News Aggregator

Securing DevOps Pipelines: A Practical Guide to Balancing Speed and Security

Aggregated on: 2025-06-02 19:13:07

Security has been a major concern in today’s world. Security issues lead to data breaches with consequences such as data loss and financial losses. As a result, there is a need to focus on enhancing security. The OWASP top 10 is a good guide for DevOps teams to mitigate key web application security risks. Integrating security strategies from the beginning to the end of the process can enable DevOps teams to enhance the overall security of the applications

Enterprise Data Loss Prevention (DLP) Security Policies and Tuning

Aggregated on: 2025-06-02 18:13:07

I’ve worked with a lot of enterprise customers over the years—big ones, too—and a common struggle I see is with their Data Loss Prevention (DLP) policies. Even though they’ve had the product for years, they often face one of two issues: either the policies are too weak and don’t flag anything, or they’re too strict, overwhelming teams with false alerts. When DLP policies aren’t properly tuned, it creates gaps that can lead to business disruptions and even huge losses. A well-known example is NASA’s ITAR email filtering issue in 2019, where an excessively strict DLP policy blocked legitimate emails, grinding operations to a halt. While there are many such cases, enterprises rarely disclose disruptions caused by poor policy design. The stakes are simply too high—reputation, confidential information, and, most importantly, customer trust are all on the line. Because of this, companies are often reluctant to seek external help in refining their security policy infrastructure, even when expert guidance could prevent costly mistakes. Ultimately, security software vendors play a crucial role in simplifying policy tuning, ensuring enterprises can strike the right balance between protection and usability.

PostgreSQL Performance Tuning

Aggregated on: 2025-06-02 17:13:07

PostgreSQL is already known for its reliability, extensibility, and open-source pedigree and continues to grow and evolve with each release. PostgreSQL 17 introduces several performance improvements and features that make it a powerhouse for Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) workloads. This blog will explore advanced performance tuning techniques for PostgreSQL 17 and highlight key improvements compared to versions 15 and 16.

From Hype to Harm: Why AI Governance Needs More Than Good Intentions

Aggregated on: 2025-06-02 16:13:07

The race to implement AI technologies has created a significant gap between intention and implementation, particularly in governance. According to recent data from the IAPP and Credo AI's 2025 report, while 77% of organizations are working on AI governance, only a fraction have mature frameworks in place. This disconnect between aspirational goals and practical governance has real consequences, as we've witnessed throughout 2024-2025 with high-profile failures and data breaches. I've spent the last decade working with organizations implementing AI solutions, and the pattern is distressingly familiar: enthusiasm for AI capabilities outpaces the willingness to establish robust guardrails.

The Truth About AI and Job Loss

Aggregated on: 2025-06-02 15:58:07

I keep finding myself in conversations with family and friends asking, “Is AI coming for our jobs?” Which roles are getting Thanos-snapped first? And will there still be space for junior individual contributors in organizations? And many more. With so many conflicting opinions, I felt overwhelmed and anxious, so I decided to take action instead of staying stuck in uncertainty. So, I began collecting historical data and relevant facts to gain a clearer understanding of the direction and impact of the current AI surge.

MySQL Formatter: How to Make Beautiful Code and Why You Need It

Aggregated on: 2025-06-02 15:13:07

A MySQL formatter makes code beautiful. But have you ever wondered what beautiful code looks like? You might think that this is not so important. As long as it works as intended, why make a case about it?

How to Improve Copilot's Accuracy and Performance in Power BI

Aggregated on: 2025-06-02 14:13:07

Copilot in Power BI has been a powerful advancement in making data analysis accessible to everyone. But the quality of Copilot's output is heavily dependent on the foundation it sits upon — your Power BI data model and metadata. If Copilot doesn't understand your data structure clearly, its responses can become vague, inaccurate, or not business-friendly. This article will explain how building a strong semantic model and using rich metadata and descriptions could improve Copilot’s accuracy in Power BI.

