Day 26: Power BI vs Other Tools

Power BI: lower cost (free Desktop, Pro ~₹700/user/month), tighter Microsoft ecosystem integration (Excel, Teams, Azure, SQL Server), DAX for calculations, stronger data modelling capabilities. More common in Indian corporate environments (banking, IT services, manufacturing). Tableau: stronger visualisation flexibility and customisation, better for complex visual storytelling, LOD (Level of Detail) expressions vs DAX, more intuitive for non-technical users to build charts. Higher cost. More common in analytics-heavy global companies. For India's job market in 2026: Power BI has significantly more job openings — ~3x more Power BI roles than Tableau on Naukri and LinkedIn.

Use Power BI when: the dataset is over 100,000 rows (Excel slows down, Power BI handles millions), multiple stakeholders need to access the report (publish to Service), data refreshes automatically from a database or API, you need interactive filtering and cross-visual drilling that is hard in Excel, or you need RLS for user-specific data access. Use Excel when: the analysis is ad hoc and one-time, stakeholders need to edit the data themselves, the dataset is small and simple, or the deliverable is a formatted document rather than an interactive report. Many professional analysts use both — Excel for exploration and ad hoc, Power BI for production dashboards.

Looker Studio: free, browser-based, strong Google ecosystem integration (GA4, BigQuery, Google Sheets, Google Ads), good for digital marketing analytics. Limited data modelling — no in-memory engine like VertiPaq. Calculations are basic compared to DAX. Power BI: richer data modelling, DAX for complex calculations, better for enterprise finance/HR/sales dashboards, requires Windows for Desktop. In India's job market: Power BI is far more common in corporate roles. Looker Studio is used primarily at digital agencies, startups, and companies in the Google ecosystem. If you know both, mention it — it is a differentiator.

SQL: the most common data source for Power BI. Writing SQL queries to extract the right data before it reaches Power BI (pushing transformation to the database rather than doing it in Power Query or DAX) improves performance significantly. Power BI's native SQL connector lets you write custom SQL queries instead of importing entire tables. Python: Power BI supports Python scripts as data sources (Get Data → Python Script) and as visuals (Python Script Visual). Use Python for statistical analysis (scipy, scikit-learn), custom visualisations (Matplotlib, seaborn), or data access to APIs that Power BI connectors do not support. Python visuals are static images and do not support interactivity.

Power BI Embedded is an Azure service that lets developers embed Power BI reports and dashboards into custom applications, websites, or portals — without requiring users to have Power BI accounts. The embedding application handles authentication; users see the report as part of the app without knowing Power BI is behind it. Use cases: embedding sales dashboards in a CRM, showing analytics in a customer-facing SaaS product, building an internal analytics portal. Licensing: Power BI Embedded uses capacity-based Azure SKUs (A1-A8) rather than per-user licensing. Relevant for: software product companies building analytics features into their product.