POWER QUERY BEST PRACTICES

• ‍Get Organized before Loading and Connecting Data: Set up an organized structure before importing data. Use clear, intuitive names for your tables and queries from the start. If you're working with local flat files, establish a consistent file and folder structure to avoid issues with file names or paths later on.

PRO TIPS:
      Power BI Desktop offers several storage and connection modes, each suited to different  data scenarios:

• Import: Data is loaded into memory and stored within Power BI. Queries are executed using cached data, offering fast performance (this is the default mode).

Import Mode is ideal for datasets under 1GB (after compression) that require fast performance and where the source data does not change frequently. This mode provides full flexibility, allowing unrestricted use of Power Query, data modeling, and DAX functions, making it a powerful choice for static or infrequently updated data.

• DirectQuery: Data remains in the source system, with queries executed in real time at the data source. This mode provides live access to the data but may result in slower query performance depending on the source.

DirectQuery is suitable for situations where the dataset is too large to fit in memory, the source data changes frequently and needs to be reflected in real-time, or company policies require data to be accessed directly from the source without importing it into Power BI.

• Composite Model (Dual): A hybrid mode that allows tables to come from both Import and DirectQuery sources, or integrates multiple Direct Query tables, providing flexibility in how data is accessed and managed.

The Composite Model allows you to enhance performance by choosing the optimal storage mode for each table. You can blend DirectQuery with imported data or combine multiple DirectQuery models into a single unified model, offering flexibility in how data is managed and accessed.

• Live Connection: Connects directly to published datasets in the Power BI Service or Azure Analysis Services, enabling you to leverage existing data models without importing the data.   

Live Connection enables the creation of a single central dataset as the authoritative source of truth. This setup allows multiple analysts to generate different reports from the same data and supports collaborative workflows, where one team member builds the model and another focuses on visualizations.

• Disable Refresh for Static Data Sources
  For data sources that don't change, such as lookup tables or reference data, there's no need to set up automatic refresh. 

Disabling refresh for these static sources can help optimize performance and reduce unnecessary load times.

• Load Only the Data You Need for Large Tables
  When working with large datasets, filter the data at the source to include only what’s necessary. For example, if you only need daily data, don’t load hourly data; if you only need product-level summaries, avoid loading transaction-level detail. Loading excessive data can significantly slow down report performance.

DATA MODEL BEST PRACTICES

• Start with a Well-Normalized Data Model: From the outset, focus on building a normalized data model. Ensure each table has a clear, distinct role and that relationships between them are leveraged effectively.

• Place Dimension Tables Above Fact Tables: In your model, position dimension tables above fact tables. This layout serves as a visual cue that filters naturally flow downstream from dimensions to facts.

• Keep Relationships Simple: Minimize the use of complex relationships in your model. Prefer straightforward 1-to-many relationships with one-way filters whenever possible, as they are easier to manage and improve performance.

• Avoid complex relationships unless absolutely necessary: Aim to use 1-to-many table relationships and one-way filters whenever possible

• Hide Unnecessary Fields from the Report View: Hide fields that are not needed for reporting to avoid creating invalid filter contexts. This encourages users to apply filters using primary keys from dimension tables, ensuring accurate and meaningful analysis.

DAX BEST PRACTICES

When to Use Calculated Columns vs. Measures

Use calculated columns for filtering: Calculated columns are useful when you need to create new columns for filtering, categorizing, or establishing relationships in your data.

• Opt for Explicit Measures, Even for Simple Calculations:
  Explicit measures provide greater flexibility. They can be reused in multiple places and nested inside other measures, which enhances both performance and clarity.

• Use Fully-Qualified Column References in Measures:
  For better readability and to avoid confusion, always use fully-qualified column names (e.g., Table[Column]) in your DAX expressions. This distinguishes column references from measure references and makes your code easier to follow.

• Move Column Calculations "Upstream" When Possible:
  Where feasible, perform calculations in the data source or during data import (e.g., in Power Query). This approach reduces the workload on the model and improves report performance.

• Minimize the Use of "Expensive" Iterators:
  Be cautious with iterator functions (like SUMX, FILTER, etc.), especially on large datasets or in complex models. These functions can be computationally expensive and may impact performance.

Use measures for aggregating values: Measures are designed for calculations that involve aggregation (like sums, averages, counts) or that are context-dependent.

DATA VISUALIZATION BEST PRACTICES

• Prioritize Clarity and Simplicity
  "Perfection is not when there is nothing more to add, but when there is nothing left to take away." — Antoine de Saint-Exupéry. Aim to create clear, concise visuals that communicate effectively. Keep your design minimalistic and intuitive, removing any unnecessary complexity.

• Design with Clear Narratives and Intuitive User Experiences
  Leverage tools like bookmarks, drillthroughs, tooltips, and navigation buttons to guide users through your reports smoothly. The goal is to create a seamless experience that allows users to interact with data easily and understand the insights you’re presenting.

Ask Yourself Three Key Questions

• What type of data are you visualizing?
  Is it time-series, financial, geospatial, or hierarchical?

• What are you trying to communicate?
  Are you focusing on comparison, composition, relationships, or distribution?

• Who is the end user?
  Are your reports aimed at analysts, managers, or executives?

• Optimize for Mobile Viewers
  If your reports will be viewed on mobile devices, ensure they are optimized for smaller screens. Use custom mobile layouts when publishing reports to the Power BI Service or Power BI app to provide an optimal experience for mobile users.

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