Revolutionizing KYC: Leveraging AI/ML for Regulatory Compliance

Aggregated on: 2025-06-02 13:13:07

Know Your Customer (KYC) embodies a sophisticated and proactive compliance framework strategically adopted by financial institutions to methodically scrutinize and validate client identities, transactional behaviors, and risk exposures. Beyond mere regulatory formality, KYC constitutes an integral pillar for institutional integrity, meticulously architected to mitigate systemic vulnerabilities such as identity fraud, illicit financial flows, and potential terrorist financing. Fundamentally, KYC represents an intersection of regulatory rigor and advanced analytical methodologies. It encompasses a structured process of gathering detailed identity evidence—ranging from government-issued documentation to transactional patterns—and applying intricate risk-scoring models to ascertain and continuously reassess customer authenticity, credibility, and behavioral consistency.

Observability Expenses: When ‘Pennies on the Dollar’ Add Up Quickly

Aggregated on: 2025-06-02 12:13:07

I’ve specialized in monitoring and observability for 27 years now, and I’ve seen a lot of tools and techniques come and go (RMon, anyone?); and more than a few come and stay (rumors of the death of SNMP have been – and continue to be – greatly exaggerated). Lately I’ve been exploring one of the more recent improvements in the space – OpenTelemetry (which I’m abbreviating to “OTel” for the remainder of this blog). I wrote about my decision to dive into OTel recently: "What’s Got Me Interested in OpenTelemetry—And Pursuing Certification". For the most part, I’m enjoying the journey. But there’s a problem that has existed with observability for a while now, and it’s something OTel is not helping. The title of this post hints at the issue, but I want to be more explicit. Let’s start with some comparison shopping.

Designing Embedded Web Device Dashboards

Aggregated on: 2025-06-02 11:13:07

Overview A web dashboard serves as the “front panel” for an embedded product — whether that product is a rack-mounted industrial controller, a bike-mounted GPS tracker, or a battery-powered soil-moisture sensor buried in a greenhouse bed. Because the dashboard is delivered over plain HTTP(S) and rendered in any modern browser, users do not have to download a native app, install drivers, or worry about operating-system compatibility; the interface is as portable as a URL. Typical tasks include: Toggling outputs (relays, MOSFETs, LEDs) Inspecting live data such as temperature, humidity, current draw, or RSSI Adjusting parameters like Wi-Fi credentials, alarm set-points, sampling rates Collecting diagnostics like log files or memory statistics for field support staff Implementation Approaches Embed an HTTP server — Mongoose, lwIP-HTTPD, MicroPython’s uHTTPD, or a hand-rolled socket handler - inside the firmware. Then choose, or mix, the patterns below. Each technique sits at a distinct point on the scale of resource cost versus user-experience richness.

Scaling Azure Microservices for Holiday Peak Traffic Using Automated CI/CD Pipelines and Cost Optimization

Aggregated on: 2025-05-30 20:28:05

Scaling microservices for holiday peak traffic is crucial to prevent downtime and ensure a seamless user experience. This guide explores Azure DevOps automation, CI/CD pipelines, and cost-optimization strategies to handle high-demand traffic seamlessly. Manual scaling quickly becomes a bottleneck as organizations deploy dozens, sometimes hundreds, of microservices powered by distinct backend services like Cosmos DB, Event Hubs, App Configuration, and Traffic Manager. Multiple teams juggling these components risk costly delays and errors at the worst possible moments. This is where automation comes in: a game-changing solution that transforms complex, error-prone processes into streamlined, efficient operations.

Agentic AI Systems: Smarter Automation With LangChain and LangGraph

Aggregated on: 2025-05-30 19:13:05

Things are changing fast in the world of automation. We're not just talking about those old, clunky robots that do the same thing over and over. Now, we're seeing really clever systems that can actually adapt and figure things out on their own. Leading the charge in this exciting shift are what we call Agentic AI Systems, and they're powered by some seriously cool tools like LangChain and LangGraph. These technologies are opening up a whole new level of 'smart automation.' We're talking about systems that can actually think through problems, make plans, take action, and even learn as they go. This makes them incredibly useful for those tricky tasks and situations that are always changing.

