Data Engineering

Building Your First FastMCP Server A Complete Guid

Building Your First FastMCP Server: A Complete Guide

ChatGPT Image Jun 20, 2025, 12_50_00 PM.png

Creating AI integrations used to mean wrestling with complex protocols, managing boilerplate code, and dealing with transport layers. FastMCP changes all that. It’s designed to be high-level and Pythonic. In most cases, decorating a function is all you need. This guide walks you through building a production-ready MCP server that any AI system can connect to—whether it’s Claude, GPT-4, or any other MCP-compatible client.

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Building Intelligent AI Applications with LangChai

Ready to transform your AI ideas into reality? Discover how LangChain bridges the gap between raw AI capabilities and practical applications! From chatbots to intelligent assistants, this guide takes you on a journey from concept to production. Dive in and unlock the potential of multi-model AI development!

LangChain empowers developers to build intelligent AI applications by bridging the gap between raw LLM capabilities and practical use cases. It offers modular components, standardized interfaces, and tools for effective integration and deployment across multiple AI models.

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Data Engineers Job Trends after Gen AI

-**Data Engineer Salaries:**Average base salary is $125,000, with experienced professionals earning $150k+ and additional compensation through bonuses and equity. -Explosive Job Growth:~50% year-over-year growth in job market demand, making it one of the fastest-growing tech occupations. -**Skills in High Demand:**Core requirements include SQL, Python/Scala/Java, big data frameworks (Spark, Hadoop), and cloud platforms. -**Talent Shortage:**Companies are creating positions faster than qualified candidates are entering the field, leading to competitive compensation packages. -**Remote Work:**Data engineering roles are highly compatible with remote work.

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Tech Job Market Outlook 2025 after AI Software and

Tech Job Market Outlook 2025: Software and Data Roles in the United States

The technology job market in 2025 continues to evolve rapidly, with significant growth projected across software development and data science fields. According to the Bureau of Labor Statistics (BLS), computer and information technology occupations are expected to grow much faster than the average for all occupations through 2033, with approximately 356,700 openings projected annually2. This comprehensive analysis examines the current state and future outlook for software developers, software engineers, software architects, data engineers, and data scientists, providing detailed insights into salary trends, job growth projections, in-demand skills, remote work opportunities, and hiring challenges.

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Navigating the Evolving Terrain after Gen AI The U

Navigating the Evolving Terrain: The U.S. Technology Job Market Outlook for 2025

I. Executive Summary

The United States technology job market in 2025 presents a complex and evolving landscape, characterized by cautious optimism, significant skill shifts, and ongoing adjustments to new economic and technological realities. While hiring is expected to rebound in some areas, the market remains competitive, with layoffs persisting alongside new job creation. Artificial Intelligence (AI) and cybersecurity are undeniably dominant forces, reshaping skill demands and creating new opportunities across all sectors. The debate around remote and hybrid work models continues, with a discernible tension between employee preferences for flexibility and employer pushes for increased in-office presence. Compensation trends reflect a modest overall growth, with significant premiums for specialized skills, particularly in AI. However, employee satisfaction with pay has declined, and benefits packages are undergoing scrutiny.

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2025 Job Market Analysis Software & Data Professio

2025 Job Market Analysis: Software & Data Professionals from DevOps to AI Specialist

Executive Summary

The 2025 job market for software and data professionals presents a mixed landscape characterized by continued demand for specialized skills despite some softening in certain areas. This report provides a comprehensive analysis of current trends affecting software developers, software engineers, software architects, data engineers, and data scientists across the United States, including salary trends, job growth projections, in-demand skills, remote work trends, and hiring challenges.

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2025 Job Trends in Software Engineering and AI

A comprehensive report analyzing the 2025 U.S. job market for software developers, software engineers, software architects, data engineers, and data scientists. This analysis covers salary trends, job growth projections, in-demand skills, remote work patterns, and hiring challenges. Each role is examined individually, with relevant groupings to highlight industry-wide patterns.

