PowerBI

Difference import vs directquery in power bi

Mar 22, 2024

Difference import vs directquery in power bi

Understanding Import and DirectQuery Modes in Power BI

Power BI empowers users to analyze data from various sources. This post dives into two key connection modes: Import and DirectQuery. Each offers distinct advantages depending on your data analysis needs.

Import Mode: Power and Flexibility

Import mode brings your data directly into Power BI's internal memory. This creates a copy of the data, allowing for:

Faster Performance: Since the information is readily available, visualizations and calculations happen swiftly.
Enhanced Data Manipulation: Transform and mold the data to your liking before loading, offering greater control compared to DirectQuery.
Offline Accessibility: Reports built with import mode function flawlessly even without an internet connection.

However, there are limitations to consider:

Resource Demands: Importing large datasets strains system resources like RAM and disk space.
Data Refresh: Changes made to the source data won't be reflected until you refresh the import. Setting up automatic refreshes can help, but large datasets can lead to slow refresh times.

DirectQuery Mode: Real-Time Insights

DirectQuery mode bypasses internal storage. Instead, it sends queries directly to the original data source. This approach offers several benefits:

Real-Time Analysis: Always see the latest data without manual refreshes. Changes in the source database are instantly reflected in your reports.
Large Dataset Efficiency: DirectQuery handles massive datasets effectively, avoiding memory constraints encountered in Import mode.
Guaranteed Accuracy: Reports always showcase the most up-to-date information.

However, DirectQuery comes with its own limitations:

Limited Functionality: Certain features like calculated columns and complex data models are restricted due to the reliance on live data.
Potential Performance Lag: Queries travel back and forth between Power BI and the source system, impacting response times compared to Import mode.

Let’s take a look on how Import & Direct Query modes works

One of the main advantages of using Power BI is its ability to import data from various online sources. To import data from your database directly into your Power BI reports and dashboards, you need to connect to the database first.
Here are the steps to follow:

Open Power BI and click on the “Get Data” button.
In the “Get Data” window, select the “Database” option.
Choose SQL Server option.
Enter the server name and credentials to connect to the database.
Select the specific tables or views you want to import data from.
Click on the “Load” button to import the data into Power BI or you can Transform the data if any necessary transformations or filters you wants to apply.

Setting Up a DirectQuery Connection

Except for the import mode prompt, the steps for configuring a DirectQuery connection will remain the same.
Choose the DirectQuery option when prompted for the import mode.

Choosing the Right Mode

The optimal mode hinges on your specific needs:

Import Mode: When speed, offline access, and intricate data manipulation are paramount, Import mode shines. It fosters a responsive environment for in-depth analysis, ideal for creating reports and dashboards that can be explored without an internet connection. This mode is particularly advantageous for smaller to medium-sized datasets, where refresh times are manageable.
DirectQuery Mode: This mode is your go-to for real-time insights. It ensures you're always basing your decisions on the freshest data available, minimizing the risk of outdated information influencing critical choices. For very large datasets, DirectQuery eliminates the memory limitations of Import mode, making it a powerful tool for handling massive volumes of information.

By understanding the strengths and weaknesses of each mode, you can leverage Power BI effectively to make informed decisions based on your unique data analysis requirements.