Web Crawling for RAG With Crawl4AI

Aggregated on: 2025-05-30 18:13:05

The importance of AI-powered web crawling and data extraction cannot be overstated. With the exponential growth of online data, businesses and organizations need efficient and accurate methods for collecting and analyzing data to inform their decision-making processes. Crawl4AI and Ollama offer a range of features and benefits that can help address these challenges, from automated web crawling and data extraction to natural language processing and machine learning. Crawl4AI is a powerful tool for AI-powered web crawling and data extraction. It offers a range of features and benefits, including automated web crawling, data extraction, and natural language processing. With Crawl4AI, users can easily extract data from websites, social media platforms, and other online sources, and then analyze and visualize the data using a range of tools and techniques. Crawl4AI is particularly useful for data scientists and machine learning engineers who need to collect and analyze large datasets for their projects.

My Favorite Interview Question

Aggregated on: 2025-05-30 17:13:05

Interviews are on my mind again (read into that whatever you want) and I’ve been having conversations about the experience on both sides of the Zoom camera. Finding out whether a candidate can do the job is incredibly challenging. The usual slate of techniques—from so-called “experiential questions” (“tell me about a time when you didn’t agree with a coworker”) to google-style brain teasers (“how would you go about finding out the weight of the moon using nothing but croutons”) to supposed leetcode questions (“You have two linked lists and you need to combine them”)—fall short in so many ways, but primarily they fail in the most critical way:

Yet Another GenAI Nightmare: Seven Shadow AI Pitfalls to Avoid

Aggregated on: 2025-05-30 16:13:05

If you’re ancient like me, you probably remember Lotus Notes. The leading groupware platform of the last millennium, it not only provided corporate email and pre-Slack communications, it also empowered anyone in the organization to build and publish mini-web sites for anyone to use. It didn’t take long for this whole employee empowerment train to go off the rails. Suddenly, Madge in accounting could slap up a site that exposed private corporate data—with the IT organization none the wiser. No testing, no compliance, no oversight at all.

Resolving Parameter Sensitivity With Parameter Sensitive Plan Optimization in SQL Server 2022

Aggregated on: 2025-05-30 15:13:05

For years, database administrators and developers working with SQL Server faced a persistent performance issue known as parameter sniffing. This problem arises because SQL Server’s traditional query plan caching mechanism generates only a single execution plan for a parameterized query. That plan is based on the parameter values used during the query's first execution. While efficient in many cases, this behavior leads to significant performance degradation when parameter values vary widely in terms of their data selectivity or distribution. For example, a plan optimized for a parameter value that filters a small subset of rows can be reused for a parameter that retrieves millions of rows, causing inefficient scans and resource overuse.

Source-Driven Development in Salesforce: Managing Metadata and API Versions

Aggregated on: 2025-05-30 14:13:05

Development on Salesforce has seen major changes in the last few years. SDD has made it possible for teams to match their Salesforce processes to the best modern DevOps approaches. Fundamentally, SDD depends on version control, automated deployments, and coding your data’s metadata. With benefits like consistency, traceability, and automation, such changes introduce new challenges about how versions and metadata should be managed throughout the project. Here, we’ll look at SDD principles and guide you through properly managing metadata and API versions in a Salesforce environment.

Designing Fault-Tolerant Messaging Workflows Using State Machine Architecture

Aggregated on: 2025-05-30 13:13:05

Abstract As a leader of projects for the backend of a global messaging platform that maintains millions of users daily, I was also responsible for a couple of efforts intended to enhance the stability and failure tolerance of our backend services. We replaced essential sections of our system with the help of the state machine patterns, notably Stateful Workflows. The usage of this model led to the elimination of problems in the field of message delivery, visibility of the read receipt, and device sync, such as a mismatch of phone directories. The intention of this article is to let the reader know how to keep a messaging infrastructure highly available and adaptable by sharing the practicalities and trials one faces when bringing the said architectures into production.

