If you want to update the existing item in the SharePoint list instead of creating a new one when you send the same data from the Power BI report. This is certainly possible with Microsoft Flow. Here are the steps you can follow to update the existing item in SharePoint list: 1. Create a Flow with a Trigger: • Start by creating a new flow or editing your existing flow. • Set up the trigger as per your requirement (either automated or instant with a button). 4. Get Data from Power BI Report: • Add a new step and choose "Power BI." • Use the "Get rows" action to retrieve data from your filtered Power BI report. • Configure the "Get rows" action by specifying the workspace, dataset, and table. 8. Check if Item Exists in SharePoint List: • Add a new step and choose "SharePoint." • Use the "Get items" action to retrieve items from your SharePoint list. • Use a filter query to check if the item you want to update already exists in the list. 12. Update or Create Item in SharePoint List: • Add a condition to check if the item exists in the SharePoint list. • If the item exists, use the "Update item" action to update the existing item in the list. • If the item does not exist, use the "Create item" action to create a new item in the list. 16. Save and Test Your Flow: • Save your flow and test it by triggering it manually or waiting for the automated trigger to occur.

Interviewer (I): Uh, yeah. Uh, can you quickly walk me through your profile? Candidate (C): Yeah, sure. Thanks for this opportunity. My name is Dinesh. I have a total of four years of experience in IT. Most of my relevant experience comes from working with Power BI, which is almost three years. Currently, I'm employed at Fidelity. This is my current company. I've been involved in many end-to-end reporting projects. As for my skills, I'm proficient in SQL and visualization tools such as Power BI. Much of my experience involves building comprehensive reports. Right now, I'm working with a client named Sigma, a US-based insurance company. This client requires us to gather and analyze insurance data from on-premises databases for reporting purposes. INTERVIEWER: Great. Can you tell me about the sources you've connected for the current project? CANDIDATE: Certainly. For the current project at Sigma, we're connected to a Microsoft SQL database using the import mode. Although I have experience with various data sources like CSV, Excel, JSON, and XML files, for this project, we're specifically using a SQL Server. INTERVIEWER: Could you explain the difference between import mode and direct query mode? CANDIDATE: Import mode and direct query mode are both ways to access data. In import mode, Power BI brings the data into its environment, allowing for comprehensive modeling, including creating schemas, implementing functions, and more. However, there's a storage limitation. Direct query mode, on the other hand, accesses data from the source tables via queries. It's beneficial when dealing with unlimited data sizes. Import mode lets you build complex models, while direct query mode allows for working with vast datasets directly from the source. INTERVIEWER: How would you rate your proficiency in SQL and Power BI? CANDIDATE: In terms of SQL, I'd rate myself at around 3.5 out of 5. As for Power BI, I'd give myself a 4 out of 5. SQL was my starting point, and I've honed my skills with it. With Power BI, I've gained significant experience over time. INTERVIEWER: Can you share your role and responsibilities across the projects you've worked on as a Power BI developer? CANDIDATE: Certainly. In my current project, I'm the sole Power BI developer. I've been involved in the entire lifecycle, starting from gathering requirements. Our client provided requirements without direct data, so I had to analyze the database, identify relevant tables, and create schema structures. I've also performed various data transformations, including text, column, and data transformations. I'm well-versed in choosing the appropriate visualization charts for different data. Additionally, I've been responsible for publishing the reports and setting up schedules for automated updates. INTERVIEWER: Have you worked with any custom charts beyond the default ones? CANDIDATE: Yes, I have. While custom charts are typically obtained through premium access or organizational accounts, I've used charts like infographic charts, scroller charts for categorical data, word clouds, and drill-down column charts. We follow guidelines to ensure the charts we use are approved and reliable for our reports. INTERVIEWER: How about Sankey charts or ribbon charts? Are you familiar with those? CANDIDATE: Certainly. Sankey charts are useful for visualizing relationships between categorical variables. Ribbon charts, on the other hand, are an extended version of stacked column charts. They allow you to visualize the change and growth between different levels, making them great for year-over-year comparisons or percentage growth analyses. INTERVIEWER: Have you utilized conditional formatting in your reports? CANDIDATE: Yes, I've implemented conditional formatting, especially when accuracy down to the decimal point is crucial. We use it in metrics and tables to make the data more informative and visually appealing. INTERVIEWER: You mentioned working with DAX functions. Can you provide an example of a scenario where you used DAX functions? CANDIDATE: Certainly. Let's say we have a sales scenario, and we want to compare this year's sales with the sales of the previous year for the same time frame, say from November to December. In this case, I'd use the SAMEPERIODLASTYEAR function. It's a versatile way to achieve year-over-year comparisons without having to manually adjust time frames. INTERVIEWER: Have you also worked with DAX functions beyond this example? CANDIDATE: Absolutely. I've worked with various DAX functions. For instance, I've used SUMX for calculating totals even when the original column isn't present. It's a powerful way to achieve calculations without altering the original dataset. I've also used RANKX, ALL, ALLSELECTED, and ALLEXCEPT functions, depending on the requirements and context of the reports. INTERVIEWER: You mentioned working with Power Query Editor for data transformations. Can you share some of the transformations you've performed? CANDIDATE: Certainly. I've worked with text transformations, number transformations, column transformations, and merging queries. I've also built schema structures like star schemas and snowflake schemas to organize the data efficiently. Power Query Editor has been instrumental in transforming raw data into meaningful insights. INTERVIEWER: Have you faced any challenges during your projects? CANDIDATE: Yes, there were a few challenges. One significant challenge was data gathering. Clients often provide requirements without direct data sources, which means I had to explore and identify relevant tables. Additionally, implementing complex calculations and DAX functions sometimes required intensive effort. In another project, data performance tuning was crucial, where we aimed to optimize report performance through efficient Power Query transformations. INTERVIEWER: Could you provide an example of a complex report you've built and any positive feedback you received? CANDIDATE: Certainly. In a previous project for a telecommunication vendor, we built a dynamic report with innovative features. One instance was using infographic charts to represent different telecom services visually. The client appreciated the creativity and out-of-the-box thinking, and the report was well-received. INTERVIEWER: What type of access did you hold in your projects, and what were your roles? CANDIDATE: In my current project, I held contributor access since I was the primary Power BI developer. I had the responsibility of creating reports, performing data transformations, and publishing them. I've seen different roles in other projects, where teams had multiple members with varying levels of access. INTERVIEWER: Have you implemented row-level security in your reports? CANDIDATE: Yes, we've implemented row-level security to restrict data access based on user roles. This ensures that each user sees only the relevant data, enhancing data security. INTERVIEWER: How do you keep yourself updated with Power BI's frequent updates? CANDIDATE: I stay updated by regularly checking Microsoft's official documentation, where they summarize new features and updates each month. I also engage in hands-on Proof of Concept (POC) projects to test new features before incorporating them into live projects. For instance, I recently explored small multiples for improved data visualization. INTERVIEWER: Thank you for sharing your experiences and insights. Do you have any questions for me? CANDIDATE: Yes, thank you for the discussion. I'd like to know more about the specific role you're looking to fill and the challenges the team is currently facing. INTERVIEWER: Certainly. We're seeking a Power BI developer with end-to-end experience, similar to what you've described. The role involves working across all phases of the reporting lifecycle, from data gathering to sharing insights with stakeholders. It seems like your experience aligns well with our requirements. CANDIDATE: Thank you for the information. It's been a great conversation and I appreciate the opportunity to discuss my background and skills. INTERVIEWER: Likewise, thank you for your time. We'll be in touch soon regarding the next steps in the interview process. CANDIDATE: Sounds good. Looking forward to it. Thank you again. INTERVIEWER: You're welcome. Have a great day. CANDIDATE: You too. Goodbye  

