DataStack Daily

Slow queries are the most common thing that makes a data engineer look bad in a meeting. Someone runs a dashboard, the spinner turns for 20 seconds, and the Slack message hits your DMs before the query finishes. This guide covers the five fixes that resolve 80% of production slowdowns—each with a concrete example you can run against your own warehouse. In this guide Fix 1: Missing (or wrong) indexes Fix 2: SELECT * on wide tables Fix 3: Nested subqueries vs CTEs vs JOINs Fix 4: Implicit type casting in JOINs Fix 5: Unanchored JOINs (cartesian products) FAQ Quick answer: If a query is slow and you can only check one thing, check the JOIN columns for indexes. A single missing index on a foreign-key column will turn an O(log n) lookup into a full table scan across millions of rows. Fix 1: Missing (or wrong) indexes An index is a sorted lookup structure. Without one on a JOIN or WHERE column, the database scans every row in the table. With one, it jumps dire...

Building a data dashboard is the last mile of the whole modern data stack — the point where clean, modeled data becomes something a person looks at and acts on. Skip the design step and you get a dashboard nobody opens. Skip the SQL foundation and you get a pretty chart hooked to wrong numbers. This guide walks through both halves: the query that feeds the dashboard, and the layout that makes it worth opening. In this guide The SQL foundation (get the numbers right first) Pick the right chart for the question Layout principles (above the fold) The four-chart dashboard that covers 80% of use cases Dashboard traps to avoid FAQ Quick answer: A dashboard lives or dies on two things: (1) a data model that answers one clear question per chart, and (2) a layout where the most important number is in the top-left corner. Start with one question, one chart, and one person who will use it. Add more only after the first one is actually opened. The SQL foundation (get...

A star schema is the data-modeling pattern you’ll hit the moment you build your first real dashboard, and it’s far simpler than the name suggests. It organizes your tables into one central table of measurements surrounded by tables of context — shaped, when you draw it, like a star. This guide is for analysts and beginners who want facts, dimensions, and grain to finally make sense, with one worked sales example. In this guide Why analytics needs a schema pattern Fact tables: the measurements Dimension tables: the context A worked sales example Star vs snowflake Grain: the mistake beginners make How this powers fast dashboards FAQ Quick answer: A star schema organizes analytics tables into one central ‘fact’ table of measurements (like sales) surrounded by ‘dimension’ tables of context (like product, customer, and date). It makes reporting queries simple to write and fast to run. Why analytics needs a schema pattern Databases...

An analytics engineer is the person in the data team who owns the transformation layer — the clean, tested SQL models between raw ingested data and the dashboards analysts use. This guide covers what the job involves, how it differs from data engineering and analysis, and whether it’s right for you. In this guide What an analytics engineer is Analytics engineer vs data engineer vs data analyst What an analytics engineer does day to day The skills and tools you need A day in the life: tracing a broken revenue number How to become an analytics engineer Is analytics engineering a good career? Frequently asked questions Quick answer: An analytics engineer owns the transformation layer in the modern data stack — the dbt models, tests, and metric definitions that sit between raw data and the dashboards teams use. The role emerged with dbt and cloud warehouses, and fits between the data engineer (pipelines & infrastructure) and t...