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#Important #Questions #PowerBI #SQL 1. Tell me about yourself? 2. How would you rate yourself in Power BI & SQL out of 10? 3. Are you aware of feature called RLS? 4. Is it possible to use RLS to limit the columns & measures? 5. What are the connectivity modes available in Power BI? 6. In Direct query can you use RLS features? 7. How Power BI get to know that all those data related information when we use direct query connectivity mode? 8. Are you aware of metadata? 9. List out some of the common table DAX functions? 10. Scenario: There is a data table having more than 4TB of data, you have used live connection or direct query mode. You imported he data and then after that you created a chart. When you are selecting any filter. For refreshing the whole chart, it will take 6 minutes of time and you wanted to reduce to 1 min then client ask you to reduce to 1 min. They told to optimise what are all the procedures you will take to optimise it and to reduce to 1sec 11. What is bidirectional cross filtering? 12. Different kind of filters have in Power BI? 13. Difference between Primary key & unique key? 14. Difference between UNION & UNIONALL? 15. Explain the types of joins with an example? 16. Can we put any condition in cross join? 17. Use cases of full outer join? 18. Can we use left join as a inner join?

For data pros: Speed up your work. Discover Power Query shortcuts! ⬇ Examples of Optimization Techniques 1️⃣ Minimize Data Load Only load essential columns and rows. For instance, if you're analyzing sales data, focus on relevant date ranges and specific product categories to avoid unnecessary data clutter. 2️⃣ Use Native Query Options Whenever possible, leverage Power Query’s ability to push operations back to the data source (like SQL databases). This reduces the data processing load on Power BI. 3️⃣ Avoid Unnecessary Calculations Perform calculations in the data source if possible. This means fewer transformations in Power Query and a lighter load for Power BI. 4️⃣ Merge Queries Judiciously While merging queries is powerful, it can be resource-intensive. Ensure you're merging only when necessary and with optimized join conditions. 5️⃣ Utilize Index Columns for Large Tables When working with large datasets, creating an index column can improve sorting and filtering efficiency. 6️⃣ Optimize Data Refresh Schedules Schedule data refreshes during off-peak hours to ensure efficient use of resources and reduce the impact on end-user experience. By implementing these techniques, you'll see a marked improvement in your Power BI reports' load times and overall performance! #PowerBI #PowerQuery #DataOptimization #DataAnalysis #BusinessIntelligence

How do you increase the performance of a direct query in #powerbi? 1)Ensure that your data source is properly indexed for faster query execution. 2)Leverage database-specific optimizations like views, indexed views, stored procedures, or indexed columns to enhance query performance. 3)Leverage query folding in Power Query to push back as many operations as possible to the data source. 4)Isolation levels determine how transactions interact with each other within a database. For some data sources, you might have the ability to set isolation levels for your queries. Choosing the appropriate isolation level can impact query performance and data consistency. Lower isolation levels like Read Uncommitted can improve query performance but might lead to less strict data consistency. 5)Some data sources support parallel query execution. Enabling parallel queries can improve performance by allowing Power BI to send multiple queries concurrently to fetch data, reducing overall query execution time. 6)Some optimizations might require adjustments at the data source level or using specific connectors that offer more granular control over these settings. do you think I have missed anything? Please add your approach to optimize a direct query.

Power BI Learnings: Day 12 📊 Import Query Mode vs. Direct Query Mode Summary! 🔍 Import Query Mode: 1️⃣ Data is imported and stored within the Power BI file. The data is cached in Power BI's internal data model. 2️⃣ Generally faster for end users because the data is pre-loaded and cached in memory. ⚡ 3️⃣ Data needs to be refreshed periodically, which can be scheduled manually using Power BI Service or Desktop. 🔄 4️⃣ Use Case: Ideal for small to moderate datasets where we need high performance and quick responsiveness in our reports. 📈 🔗 Direct Query Mode: 1️⃣ Data remains in the source system, and queries are sent directly to the data source in real-time. Power BI doesn't store data. 2️⃣ Performance depends on the underlying data source and the efficiency of its queries (generally slower). ⏳ 3️⃣ No need for periodic data refreshes because it's a live connection. �live 4️⃣ Use Case: Ideal for large databases or when we need real or near-real-time data. 🌐 Which mode do you prefer for your Power BI projects? Share your thoughts below! 💭👇 #PowerBI #DataAnalytics #BusinessIntelligence #DataVisualization #DataModeling #PowerBITips #BusinessAnalytics #DataScience #DataDrivenDecisions #PowerBIReporting #DataManagement #BI #Analytics #DataInsights #PowerBICommunity #DataStrategy #DataGovernance #DataPerformance #PowerBILearning

