Power BI supports real-time data streaming by connecting to streaming data sources, allowing for the creation of dashboards that update in real-time as data flows in.

Handle missing data by using Power Query to identify and fill gaps, apply data imputation techniques, or remove incomplete records to maintain data integrity.

DAX (Data Analysis Expressions) is a formula language in Power BI used to create calculated columns, measures, and custom tables, enabling complex calculations and data analysis.

Power Query is a data connection technology in Power BI that enables users to import, clean, and transform data from various sources, streamlining the data preparation process.

Power BI seamlessly integrates with Microsoft Excel, Azure, and SQL Server, allowing users to import, analyze, and visualize data from these platforms, enhancing the overall data analysis process.

Effective data modeling organizes data into structured formats, defines relationships, and optimizes performance, serving as the foundation for accurate and efficient data analysis in Power BI.

Power BI is a data visualization and business intelligence tool developed by Microsoft. In data science, it's used to create interactive dashboards and reports, enabling professionals to visualize and share insights from their datasets.

Power BI Free offers basic functionalities, while Power BI Pro provides advanced features like collaboration, data governance, and sharing capabilities, essential for organizational use.

Ensure data security by implementing row-level security (RLS), managing user roles, and setting appropriate permissions to control data access within Power BI.

Gateways act as bridges between Power BI and on-premises data sources, enabling secure data transfer for reports and dashboards.

Improve report performance by optimizing data models, reducing the number of visuals on a page, and limiting the use of high-cardinality columns to enhance loading times.

To optimize data models, use a star schema design, minimize the use of calculated columns, and ensure proper relationships between tables to enhance query performance.

Implement row-level security (RLS) by defining roles and rules within Power BI Desktop, then assigning users to these roles to control data access at the row level.

Create interactive dashboards by incorporating slicers, filters, and drill-through actions, allowing users to explore data dynamically and gain deeper insights.

Effective data preparation in Power BI involves cleaning data to remove duplicates and handle missing values, transforming data using Power Query, and ensuring consistent data types for accurate analysis.

Power BI supports big data analytics by connecting to large datasets, performing data transformations, and providing scalable visualizations, facilitating comprehensive analysis of vast data volumes.

Effective visualizations include bar charts for comparisons, line charts for trends, scatter plots for correlations, and maps for geographical data, aiding in comprehensive data analysis.

Custom visuals in Power BI are third-party or user-created visualization tools that go beyond standard charts and graphs. They enable enhanced data storytelling by offering more tailored and engaging ways to present data insights.

Calculated columns are new data columns created using DAX formulas, while measures are calculations performed on data during analysis, both enhancing the analytical capabilities of Power BI.

Schedule data refreshes by configuring refresh settings in the Power BI Service, allowing reports to update automatically at specified intervals.

Share reports by publishing them to the Power BI Service, setting appropriate permissions, and providing stakeholders with access links or embedding reports into other platforms.

Yes, Power BI can connect to various cloud-based data sources like Azure, Google Analytics, and Salesforce, facilitating seamless data integration and analysis.

Key best practices include creating a clear data model, using DAX measures instead of calculated columns, implementing row-level security, maintaining clean data sources, and using appropriate visuals to convey the story effectively.

Power BI supports predictive analytics by integrating with tools like Azure Machine Learning and using Python or R scripts. This allows data scientists to run predictive models and visualize the outcomes directly within Power BI.

Power BI Desktop is a Windows application for creating reports, while the Power BI Service is a cloud-based platform for sharing, collaborating, and managing reports and dashboards.