Apache Spark 4.0: Transforming Big Data Analytics to the Next Level

Aggregated on: 2025-05-30 12:13:05

Hurray! Apache Spark 4.0, released in 2025, redefines big data processing with innovations that enhance performance, accessibility, and developer productivity. With contributions from over 400 developers across organizations like Databricks, Apple, and NVIDIA, Spark 4.0 resolves thousands of JIRA issues, introducing transformative features: native plotting in PySpark, Python Data Source API, polymorphic User-Defined Table Functions (UDTFs), state store enhancements, SQL scripting, and Spark Connect improvements. This report provides an in-depth exploration of these features, their technical underpinnings, and practical applications through original examples and diagrams. The Evolution of Apache Spark Apache Spark’s in-memory processing delivers up to 100x faster performance than Hadoop MapReduce, making it a cornerstone for big data analytics. Spark 4.0 builds on this foundation by introducing optimizations that enhance query execution, expand Python accessibility, and improve streaming capabilities. These advancements make it a versatile tool for industries like finance, healthcare, and retail, where scalability and real-time analytics are critical. The community-driven development ensures Spark 4.0 meets enterprise needs while remaining accessible to diverse users, from data scientists to engineers.

Is Low Code the Developer's Ally or Replacement? Debunking Myths and Misconceptions

Aggregated on: 2025-05-30 11:13:05

The rise of low-code development platforms has ignited passionate debates within the software development community. As these tools promise to democratize application creation and accelerate development cycles, a fundamental question emerges: Are low-code platforms here to supplement professional developers, or will they eventually render traditional coding obsolete? This tension between opportunity and threat has generated numerous myths and misconceptions about low-code's place in the development ecosystem. For professional developers, the question isn't merely academic — it's existential. With organizations increasingly adopting low-code solutions to address development backlogs and resource constraints, understanding the true relationship between traditional development and low-code approaches has never been more important. This article examines the reality behind the rhetoric, offering evidence-based insights into how low-code is reshaping — not replacing — the developer profession.

Bridging UI, DevOps, and AI: A Full-Stack Engineer’s Approach to Resilient Systems

Aggregated on: 2025-05-29 21:13:05

Modern full-stack engineering isn’t confined to just coding a front-end and back-end — it’s about bridging the user interface (UI), DevOps processes, and artificial intelligence (AI) into one cohesive, resilient system. A full-stack engineer today might design an Angular UI, implement a Node.js API, set up CI/CD automation, and even integrate an AI-powered feature — all with an eye toward scalability and reliability. This article explores a holistic approach to resilient system design, touching every layer from the Angular frontend to the Node backend, with DevOps and AI as glue that binds them. We’ll use examples, code snippets, and diagrams to illustrate how these pieces come together. The content is geared to be accessible to general developers while also delving into details that senior engineers and DevOps architects expect.

The Ultimate Guide to Code Formatting: Prettier vs ESLint vs Biome

Aggregated on: 2025-05-29 20:13:05

It doesn't matter if you work alone or in a large team — code formatting should be uniform for all developers. It reduces noise and arguments about code style at code review, makes code more readable, and speeds up development by eliminating the need to manually format each line. In this article we'll take a closer look at three of the most popular tools with detailed customization instructions.

Security by Design: Building Full-Stack Applications With DevSecOps

Aggregated on: 2025-05-29 19:13:05

Building a full-stack application with Angular (frontend) and Node.js (backend) demands a holistic security approach. Security by design means baking in security from the architecture stage and throughout development, rather than as an afterthought. DevSecOps extends DevOps by integrating security into every phase of the software lifecycle – developers, operations, and security teams share responsibility to ensure continuous security. This article explores how to secure an Angular + Node.js application end-to-end (frontend, backend/API) and embed security into the CI/CD pipeline with DevSecOps practices. We’ll include code snippets (like input validation and JWT auth) and diagrams for a secure architecture and pipeline.