2025 U.S. Job Market Report: Software & Data Roles

Overview: The tech job market in 2025 remains robust for key software and data roles. Software developers, software engineers, and software architects continue to see strong demand alongside data engineers and data scientists. These professions offer high salaries (often well into six figures) and are projected to grow much faster than the average U.S. occupation. Industry experts note that the market has stabilized after recent volatility, with tech unemployment around 2%. Emerging technologies like artificial intelligence (AI), cloud computing, and big data are reshaping required skill sets and driving demand. Remote work remains a key factor, and many tech professionals work remotely or in hybrid arrangements, even as some companies encourage a return to office. Below, we break down the 2025 trends for each role in terms of salary, growth, skills, remote work, and hiring challenges.

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Architecture and Strategy for Business Value

Architecture and Strategy for Business Value

Modern IT Infrastructure Management: Architecture and Strategy for Business Value

In today’s rapidly evolving technological landscape, IT Infrastructure Management (ITIM) has undergone a profound transformation. No longer just about maintaining operational systems, modern ITIM has become a strategic business enabler. It combines cloud services, edge computing, on-premises systems, and platform services into an integrated ecosystem aligned with business objectives and value streams.

The Shifting Focus of Infrastructure Architecture

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Defining Modern IT Infrastructure The Evolving Lan

Defining Modern IT Infrastructure: The Evolving Landscape

As organizations grapple with increasingly complex infrastructure requirements, the need for a clear, comprehensive understanding of modern IT infrastructure has never been more critical. This ever-evolving landscape demands not just technical expertise, but a strategic mindset that can navigate the intersections of business needs, technological capabilities, and sustainability imperatives. For leaders charting a course through this complexity, establishing a solid definitional foundation is the first crucial step.

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Implementing Retrieval-Augmented Generation (RAG)

The Power of Contextual AI: Enhancing Foundation Models with External Knowledge

Imagine a student taking an exam. Limited to what they’ve memorized, their answers might be incomplete or inaccurate. Now picture that same student with access to their textbooks and notes. They can verify facts, make detailed connections, and develop deeper insights. This is the perfect analogy for Retrieval-Augmented Generation (RAG).

![ChatGPT Image Apr 22, 2025, 06_00_35 PM.png](/images/implementing-retrieval-augmented-generation-rag/Implementing Retrieval-Augmented Generation (RAG)%20%201ddd6bbdbbea80ab97cbc6461ed249d6/ChatGPT_Image_Apr_22_2025_06_00_35_PM.png)

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A Deeper Dive When the Vibe Dies Comparing Codebas

Comparing Codebase Architectures for AI Tools

As AI coding tools become more prevalent in software development, choosing the right architecture can significantly impact both development efficiency and AI collaboration. This article explores three prominent architectural approaches and their implications for AI-assisted development.

Let’s examine these architectures in detail. We’ll analyze how each one uniquely positions itself to handle AI-assisted development workflows. We’ll also explore what trade-offs developers need to consider when making architectural decisions. This is a continuation of this vibe article.

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The Evolving Data Landscape and Architectural Impe

The Evolving Data Landscape and Architectural Imperatives

Just as a 1920s city planner could not anticipate self-driving cars, today’s technical leaders face the challenge of designing data architectures for an uncertain future. Traditional data warehouses struggle to keep pace with exploding data sources and growing AI demands, forcing us to fundamentally rethink our approach to data management. This article explores not just what modern data architecture is, but why it’s crucial for business success in today’s rapidly evolving landscape.

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The Rise of Container-Native Workflow Orchestratio

Modern data engineering requires modern solutions. As data volumes explode and real-time processing becomes essential, traditional pipelines are reaching their limits. Enter container-native workflow orchestration with the Argo Project—a revolutionary approach to managing data flows in the cloud-native era.

The Data Deluge Challenge

Today’s businesses face an unprecedented challenge: the sheer volume, velocity, and variety of data is growing exponentially. Every online purchase, IoT interaction, and app usage generates data that requires near real-time processing to provide meaningful insights. Traditional data pipeline architectures—often monolithic, batch-oriented, and manually managed—simply cannot keep pace with these demands.

