Large language models have transformed how we interact with AI, but their impressive capabilities rest on two fundamental processes that most users never see: tokenization and embeddings. Understanding tokenization and embeddings in LLMs is essential for anyone working with these systems, whether you’re optimizing API costs, debugging unexpected behavior, or building applications that leverage language … Read more
Connecting large language models with databases unlocks transformative capabilities that pure LLM interactions cannot achieve. While LLMs excel at understanding natural language and generating coherent responses, they lack access to your organization’s proprietary data, real-time information, and structured records. Learning how to connect LLM with a database bridges this gap, enabling applications that combine conversational … Read more
Large language models have evolved from passive question-answering systems into active problem-solvers that can plan, use tools, and pursue goals with increasing autonomy. This shift from reactive to proactive AI represents one of the most significant developments in artificial intelligence—the emergence of agentic LLMs. While traditional language models simply respond to prompts, agentic LLMs break … Read more
The debate between ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) has evolved significantly with the emergence of distributed SQL databases like CockroachDB. Traditional wisdom held that data warehouses were for ELT while operational databases required ETL, but CockroachDB’s unique architecture—combining transactional capabilities with analytical performance and horizontal scalability—blurs these boundaries. Organizations building modern … Read more
The artificial intelligence landscape has been dominated by headlines about ever-larger language models—GPT-4 with its rumored trillion parameters, Claude with its massive context windows, and Google’s PaLM pushing the boundaries of scale. Yet a quieter revolution is happening in parallel: small language models (SLMs) with just 1-10 billion parameters are proving remarkably capable for specific … Read more
The evolution from traditional machine learning systems to agentic AI represents a fundamental shift in how we design intelligent systems. While conventional ML architectures treat models as static components that process inputs and return outputs, agentic AI systems exhibit autonomous behavior—making decisions, taking actions, and adapting their strategies based on environmental feedback. The challenge lies … Read more
Change Data Capture (CDC) has become essential for modern data architectures, enabling real-time analytics, audit trails, and downstream system synchronization. While traditional CDC solutions require managing complex infrastructure—database servers, streaming platforms, and processing clusters—AWS Lambda and Kinesis Firehose offer a fully serverless alternative that scales automatically, requires no infrastructure management, and costs nothing when idle. … Read more
Modern applications generate data continuously, and the ability to process this data in real-time has become a competitive necessity rather than a luxury. Whether you’re building fraud detection systems, personalizing user experiences, or maintaining up-to-date analytics dashboards, traditional batch ETL processes that run overnight no longer meet business requirements. AWS Database Migration Service (DMS) combined … Read more
In an era where data drives innovation across every field—from medical research to climate science to machine learning—finding the right datasets remains surprisingly difficult. Researchers often spend weeks searching through institutional repositories, government databases, and university websites, piecing together information scattered across thousands of sources. Google Dataset Search emerged to solve this fundamental problem: making … Read more
Change data capture has become essential for modern data architectures that demand real-time synchronization between operational databases and analytics platforms. Debezium excels at capturing database changes with minimal latency, while AWS Kinesis provides scalable, reliable streaming infrastructure. Integrating these technologies creates a powerful pipeline for propagating database updates across distributed systems with millisecond-level latency. The … Read more