TAGS PowerBI Data-Analysis

EntityFramework

Building Dynamic Web Applications with Entity Framework Core in ASP.NET Core

Pruthvi Gohil

Jun 24, 2024

Introduction First we need to understand why we use ORM tools instead of manually managing data access. Manually managing data access involves writing code to interact with the database directly using languages like SQL. This approach can lead to several challenges: Boilerplate Code: You need to write repetitive code for common operations like connecting to the database, executing queries, and processing results. This can be time-consuming and error-prone. Error Handling: Manual error handling for database interactions is complex and requires careful checking for potential issues like SQL injection attacks or data type mismatches. Data Model Mapping: You need to manually map data between your application objects and the database tables. This can be cumbersome and error-prone, especially for complex data models. ORM Tools as a Solution: Object-Relational Mapping (ORM) tools like Entity Framework Core (EF Core) address these challenges by providing a layer of abstraction between your application and the database. Reduced Boilerplate: ORMs automatically generate most of the code for data access tasks, freeing developers to focus on application logic. Simplified Error Handling: ORMs handle common errors and data type conversions, improving code reliability and security. Automatic Data Mapping: ORMs map your application objects (like classes) to database tables, reducing the need for manual data model manipulation. Improved Portability: Many ORMs support multiple database providers, allowing you to switch databases more easily. Enhanced Maintainability: Changes to the data model can be reflected in your object classes, simplifying code updates. Overall, ORMs like EF Core streamline data access in web applications, promoting faster development, better code maintainability, and reduced risk of errors. What is EF Core? : Entity Framework Core (EF Core) is a tool that simplifies working with databases in your .NET applications. It acts as a bridge between the object-oriented world of your application (think classes and properties) and the relational world of databases (tables and columns). Benefits of using EF Core for data access: Increased Developer Productivity: EF Core significantly reduces the amount of boilerplate code required for data access tasks. Developers can focus on building the core functionalities and business logic of the application instead of writing repetitive SQL queries. Automatic data model mapping eliminates the need for manual data manipulation between objects and tables. Improved Code Maintainability: By centralizing data access logic in EF Core, your code becomes cleaner and easier to understand. Changes to the data model can be reflected in your object classes, simplifying code updates. Type Safety and Compile-Time Checks: Defining your data model with classes and properties in EF Core enforces type safety, helping catch errors early in the development process. Support for LINQ: EF Core allows you to use LINQ (Language Integrated Query) expressions for querying data. LINQ syntax is similar to working with objects directly, making it easier to write and understand queries. Improved Code Readability: Separating data access logic from business logic leads to cleaner and more readable code. This makes it easier for other developers to understand and maintain the code base. Step-by-Step Guide with Code Examples This guide walks you through creating a basic Web API in ASP.NET Core that uses Entity Framework Core (EF Core) to interact with a SQL Server database. We'll build a simple blog example with functionalities for managing blog posts. Setting Up the Project: Open Visual Studio and create a new project. Select "ASP.NET Core Web API" as the project template and give it a suitable name (e.g., MyBlogApi). Choose a suitable .NET version and click "Create". Installing NuGet Packages: Navigate to "Manage NuGet Packages..." & Search for and install the following packages Microsoft.EntityFrameworkCore Microsoft.EntityFrameworkCore.SqlServer These packages provide the necessary functionalities for EF Core and its SQL Server provider. --

PowerBI

DAX Tips: Year-to-Date & Year-over-Year in Power BI

Pruthvi Gohil

Feb 29, 2024

Introduction to Power BI and Year-to-Date(YTD) & Year-over-Year(YoY) Calculations Power BI is a data visualization and business intelligence tool that allows users to connect to different data sources, transform data, and create insightful reports and dashboards. With Power BI, users can easily perform complex calculations such as YTD calculation, which provides a way to view data from the beginning of the year up to a given point in time. YoY growth is a change in a metric compared to the same period one year prior. There are several approaches to achieve YTD & YoY calculation using DAX in Power BI. Let's use one of the approach to accomplish that. What is Year-to-Date(YTD)? Imagine you’re in February, looking back at all the data from the beginning of the year (January 1st) until today. That’s YTD. It’s like a running total of your performance throughout the current year. How to Calculate Year-toDate(YTD)? Assume we have a calendar & sales table and having a column for sales amount. Now use DAX to develop a measure that computes the current year's YTD revenue. Previous Year-to-Date(PYTD): Now, rewind to the same day in the previous year. The data from January 1st of that year up to that day is PYTD. It’s your benchmark, a reference point to compare your current year’s progress. How to Calculate Previous Year-to-Date(PYTD)? Using SAMEPERIODLASTYEAR function we can get the same date of previous year. Year-over-Year(YoY) Growth: This is where things get exciting! YoY is the change between your current YTD and the PYTD for the same day. It tells you how much you’ve grown (or shrunk) compared to the same period last year. How to calculate YoY growth : Subtract PYTD(YTD Rev LY) from YTD Revenue(YTD Rev) : The DAX functions I utilized to get these calculations : LASTDATE(Dates) : Returns last non blank date STARTOFYEAR(Dates) : Returns the start of year SAMEPERIODLASTYEAR(Dates) : Returns a set of dates in the current selection from the previous year CALCULATE (Expression,Filter,Filter, …) : Evaluates an expression in a context modified by filters. DATESBETWEEN(Dates,StartDate,EndDate) : Returns the dates between two given dates. Conclusion : Calculating YTD and YOY growth in Power BI using DAX is a valuable technique for analyzing financial performance and identifying trends. Furthermore, it's important to note that this comprehensive approach leverages only pre-defined DAX functions. By understanding and practicing these versatile functions, you can unlock the ability to perform a wide range of complex calculations within Power BI, ultimately transforming your data into actionable insights.

About the Author

Pruthvi Gohil

I'm a Software Engineer, bringing over 2+ years of experience in building web applications. My expertise lies in full-stack development, leveraging technologies like C#, ASP.NET MVC, .NET, Web APIs, Entity Framework Core, SQL Server, and Azure DevOps. I'm also proficient in JavaScript, jQuery, and Java, offering a well-rounded skillset for tackling complex software challenges.