. A part of me started this journey because data engineering is one of the hottest and highest-paying careers right now. I’m not going to pretend that wasn’t a factor. But there’s more to it than that. I’ve been learning data analytics for a while now. SQL, Power BI, Python (Pandas, NumPy, a little Polars), …
From Data Analyst to Data Engineer: My 12-Month Self-Study Roadmap Read More »
# Introduction TurboQuant is a novel algorithmic suite and library recently launched by Google. Its goal is to apply advanced quantization and compression to large language models (LLMs) and vector search engines — indispensable elements of retrieval-augmented generation (RAG) systems — to improve their efficiency drastically. TurboQuant has been shown to successfully reduce cache memory …
TurboQuant: Is the Compression and Performance Worth the Hype? Read More »
I’ve seen a lot when I’m working with enterprise AI teams: they nearly always blame the model when something goes wrong. This is understandable, but it’s also frequently incorrect, and it ends up being quite costly. The usual scenario is as follows. The outputs are inconsistent; when someone raises it, the first reaction is to …
The Next AI Bottleneck Isn’t the Model: It’s the Inference System Read More »
# Introduction The world of data science moves fast. If you are just starting your journey in 2026, you might feel like you’re trying to drink from a firehose. Between mastering Python, understanding cloud computing, and keeping up with the latest machine learning models, it is a lot to handle. But there’s a new trend …
How AI Agents Will Transform Data Science Work in 2026 Read More »
I in my previous article, “From Code to Insights: Software Engineering Best Practices for Data Analysts”, that engineering skills and best practices can be incredibly useful for analysts and other data professionals. This is even more true now in the AI era, when we have far more opportunities to build our own analytical tools: from …
# Introduction Large language models (LLMs) have a taste for using “flowery”, sometimes overly verbose language in their responses. Ask a simple question, and chances are you may get flooded with paragraphs of overly detailed, enthusiastic, and complex prose. This usual behavior is rooted in their training, as they are optimized to be as helpful …
Guardrails for LLMs: Measuring AI ‘Hallucination’ and Verbosity Read More »
any time in the data engineering world, you’ve likely encountered this debate at least once. Maybe twice. Ok, probably a dozen times😉 “Should we process our data in batches or in real-time?” And if you’re anything like me, you’ve noticed that the answer usually starts with: “Well, it depends…” Which is true. It does depend. But “it …
Batch or Stream? The Eternal Data Processing Dilemma Read More »
# Introduction JSON is great for APIs, storage, and application logic. But inside large language model (LLM) pipelines, it often carries a lot of token overhead that does not add much value to the model: braces, quotes, commas, and repeated field names on every row. TOON, short for Token-Oriented Object Notation, is a newer format …
Stop Wasting Tokens: A Smarter Alternative to JSON for LLM Pipelines Read More »
(not that long ago) when being a data scientist meant living in a notebook, tweaking hyperparameters as if your life depended on it, and in a lot of cases, the whole project did, indeed, depend on it. Do you remember those overnight grid searches? Or building feature engineering pipelines that felt more like art than …
# Introduction Anyone who has spent a fair amount of time doing data science may sooner or later learn something: the golden rule of downstream machine learning modeling, known as garbage in, garbage out (GIGO). For example, feeding a linear regression model with highly collinear data, or running ANOVA tests on heteroscedastic variances, is the …
