How much of a data analyst's day is spent writing SQL?

Most of it. On a typical day, 3 to 5 hours go into writing, testing, and fixing SQL queries. That's the bulk of the work, not a side skill. The queries aren't glamorous—you're pulling a table, checking if the numbers match what someone expects, joining it to another table, and formatting it so a non-technical person can read it.

Do data analysts use Python every day?

Not at the entry level, and often not later either. SQL and a spreadsheet tool cover most entry-level and mid-level analyst work. Python shows up more in data engineering, machine learning, or advanced analytics roles, not in the daily grind of a standard analyst seat.

What meetings does a data analyst attend?

A typical week includes a team standup, one or two stakeholder meetings where you present or explain a number, and the occasional call where someone wants to understand a dashboard they can't read on their own. The skill that matters most in these meetings is explaining a number in one sentence to someone who doesn't care how you got it.

Is being a data analyst boring?

Some of it, yes. Most days include stretches of repetitive query writing and data cleanup that nobody would call exciting. The interesting part shows up when a chart finally answers a question someone's been asking for weeks, or when you catch an error before it reaches a director's inbox.

What's the difference between a junior and senior analyst's day?

A junior analyst spends more time on execution: writing the queries, building the dashboard, fixing the data someone else flagged. A senior analyst spends more time deciding what should get built in the first place and pushing back when a request doesn't make sense. Both still write SQL most days. The senior version just spends less time on syntax and more time on judgment.

What tools does a data analyst use daily?

In order of frequency: SQL editor, a BI tool like Tableau or Looker, spreadsheets like Excel or Google Sheets, Slack or email, and Python (occasionally). SQL editor is used most, while Python ranks lower than many expect, especially at the entry level.

Data Modeling in Power BI Without Overthinking It

Data modeling in Power BI is the part most beginners skip past. They connect to data, drag fields onto a canvas, and get charts -- and it seems to work. Then the numbers start coming out wrong, and the reason is usually the model underneath.

Getting the model right is what makes everything else in Power BI reliable. It's also what separates reports that scale from reports that fall apart when you add another table.

The quick answer: start with a star schema
Facts versus dimensions
Why the star schema works
Relationships and how filters flow
The date table
Common mistakes
How the model connects to DAX
FAQ

The quick answer: start with a star schema

A star schema separates your data into fact tables (what happened -- sales, transactions, events) and dimension tables (context -- customers, products, dates, regions). The fact table sits in the center; the dimension tables connect to it in a star pattern.

This structure is what Power BI's calculation engine is designed around. Work with it and your measures will be simpler and more reliable. Work against it and you'll spend hours debugging numbers that don't match.

Facts versus dimensions

The distinction matters because they serve different roles in the model.

Fact tables contain the things you want to measure: sales amounts, quantities, costs, durations. Each row is usually a transaction or event. Fact tables tend to be long (many rows) and narrow (fewer columns). The columns you keep are numeric measures and foreign keys that link to dimension tables.

Dimension tables contain the context that describes or categorizes the facts: which customer, which product, which date, which store. Each row is usually a unique entity -- one row per customer, one row per product. Dimension tables tend to be shorter (fewer rows) and wider (more columns).

When you're deciding which is which, ask: do I sum or count this column, or do I use it to group and filter? If you sum or count it, it belongs in a fact table. If you group or filter by it, it belongs in a dimension table.

Why the star schema works

Power BI's DAX engine processes filters by following relationships from dimension tables into fact tables. When a user selects a product in a slicer, that filter travels from the product dimension into the fact table and narrows which rows get calculated.

Star schemas make this reliable because:

Each dimension connects to the fact table in one direction -- filters flow predictably
There's no ambiguity about which table is the "one" side and which is the "many" side
Measures written in DAX don't have to fight against unexpected filter paths
The model is easy to read and debug when something goes wrong

Relationships and how filters flow

A relationship in Power BI connects two tables on a matching column -- usually a key column that's unique in the dimension table and repeated in the fact table. The relationship has a direction, which determines where filters go.

In a standard star schema relationship:

The dimension table is on the "one" side (unique key)
The fact table is on the "many" side (repeated foreign key)
Filters flow from the dimension (one) to the fact (many) by default

This means when you filter on a dimension, it affects calculations on the fact table. That's what you want.

Cross-filter direction is where beginners get into trouble. Setting relationships to "both" directions makes filters flow in both directions, which can cause unexpected interactions and circular filter paths. Start with single-direction filters unless you have a specific reason to change it.

The date table

Time intelligence in DAX -- year-to-date, prior year comparison, rolling averages -- requires a dedicated date table. Power BI can auto-generate one, but building your own is better because it lets you control what columns exist and how fiscal periods are defined.

A date table should:

Cover the full range of dates in your data, without gaps
Have a unique date column used as the primary key
Include useful columns: year, month, month name, quarter, week number, day of week, fiscal period if relevant
Be marked as a Date Table in Power BI's table properties

Once marked, DAX time intelligence functions will use it correctly. Without a properly marked date table, functions like DATESYTD and SAMEPERIODLASTYEAR often produce incorrect results.

Common mistakes

The mistakes beginners make most often:

Leaving everything in one table. Flat tables with all data merged together seem convenient but break under the weight of complex DAX. Separate fact and dimension tables from the start.
Many-to-many relationships without understanding them. Power BI allows many-to-many relationships, but they require bridge tables and careful DAX to work correctly. Avoid them until you understand exactly why you need one.
Bidirectional filters everywhere. Single-direction filters are almost always what you want. Bidirectional filters should be rare exceptions.
No date table. Trying to use time intelligence without a proper date table leads to errors that are hard to diagnose.

How the model connects to DAX

DAX measures calculate in the context of the model's relationships and the filters active at the time of calculation. When you write a measure, you're writing instructions for how to aggregate numbers given whatever filter context exists.

A well-structured model makes DAX simpler. When the relationships are correct and the dimension tables are clean, most measures can be written with straightforward aggregation functions: SUM, AVERAGE, COUNT, DISTINCTCOUNT. The complexity comes in when you need to modify the filter context -- that's where functions like CALCULATE, ALL, and FILTER come in.

If you find yourself writing complex DAX to get a simple result, the model is often the actual problem. Fixing the model usually simplifies the DAX.

FAQ

Do I need a star schema for small datasets?
Not strictly. But even for small datasets, building the habit of separating facts and dimensions makes your reports more maintainable and makes the DAX easier to write. It's worth doing from the start.

What if my data source already has everything in one table?
Use Power Query to split it into dimension tables and a fact table before loading into the model. This is one of the primary uses of Power Query -- shaping data into the structure the model needs.

How do I know if my model is correct?
The most reliable check is building a few measures you can verify against a source you trust -- a spreadsheet, a report, a number your business already knows. If those measures match, the model is probably right. If they don't, start by checking the relationships and the filter directions.

Should I use the auto-generated date table?
It's fine for simple reports, but it won't have fiscal periods or custom columns, and it doesn't give you control over the date range. Building your own date table in Power Query takes about 15 minutes and is worth doing for anything beyond basic time intelligence.