When Airflow Tasks Get Stuck in Queued: A Real-World Debugging Story

Aggregated on: 2025-05-29 18:13:05

Recently, my team encountered a critical production issue in which Apache Airflow tasks were getting stuck in the "queued" state indefinitely. As someone who has worked extensively with Scheduler, I've handled my share of DAG failures, retries, and scheduler quirks, but this particular incident stood out both for its technical complexity and the organizational coordination it demanded. The Symptom: Tasks Stuck in Queued It began when one of our business-critical Directed Acyclic Graphs (DAGs) failed to complete. Upon investigation, we discovered several tasks were stuck in the "queued" state — not running, failing, or retrying, just permanently queued.

Monoliths, REST, and Spring Boot Sidecars: A Real Modernization Playbook

Aggregated on: 2025-05-29 17:28:05

Forget the idea that modernization has to mean rewriting everything. The real work happens in the in-between, where REST meets SOAP, where sidecars live beside WAR files, and where code changes are political before they're technical. Especially in high-stakes, compliance-bound environments like healthcare, government, and labor systems, modernization doesn’t look like a revolution. It looks like a careful negotiation.

What’s Got Me Interested in OpenTelemetry—And Pursuing Certification

Aggregated on: 2025-05-29 16:28:05

Someone (I don’t remember who. If it was you, feel free to take credit in the comments) once told me, “If you want to break a habit, don’t tell anyone. If you want to build a habit or achieve a goal, tell everyone.”

ACID vs BASE: Transaction Models Explained

Aggregated on: 2025-05-29 15:28:05

There are two main approaches to handling transactions: either we follow ACID or BASE principles. All other approaches are just variations of the two; we can even say that, to a certain degree, BASE is a variation of ACID. Furthermore, some databases may pick to support ACID transactions for part of operations, while not providing the same quarantine for others – just like MongoDB here. In today’s text, I will cover the description of both abbreviations, and their use cases, closing with an in-depth summary of the differences between them. For now, let’s say that the biggest difference between the two is: that ACID prioritizes consistency over availability, while BASE prioritizes availability over consistency.

Introducing Graph Concepts in Java With Eclipse JNoSQL, Part 3: Understanding Janus

Aggregated on: 2025-05-29 14:28:05

Graph databases are increasingly popular in modern applications because they can model complex relationships natively. Graphs provide a more natural representation of connected data from recommendation systems to fraud detection. Our previous articles explored graph databases broadly and delved into Neo4j. In this third part, we focus on JanusGraph, a scalable and distributed graph database. Unlike Neo4j, JanusGraph supports multiple backends and leverages Apache TinkerPop, a graph computing framework that introduces a standard API and query language (Gremlin) for various databases. This abstraction makes JanusGraph a flexible choice for enterprise applications.

Building a Real-Time Change Data Capture Pipeline With Debezium, Kafka, and PostgreSQL

Aggregated on: 2025-05-29 13:28:05

Change Data Capture (CDC) is a foundational pattern in modern data engineering. It enables systems to react to database changes in near real-time by streaming inserts, updates, and deletes as events. This capability is critical in a wide range of scenarios: synchronizing microservices, feeding real-time dashboards, updating machine learning features, powering audit logs, or building streaming data lakes. In this tutorial, we’ll walk through how to implement a CDC pipeline using the following components:

Hyperparameter Tuning: An Overview and a Real-World Example

Aggregated on: 2025-05-29 12:28:05

In machine learning, selecting the right algorithm is just the first step. The true power of a model lies in fine-tuning it to extract the best performance. This fine-tuning process, known as hyperparameter tuning, is akin to adjusting the dials on a high-performance engine. Get it right, and your model will achieve optimal accuracy and generalization; get it wrong, and you could end up with a model that underperforms or overfits. Let’s explore hyperparameter tuning across different machine learning algorithms, using a common scenario — predicting house prices. We’ll walk through the tuning process for linear regression, decision trees, and random forests, providing code examples and discussing real-world case studies where hyperparameter tuning made a significant impact.

