29 articles
This guide explains the key differences between Large Language Models and broader Artificial Intelligence, helping organizations choose the right technology for their use cases.
This guide provides a strategic framework for implementing generative AI in enterprises.
This article explains the distinction between data engineers who build and maintain data infrastructure, and data scientists who analyze data to extract insights and drive decisions.
This guide provides a practical framework for AI adoption across industries, covering tools, platforms, and applications for data leaders and technical teams.
MLOps extends DevOps to machine learning by managing code, data, and models with Continuous Training to handle model decay.
This guide evaluates modern data warehouse tools and discusses how lakehouse architectures have become the standard for organizations needing both analytics and AI capabilities.
Operational databases (OLTP) process real-time transactions with ACID guarantees and concurrency.
This guide outlines the complete development lifecycle for building production-grade AI applications, from planning through deployment and monitoring.
IoT in manufacturing has evolved from pilots to operational backbone, using sensor networks and data platforms to optimize production and prevent failures.
This article discusses personalization strategies for media companies.
This article explains the NIST AI Risk Management Framework.
This guide provides a comprehensive framework for adapting large language models to specific tasks through fine tuning, addressing key decisions from data preparation to deployment.
AI data transformation automates converting raw data into structured formats for analytics and AI systems.
Transactional databases are designed to handle large volumes of short, real-time read/write operations that ensure data accuracy, completeness and safety for business-critical activities.
pgvector is a PostgreSQL extension that enables vector embedding storage and semantic search within existing databases without requiring separate vector systems.
Agentic analytics is an approach where autonomous AI agents continuously monitor data streams, detect patterns and anomalies, and generate insights with minimal human intervention, triggering automated actions and recommendations.
This article explains what a cloud-based database management system (DBMS) is, how it works, and the types of cloud databases available.
This article explains what analytic applications are, how they work, and how they differ from traditional BI tools.
This article introduces Intelligent Document Processing (IDP), an AI-powered technology for extracting and processing information from documents like PDFs, emails, and forms.
This article explains the Relational Data Model, its core components, and its role in modern RDBMS.
