2pk03 over AI, ML, BigData and data processing

Artificial Intelligence (AI) has leapfrogged to the poster child of technological innovation, on track to transform industries in a scale similar to the Industrial Revolution of the 1800s. But in this case, as cutting-edge technology, AI presents its own unique challenge, exploiting our human behavior of "love to trust", we as humans face a challenge: AI hallucinations. This phenomenon, where AI models generate outputs that are factually incorrect, misleading, or entirely fabricated, raises complex questions about the reliability and trust of AI models and larger systems. The tendency for AI to hallucinate comes from several interrelated factors. Overfitting – a condition where models become overly specialized to their training data – can lead to confident but wildly inaccurate responses when presented with novel scenarios (Guo et al., 2017). Moreover, biases embedded within datasets shape the models' understanding of the world; if these datasets are flawed or unrepresen

ETL has been the traditional approach, where data is extracted, transformed, and then loaded into the target database. ELT flips this process - extracting data and loading it directly into the system, before transforming it. While ETL has been the go-to for many years, ELT is emerging as the preferred choice for modern data pipelines. This is largely due to ELT's speed, scalability, and suitability for large, diverse datasets generated by multiple different tools and systems, think about CRM, ERP datasets, log files, edge computing or IoT. List goes on, of course.. Data Engineering Landscape Data engineering is the new kind of DevOps. With the exponential growth in data volume and sources, the need for efficient and scalable data pipelines and therefore data engineers has become the new standard . In the past, limitations in compute power, storage capacity, and network bandwidth made the famous 3-word "let's move data round" phrase Extract, Transform, Load (ETL) the