How GitHub Copilot Helps You Write More Secure Code

Aggregated on: 2025-05-29 11:13:05

Early-career developers often struggle with secure coding practices. GitHub Copilot, an AI pair programmer, can assist in writing safer code when used wisely. However, guidance is key; a 2021 study found that approximately 40% of Copilot’s generated code had security vulnerabilities when developers weren’t careful. GitHub responded by upgrading Copilot’s AI model and adding a real-time vulnerability filter to block common insecure patterns. In this post, we’ll explore how Copilot can help you write more secure code through clear prompts and examples.

Introducing Graph Concepts in Java With Eclipse JNoSQL, Part 2: Understanding Neo4j

Aggregated on: 2025-05-28 21:13:04

Graph databases have rapidly gained popularity in modern software architecture, as systems increasingly rely on relationships, recommendations, and connected data. From social media platforms and fraud detection systems to recommendation engines and knowledge graphs, graph databases offer a powerful way to model and traverse complex relationships that are hard to express efficiently in relational databases. This second part of the series narrows the focus to Neo4j, the market's most prominent graph database engine. We'll explore its architecture, query language (Cypher), and see how Java developers can leverage Eclipse JNoSQL 1.1.8 to integrate it seamlessly into Java applications.

Simpler Data Transfer Objects With Java Records

Aggregated on: 2025-05-28 20:13:04

In very general terms, data transfer objects (DTOs) are structures that allow packing data when information is exchanged among applications or processes. While business objects or even entities own both state and behavior, DTOs should have only state. I personally see them as the apparel that the domain, the application's “center of purity,” puts on when engaging in interactions with the “exterior.” Java records, on the other hand, prove very useful when data-oriented structures are needed, as a lot of boilerplate code is removed.

Strategies for Securing E-Commerce Applications

Aggregated on: 2025-05-28 19:13:04

Electronic commerce or e-commerce has changed the way we transact, invest, purchase, communicate from virtually anywhere in the world as long as you have internet connectivity. Over the past decade, it has been creating many new exciting opportunities for businesses worldwide. However, e-commerce web sites are vulnerable to intruder attacks due to reasons aplenty. If there are loopholes or weaknesses in your design, implementation, testing and maintenance phases you'll land yourself into trouble.

Is Big Data Dying?

Aggregated on: 2025-05-28 18:13:04

In recent years, the notion that “big data is dying” seems to be gaining traction. Some say the big data craze has faded, while others lament the shrinking job opportunities, the increasing complexity of platforms, and the growing intricacy of business demands. But does this really mean big data is dying? I don’t think so. In my view, what’s truly dying is not big data itself, but rather the outdated “dragon-slaying techniques” that no longer have any “dragons” to slay. The “dragon” has evolved, while our weapons remain stuck in the past. Technology needs to evolve, architectures need to be restructured, and capabilities need to be upgraded.

Comparing Managed Postgres Options on The Azure Marketplace

Aggregated on: 2025-05-28 17:13:04

Azure is expanding their Postgres portfolio, now offering a range of managed Postgres services to suit the needs of developers and teams of all sizes. In this post, we’ll walk through the main Postgres options available on the Azure Marketplace, so you can find the one that works best for your application. Why Deploy Postgres Through the Azure Marketplace? If your company runs on Azure infra, using the Azure Marketplace to set up your database makes things simple and efficient. Here’s why:

How to Use AWS Aurora Database for a Retail Point of Sale (POS) Transaction System

Aggregated on: 2025-05-28 16:13:04

Abstract: A point of sale (POS) transaction system is a crucial component of retail and hospitality businesses, as it handles sales, payments, inventory management, and customer data. These systems need to process transactions efficiently, ensure data integrity, and be highly available, particularly in high-traffic environments like retail stores or restaurants.

