame type for session window semantics.
SQL query examples showcase defining session windows, computing session boundaries, and aggregating data.
Aggregating data within session windows aids in calculating metrics per session group.
Session windows in RisingWave provide behavior-driven insights into real-world event streams' irregular nature.
Explore RisingWave Cloud for a managed experience or the open-source version for self-running session windows.

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Dev

342

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The Best Tools to Design Database Schemas Visually in 2025

Visual database tools are essential for designing schemas, collaborating, and documenting work, especially with growing complexity and team collaborations.
DbSchema stands out for its SQL and NoSQL support, visual schema diagrams, offline work capability, Git version control, and ability to generate sample data.
DBeaver, an open-source tool, provides good ERD viewer, connects to many database systems, and offers useful SQL editor, although obtaining a trial license can be cumbersome.
DataGrip by JetBrains targets developers with smart autocomplete and refactoring for SQL, diagram view, support for various SQL engines, and built-in database inspection tools.
dbForge Studio excels in solid diagramming tools, comparison features, automation options, but has limited support for PostgreSQL and is more focused on SQL Server and MySQL.
Vertabelo, a web-based tool, emphasizes clean design, early planning, logical and physical design views, version tracking, and multi-user commenting, making it suitable for planning phases.
MySQL Workbench is ideal for MySQL users, with features like schema diagramming, forward and reverse engineering, table relationship mapping, but lacks multi-database flexibility.
Navicat offers visual ERD creation, data sync, cloud integration, and supports various SQL databases, but creating diagrams may involve additional steps compared to other tools.
Tools like Toad Edge, SQLDBM, and HeidiSQL cater to specific needs like MySQL and PostgreSQL support, online collaboration, and lightweight tasks, each with its distinct strengths and limitations.
SQLDBM, a clean and modern web-based tool, offers a good visual design, online team collaboration, GitHub integration, but certain advanced features are restricted to paid plans.
HeidiSQL, a free and simple desktop tool, is efficient for quick database exploration and queries, supporting key databases like MariaDB, MySQL, SQL Server, and PostgreSQL, despite its basic interface and limited features.

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Siliconangle

396

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IBM’s agentic strategy brings generative AI down to earth and into production

IBM is focusing on a strategy called 'agentic strategy' to scale generative AI ambitions effectively, emphasizing execution oversight and governance.
This strategy aims to balance creative momentum with practical operational discipline, bridging the gap between content creation and production-related challenges.
Bruno Aziza, IBM's group vice president for data, emphasizes the importance of managing costs and governance alongside content engineering and engagement.
IBM's agentic strategy is about agents working across apps, clouds, and workflows to drive actual value and focuses on both better engagement and efficient production management.
IBM is promoting a federated framework for integration, enabling data, agents, and business logic to move seamlessly across various silos, clouds, and tools.
The company offers prebuilt agents connected to numerous applications, allowing organizations to start fast, customize as needed, and scale confidently in hybrid and multi-cloud environments.
IBM is aiming to avoid hard-coded processes and rigid workflows by enabling dynamic agent behavior through its watsonx Orchestrate product for flexible and adaptive strategies.
The focus of IBM's approach goes beyond the excitement of generative AI to address operational challenges like scale, accountability, cost, and governance.
IBM emphasizes the importance of transformation, automation where needed, elimination of unnecessary processes, and a modular mindset to optimize use cases.
IBM's open approach to operationalizing agents at scale emphasizes its collaboration with companies like Box, Oracle, and Salesforce in production environments.

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Dev

104

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Why Cloud Infrastructure Matters

Cloud infrastructure is essential for delivering computing resources over the internet, allowing for scalability, efficiency, and cost-effectiveness.
The shift to cloud infrastructure is driven by the need for faster development, remote work capabilities, scalability, and resilience in the face of disasters.
Oracle Cloud Infrastructure (OCI) offers robust solutions for running databases and enterprise workloads, showcasing the diverse approaches of cloud providers in addressing similar challenges.
The field of cloud engineering is dynamic and continually evolving, presenting opportunities to explore new tools, enhance CI/CD pipelines, and gain a deeper understanding of cloud systems.

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Amazon

215

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Automate Amazon RDS for PostgreSQL major or minor version upgrade using AWS Systems Manager and Amazon EC2

Automating Amazon RDS for PostgreSQL major or minor version upgrades using AWS Systems Manager and Amazon EC2 simplifies database lifecycle management.
Manual upgrades through AWS Management Console can be error-prone and disrupt application stability.
The automation process involves AWS CLI commands in a Unix shell script and integration with AWS Systems Manager for scaling.
Pre-upgrade checks and instance maintenance updates are executed through reusable modules: PREUPGRADE and UPGRADE.
Logging and notification features are added for monitoring and notification through Amazon S3 and Amazon SNS.
The solution supports major and minor version upgrades and can be extended for multi-VPC or cross-account deployments with additional considerations.
Prerequisites include IAM user permissions, cloning the GitHub repository, preparing RDS instances, creating S3 bucket, SNS topic, IAM policy, and IAM role.
An EC2 instance hosts and runs the upgrade script requiring AWS CLI, PostgreSQL client, bc, and jq library.
Implementation steps include uploading the script to S3, creating an Automation Document, and executing the upgrade process through Systems Manager.
Comprehensive monitoring and notification mechanisms are in place, providing logs for pre-upgrade and upgrade phases.

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Medium

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Mini Project with PostgreSQL: Customer & Order Management

This mini project involves creating a 'Product' table in PostgreSQL for customer and order management.
Data is being populated to simulate real-world e-commerce operations, emphasizing the importance of meaningful sample data.
Various queries are demonstrated, including filtering products based on price, retrieving electronic product names and prices, and calculating total order value per customer.
Over 20 meaningful queries are provided in the 'queries.sql' file to extract business insights like total order value, popular products, and customer activity.

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Medium

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How to Crack Any SQL Interview for Data Engineering in 2025?

SQL interviews for Data Engineering in 2025 typically involve assessing candidates' ability to write efficient and effective SQL queries.
Candidates are evaluated on their SQL problem-solving skills, understanding of filtering, aggregation, joins, CTEs, and window functions.
Foundational SQL patterns such as filtering data, grouping, and aggregating before further filtering are crucial for aspiring data engineers.
Understanding different types of joins and when to use each, like INNER JOIN, LEFT JOIN, and ANTI-JOIN, is essential in SQL interviews.
Performance tuning and knowledge of table scans, indexing, and partitioning are key aspects tested in SQL interviews for data engineering roles.
Candidates are expected to demonstrate proficiency in using window functions, Common Table Expressions (CTEs), and self-joins in SQL queries.
Efficient SQL writing, clarity in logic, and understanding query performance are crucial skills looked for in SQL interviews.
Candidates are advised to optimize queries, avoid redundant scans, and think like a production-ready data engineer during SQL assessments.
Thinking beyond correctness to consider efficiency, readability, and scalability is key to excelling in SQL interviews for data engineering roles.
Real-world scenario-based SQL tasks are often used to evaluate candidates' problem-solving abilities and SQL proficiency.
The ability to write modular, efficient SQL queries that demonstrate clear thinking and scalability is a distinguishing factor in data engineering interviews.

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