🌟 Exploring Power BI: Direct Query vs. Import Mode.🌟 🔍 Understanding Power BI's Data Connectivity Options Power BI offers two primary data connectivity modes: Direct Query and Import Mode. Understanding these options is crucial for optimizing your data analysis and reporting. 📊 Direct Query Mode What is it? Direct Query mode establishes a direct connection to your data source, allowing real-time data analysis. Benefits: Live data access, always up-to-date, reduces Power BI file size. Considerations: Can be slower for large datasets, limited data transformation capabilities. 🔄 Import Mode What is it? Import mode pulls data into Power BI, storing it within the report. Benefits: Faster performance on large datasets, extensive data transformation and modeling capabilities. Considerations: Data isn’t live, requires manual or scheduled refresh, larger file sizes. Choosing between Direct Query and Import Mode in Power BI depends on your specific needs: real-time data access vs. in-depth analysis on larger datasets. Understanding these modes empowers you to make the best decision for your data analysis strategy. #PowerBI #DataAnalysis #DataConnectivity #DirectQuery #ImportMode #DataAnalytics #BusinessIntelligence #DataVisualization #DataStrategy #DataInsights #BIReporting #DataManagement #AnalyticsStrategy #ProfessionalDevelopment #BItools

Hello everyone, #day11 Today's topics are the difference between Import and Direct query connection mode from Power BI and the difference between a Subquery and a Join. Let's start! 📌Power BI Q. What is the difference between Import and Direct query connection mode? Ans: Import Mode: The extraction of data from the database and storing it locally on our local machine is called import mode. There is a 1 GB limit for the data we want to publish to the Power BI Service. It is fast and performance is better compared to other connection modes. Direct Query Mode: Leaves data in the database and sends the queries to pull the information needed. The data view section is removed from the report. No limit on the data as we have 'live' data, performance issues are the main concern, and we have limited DAX function for work upon. 📌SQL Q. What is the difference between a subquery and a join? A subquery is a query nested within another query, whereas a join is used to combine rows from two or more tables based on related columns. #powerbi #powerbideveloper #dataanalytics #dataanalysis #data #interviewquestions #interviewpreparation #dashboard #reports

Here are some of the key differences between Import and Direct Query In Power BI 📈 . . . . . . . . . 🔍 Import Advantages: ● Faster performance due to data being stored in memory ● Greater functionality, including the ability to use all M and DAX functions ● Multiple data sources can be combined Disadvantages: ● Data is only as fresh as the latest refresh ● Limited to 1GB per dataset (can be increased in Premium) ● Requires manual or scheduled refreshes 🔍 Direct Query Advantages: ● Real-time access to data ● No data storage limits ● More secure as data remains in the original data source Disadvantages: ● Slower performance due to queries being sent to the data source at run time ● Limited functionality, including limited DAX functions ● Data is stored locally, which could pose a risk if not properly secured Choosing between Import and Direct Query depends on your specific needs and requirements. Import is ideal for small to medium-sized datasets, while Direct Query is better for large datasets or scenarios where you need to access the most up-to-date data. However, Direct Query has some limitations in terms of functionality, so you may need to use a combination of Import and Direct Query to achieve your goals. #powerbi #datavisualization #dataanalysis #datainsights #businessintelligence

I have three tables, each consisting of sales data from a particular year. Now, I want to merge all these tables into one for convenience. One method to do this is by using Append Queries since they share the same column names and data types. However, using Append Queries has a drawback: even though Power Query will create a merged table, it won't delete the original tables from which the merged table was created. Here's a PRO TIP: Instead of using Append Queries in Power BI to merge different tables with the same specifications, create a folder on your PC and add all the sales data files for 2020, 2021, and 2022 to that folder. Then, open Power BI, go to Source, select Folder, and import that folder. 📁 Benefits of doing this: 1: Every time you add another file to that folder, Power BI will automatically add that file's data to your merged table. 📊 2: There is no need to manually import files into Power BI and perform Append Queries. 🚀 If this post helped you so, please share it with your friends. #DataAnalysis #PowerBi #PowerQuery #ETL #DataScience

🔰 Storage modes in Power BI In Power BI, there are three primary storage modes that we can use to optimize performance based on our data characteristics and analysis requirements: 1️⃣ Import Mode: In this mode, Power BI caches table data in-memory for reports. It delivers fast performance by using in-memory querying. Import mode is commonly used for developing semantic models and supports features like Q&A and Quick Insights. 2️⃣ Direct Query Mode: In this mode, queries are sent directly to the data source without caching data in-memory. It’s useful for near-real-time requirements and large semantic models that are too expensive to cache completely. 3️⃣ Composite Mode (Dual Mode): This mode allows a report to have two or more data connections, including Direct Query connections or Import, in any combination. Visuals that don’t require a query are imported even if they’re based on Direct Query, improving performance and reducing back-end load. #PowerBI #Data #DataStorage

🔍 Understanding the Difference: Import Mode vs. DirectQuery in Power BI 🔍 Import Mode: • Data Storage: Data is fully imported and stored in Power BI’s in-memory engine. • Performance: Lightning-fast performance with data cached for quick access. • Data Freshness: Requires scheduled refreshes to update the data. • Ideal For: Smaller datasets or when working with historical data that doesn’t change frequently. DirectQuery Mode: • Data Storage: No data is imported. Power BI queries the data source directly in real time. • Performance: Dependent on the performance of the underlying data source. • Data Freshness: Always reflects the most current data, as it queries the database on demand. • Ideal For: Large datasets and scenarios where you need the latest data, like operational dashboards. Pro Tip: Choose Import Mode for speed and flexibility with manageable data sizes. Opt for DirectQuery when data size is large, or you need the freshest data possible. #PowerBI #DataAnalytics #BusinessIntelligence #DataVisualization #TechTips #sql #datasets #DataAnalyst