AI Agents: A New Era for Integration Professionals

Aggregated on: 2025-05-28 15:13:04

• Timeless patterns, smarter tools: AI agents may seem revolutionary, but they are built on timeless integration patterns like those in ESBs and ETLs, enhanced with smarter capabilities like dynamic parsing and semantic routing. • Evolving roles and teams: They unify application and data integration efforts, dissolving silos. Traditional roles like middleware specialists, ETL developers, and data scientists evolve into agentic workflow engineers and applied AI specialists.

ITBench, Part 1: Next-Gen Benchmarking for IT Automation Evaluation

Aggregated on: 2025-05-28 14:13:04

The remarkable promise of GenAI-based agentic (AI agents) solutions, coupled with recent improvements in their ability to handle intricate tasks, has increased the potential for AI agents to manage IT systems. Given the complexity and criticality of IT systems, a key challenge in IT automation is finding reliable ways to evaluate agents before using them in production. The successful adoption of AI agents for IT automation thus depends on the ability to measure and rank their reliability and efficiency in predefined and standard ways.

Memory-Optimized Tables: Implementation Strategies for SQL Server

Aggregated on: 2025-05-28 13:13:04

Throughout my 15-year journey as a SQL Server DBA, I've encountered numerous technologies promising revolutionary performance improvements, but few have delivered as convincingly as memory-optimized tables. First appearing in SQL Server 2014 as part of the in-memory OLTP feature (codenamed "Hekaton"), this technology has matured significantly in subsequent releases. The fundamental concept is elegantly simple: while traditional tables live on disk and must be loaded into memory for processing, memory-optimized tables permanently reside in memory with disk storage serving only as a persistence mechanism.

Beyond Accuracy: A Product-Led Case for State Space Models Over Transformers

Aggregated on: 2025-05-28 12:13:04

The AI industry’s obsession with Transformers might finally be waning. While OpenAI and Google, with their extensive language models capture the public attention, there is a more discreet change taking place in the way AI is implemented into production systems, or how it is AI is utilized within industry processes. Among practitioners who are less concerned with competition metrics and more focused on speed, efficiency, and scalable solutions, newer versions such as Mamba of State Space Models (SSMs) appear to be winning some favor. The theoretical base for the SSM approach has existed for some time, but its practical application in competition against Transformers is relatively recent.

How to Introduce a New API Quickly Using Micronaut

Aggregated on: 2025-05-28 11:13:04

In the first two articles of this series (part 1 and part 2), I demonstrated how quickly an idea can become a reality using Spring Boot, the framework I have used for over 10 years to establish new services. I stepped out of my comfort zone in the last article (part 3) when I used Quarkus for the first time, which offered a really nice CLI to assist with the development process. I would like to close out this short series with another framework that’s new (to me), called Micronaut.

Useful System Table Queries in Relational Databases

Aggregated on: 2025-05-27 21:43:04

Overview System tables are managed by the DBMS (Database Management System), which provides visibility into how the databases work internally. In this article, I will go over the system tables and their use cases for SQL Server, Oracle, PostgreSQL, and MySQL databases. MySQL Queries 1. See how large each table is in terms of rows to identify large data sets or usage hotspots.

Mastering Deployment Strategies: Navigating the Path to Seamless Software Releases

Aggregated on: 2025-05-27 20:28:06

Deploying code to production is a thrilling moment for any software engineer or development team. Seeing your hard work come to life and serve millions of users is immensely satisfying. However, the journey to get there is not always smooth sailing. Navigating the various deployment strategies can be a complex and challenging task, with each approach offering its own unique set of advantages and drawbacks. In this comprehensive blog post, we'll dive deep into the top five most commonly used deployment strategies, exploring their key characteristics, benefits, and potential pitfalls. Whether you're a seasoned DevOps engineer or just starting your software development journey, this guide will equip you with the knowledge to make informed decisions and ensure seamless software releases.

