DataStack Daily

Slowly changing dimensions are what every data engineer eventually runs into: the moment you realize your dimension table only stores today’s values and someone is asking about the past. Understanding SCD types comes down to one design decision you make every time a dimension attribute changes. In this guide What a slowly changing dimension is The core problem: overwrite or keep history? SCD Type 1: overwrite the old value SCD Type 2: add a new row with validity dates SCD Type 3: add a column for the previous value Type 1 vs 2 vs 3 side by side How to implement it (and common pitfalls) FAQ Quick answer: A slowly changing dimension is any dimension attribute that drifts over time — customer city, product category, sales region. Type 1 overwrites in place (no history). Type 2 adds a new row with validity dates — the workhorse. Type 3 adds a previous-value column (one transition only). Most teams need Type 2 more than they expec...

The modern data stack is the set of cloud tools teams use to move data from where it’s created to where people can actually use it — dashboards, reports, and models. If you’re new to data and the jargon feels like alphabet soup, this guide is the map. We’ll walk each layer in plain English, follow one order all the way from a store database to a dashboard, and point you to where to start. In this guide What 'the modern data stack' actually means The five layers, from source to dashboard Ingestion: getting data in Storage: the warehouse or lakehouse Transformation: turning raw into useful BI and activation: where people see it How the pieces fit together (a worked example) Where to start if you're new FAQ Quick answer: The modern data stack is a set of specialized cloud tools — ingestion, storage, transformation, and BI — that together turn scattered raw data into trustworthy numbers people can query. Instead of one big monolith, each...

If you’re asking how to become a data engineer, here’s the blunt version: no hiring manager reads a certificate and thinks “this person can fix a broken pipeline.” They hire for one thing — can you move data from a source into a warehouse, transform it into something trustworthy, and keep it running? Everything here builds toward one end-to-end portfolio project that answers it directly. In this guide What a data engineer actually does The core skills you need in 2026 A realistic learning path, in order Build the portfolio project that proves you can do the job Certifications: do they matter? The job market and pay landscape Your first 90 days FAQ Quick answer: A data engineer builds and maintains the pipelines that move raw data from sources into a warehouse and shapes it into tables analysts can trust. To break in: learn SQL, then Python, then load data into a cloud warehouse, transform it with dbt, and orchestrat...

‘ETL vs ELT’ is one of the most common interview questions in data — and one of the most over-complicated. The difference comes down to a single letter’s worth of ordering: do you transform data before you load it, or after? This guide is for beginners who want the real distinction, the reason the default flipped, and a straight answer on which to choose. In this guide ETL and ELT in plain terms The one difference that matters: order of operations Why cloud warehouses changed the default Trade-offs side by side When ETL still wins When ELT is the better call What most teams do in 2026 FAQ Quick answer: ETL transforms data before loading it into the warehouse; ELT loads raw data first and transforms it inside the warehouse. Same three steps — extract, transform, load — in a different order, and today ELT is the more common default for cloud analytics. ETL and ELT in plain terms Both terms describe the same three jobs: Extract data f...

If you’re looking for a dbt for beginners guide that skips the marketing and gets to what the tool actually does, this is it. dbt — data build tool — became the default way analytics teams transform data inside a warehouse not by being clever, but by bringing three habits software engineers already had (version control, testing, documentation) to SQL analysts writing queries in isolation. If you know SQL, most of dbt will feel familiar within an hour. In this guide What dbt actually is (and is not) How dbt works: models are just SELECT statements The three ideas that made dbt win: ref(), tests, docs Worked example: from raw to a clean model dbt Core vs dbt Cloud Where dbt fits in your stack (and where it doesn’t) Common beginner pitfalls and how to start FAQ Quick answer: dbt is a transformation tool for SQL analysts. You write a .sql file containing one SELECT statement; dbt compiles it and sends it to your warehouse to...

‘Data warehouse vs data lake’ trips up almost everyone new to the field, partly because people use the two words as if they mean the same thing. They don’t. A warehouse stores clean, structured tables ready for analysis; a lake stores raw files of any shape for later. This guide explains both (plus the newer lakehouse), who uses each, and how to pick — in plain English. In this guide The three terms, defined Structured vs unstructured: the core split The warehouse: fast, clean, pricey The lake: cheap, raw, flexible The lakehouse: the merge A comparison table Which one you actually need FAQ Quick answer: A data warehouse stores structured, cleaned data optimized for fast SQL analytics; a data lake stores raw data of any type cheaply for flexible, later use. A lakehouse combines the two — lake-style storage with warehouse-style querying layered on top. The three terms, defined The data warehouse vs data lake question is really about how mu...