Guides

Data Integration Platform – Must Have Features In Gen AI Era

Modern applications increasingly rely on unified data access to high-quality, ready-to-use datasets. Gen AI has opened up new frontiers in…

Data Synchronization – Best Practices In the Gen AI Era

Data synchronization is the process of keeping data consistent across multiple systems. It maintains data coherence in distributed environments involving…

API Data Integration – Key Factors While Choosing a Platform

API data has become particularly relevant as more organizations use data feeds from third-party services to implement their Gen AI…

ETL Tools—Key Features to Consider in The Post-AI Era

Data no longer exists only in traditional databases but throughout cloud services, APIs, and streaming platforms, among others. Conventional ETL…

Data Integration Tools—How to Choose the Best One?

Data integration is emerging as a core competency for enterprises that need to unify and leverage data from multiple sources….

ETL vs. ELT—Key Differences, Improvements, and Trends

Traditionally, data integration and analytics were accomplished through ETL, which stands for Extract, Transform, and Load. This process involved extracting…

Data Integration Techniques—the Past, Present, and Future

Data integration is critical to modern data engineering, especially in AI and machine learning applications. As organizations increasingly rely on…

LLM Fine-Tuning—Overview with Code Example

Large language models are everywhere. Nearly every day, a language model is released publicly. However, if you’ve worked with LLMs…

Low-rank Adaptation of Large Language Models—Implementation Guide

Low-rank adaptation, or LoRA, is an advanced fine-tuning technique designed to reduce the number of trainable parameters in large language…