How To Build AI-Powered Prompt Templates Using the Salesforce Prompt Builder

Aggregated on: 2025-05-27 19:28:04

Corporate use of Generative AI is currently on an exponential rise, looking to enhance business capabilities and improve agents' efficiency in a variety of cases: writing customer emails, summarizing case conversations, or giving a record snapshot from multiple objects for a quick overview of a customer. Users should write proper instructions to get the optimal output from Generative AI. These instructions are given as prompts, and effective prompts will flow nicely with AI processing. This step-by-step guide will show how efficient Salesforce's Prompt Builder is in creating effective prompt templates by retrieving data and using merge fields referencing record fields and flows. These templates serve to create more personalized output using generative AI. To create a new template, start by enabling Einstein in your organization. Assign a user with permissions set to “Prompt Template Manager.” This will allow developers to construct and supervise your templates.

Caching 101: Theory, Algorithms, Tools, and Best Practices

Aggregated on: 2025-05-27 18:13:04

Some time ago I wrote a post on scalability. In it, I mentioned caching as one of the most important techniques to help our system scale. Today, I want to take advantage of that fact and dive deeper into caching itself. Today, I will walk you through basic definitions, the most common problems with caching, different caching eviction algorithms (or cache algorithms), mention the most commonly used tools, and share some best practices when working with cache.

Optimizing Serverless Computing with AWS Lambda Layers and CloudFormation

Aggregated on: 2025-05-27 17:13:04

Recent advancements in cloud computing and serverless architectures have already changed the way applications are created. Among the most widely acknowledged services for serverless computing is AWS Lambda, which enables the execution of code without managing any servers. On the other hand, a large-scale application generally contains many dependencies and maintainable configurations. This article examines how AWS Lambda layers and CloudFormation can be used to develop scalable, efficient, and maintainable serverless systems.

A Guide to Auto-Tagging and Lineage Tracking With OpenMetadata

Aggregated on: 2025-05-27 16:28:04

Tagging metadata and tracking SQL lineage manually is often tedious and prone to mistakes in data engineering. Although essential for compliance and data governance, these tasks usually involve lengthy manual checks of datasets, table structures, and SQL code. Thankfully, advancements in large language models (LLMs) such as GPT-4 provide a smarter and more efficient solution. This guide helps beginner data engineers learn how to use LLMs with tools like OpenMetadata, dbt, Trino, and Python APIs to automate metadata tagging (like identifying PII) and lineage tracking for SQL changes. Let's explore the details.

Introducing Graph Concepts in Java With Eclipse JNoSQL

Aggregated on: 2025-05-27 15:28:04

When we talk about databases today, we face a landscape of diversity. Gone are the days of a one-size-fits-all solution. We live in an era of polyglot persistence, where the guiding principle is to use the most appropriate data model for each use case. This article focuses on graph databases, their structure, practical applications, and how Java developers can leverage Eclipse JNoSQL and Jakarta Data to work seamlessly with them. Graph databases enable richer and more natural modeling of connected data. Unlike traditional relational databases that require complex joins to represent relationships, graph databases make relationships first-class citizens. A graph model organizes data into vertices (nodes) and edges (relationships). Both vertices and edges can hold properties, and edges are directional, adding semantic meaning to how data points connect.

Building a Real-Time Audio Transcription System With OpenAI’s Realtime API

Aggregated on: 2025-05-27 14:13:04

OpenAI launched two new Speech to Text models gpt-4o-mini-transcribe and gpt-4o-transcribe in March 2025. These models support streaming transcription for both completed and ongoing audio. Audio transcription refers to converting the audio input to text output (output format would be text or json).The transcription of already completed audio is much simpler using the transcription API provided by OpenAI. The Realtime transcription is useful in application that require immediate feedback such as Voice assistants, Live captioning, Interactive voice applications, Meeting transcription and Accessibility tools. OpenAI has provided Realtime Transcription API (currently in beta) which allows you to stream audio data and receive transcription results in real-time. The realtime transcription API should be invoked using WebSocket or webRTC. This article focuses on invoking Realtime API using Java WebSocket implementation. This image has been designed using resources from Flaticon.com