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Data Governance Turning Information into Business

In today’s data-driven world, effective data governance isn’t just a technical necessity—it’s a business advantage.Organizations that treat data as a strategic asset rather than just an IT concern are seeing measurable returns on their investment. This article explores how robust data governance drives profitability, reduces risk, and enhances business agility through practical frameworks and real-world examples.


Why Data Governance Matters to Your Bottom Line

Unlike finite resources, data grows in value when properly managed. Modern data governance provides your information is accurate, consistent, secure, and available for real-time decision making. This foundation enables:

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Setting up Claude Filesystem MCP

Setting up Claude Filesystem MCP

The Model Context Protocol (MCP) is a big deal in artificial intelligence. It was introduced on November 25th, 2024, and it’s like a universal connector for AI systems. Before MCP, AI assistants were like chefs with only one ingredient - their own capabilities. But now, with MCP, AI assistants have a “fully stocked pantry” of information to work with. This means they can do more and better things for us.

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Rick and Chris Review Machine Learning

Trail Talk: Rick and Chris Review Machine Learning

On a crisp Saturday morning, Rick and Chris were hiking up a favorite mountain trail, the sun casting a golden glow through the trees. Backpacks strapped on and water bottles filled, they set a steady pace up the incline.

Rick: adjusting his backpack straps

“Chris, you’ve been diving deep into machine learning lately. I keep hearing terms like supervised and unsupervised learning, but I’m a bit fuzzy on what they actually mean.”

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Streamlit Adventures Part 5

Streamlit Adventures Part 5

**Building a Real-Time File Monitor with Streamlit*A Tale of Synchronization, Queues, and Friendly BanterOn a sunny afternoon in Austin, Texas, Rick and Chris were lounging at their favorite coffee shop, laptops open, cups of coffee steaming. Their latest project, Meeting Buddy, was giving them a bit of a headache.***Rick:**Sipping his coffee “You know, Chris, the file drop synchronization just is not working as expected. The UI is not updating when new files are added.”Chris:“Yeah, I noticed that. It is like the UI is oblivious to the new markdown files we generate during meetings.”Rick:“Exactly! We need a way to have the UI respond in real-time as files are added or removed from the directory.”**Chris:**Grinning “Sounds like a job for the watchdog library and a bit of Streamlit magic!”Rick:“Agreed. Let us break it down and build a simple prototype that listens to a directory and updates the UI accordingly.”

They clinked their coffee mugs together, ready to embark on another coding adventure.

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The Kafka Ecosystem

This article appeared on LinkedIn on Feb 24th, 2018.

The Kafka Ecosystem - Kafka Core, Kafka Streams, Kafka Connect, Kafka REST Proxy, and the Schema Registry

Rick HightowerEngineering Consultant focused on AI

February 24, 2018

The Kafka ecosystem consists of Kafka Core, Kafka Streams, Kafka Connect, Kafka REST Proxy, and the Schema Registry. Most of the additional pieces of the Kafka ecosystem comes from Confluent and is not part of Apache.

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Is JParse Fast

This article originally appeared on LinkedIn on Feb 19th, 2024 by Rick Hightower

JParse: The most efficient JSON parser for the JVM yet!

Rick Hightower Engineering Consultant focused on AI

February 19, 2023

JParse

JParse, is the most efficient JSON parser for the JVM yet.

Why JParse?

JParse is the most efficient JSON parser for the JVM yet - it uses an index overlay to deliver lightning-fast parsing speeds.

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Using ChatGPT Chat Function Calls from Java

This article originally appeared on LinkedIn.

Title: Using ChatGPT Chat Function Calls from Java

Author: Rick Hightower

Original Publication Date: July 9, 2023

Using ChatGPT Chat Function Calls from Java

Introduction

As artificial intelligence and chatbots become more popular, it is increasingly important to integrate functions into chat conversations. Functions are small pieces of code that can be reused and embedded into larger programs to perform a specific task. In this blog post, we will discuss how to implement and integrate functions into ChatGPT conversations using JAI, a Java OpenAI API client. This guide will cover how to define a function, handle function callbacks, and mix function results with the content and context returned from the function. We will also provide an example of a weather-related function and its integration into a larger program using a function map.

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