Any one have dax pattern collection notes?

Let's start with a brief introduction and then move on to interview preparation. Introduction to Groovy: 1. Basic Syntax: • Like Java, Groovy requires classes and methods. • It's less verbose; you don't need semicolons at the end of each line. • You can define variables using def. def name = "ChatGPT" println "Hello, $name" 2. D(http://2.Data)ata (http://2.Data)Types: • Groovy supports all Java data types. • Dynamic typing with the def keyword. • Lists: def list = [1, 2, 3] maps: def map = ['name': 'Chat', 'type': 'AI'] 1. 3. Control Structures: • If-else, loops, and switch-case structures are similar to Java but with a simpler syntax. Sample Interview Questions: 1. Basic Understanding: • What is Groovy, and how is it different from Java? • Explain dynamic typing in Groovy. 4. Syntax & Semantics: • What is the use of the def keyword? • How do you define a closure in Groovy and provide an example? 7. Features: • What are Groovy Builders, and why are they useful? • Explain how Groovy handles null values. 10. Advanced Topics: • How does Groovy enable metaprogramming, and what are its benefits? • Describe the difference between GString and a regular Java String. 13. Practical Scenarios: • Given a list of integers, write a Groovy script to filter out even numbers and square the remaining ones. • How would you handle JSON parsing in Groovy?

Practicing DAX (Data Analysis Expressions) functions is essential for mastering Power BI and Excel data modeling. DAX is a formula language that allows you to create custom calculations and aggregations in your data models. Below are some common DAX functions along with examples and sample data to help you get started: Sample Data: Let's consider a simple sales table with the following columns: Date: The date of the sales transaction. Product: The name of the product sold. Quantity: The quantity of the product sold in each transaction. Price: The price of each product sold. Examples of DAX Functions: SUM: Calculates the sum of a numeric expression over a table. Total Sales = SUM(Sales[Quantity] * Sales[Price]) This DAX formula calculates the total sales amount by multiplying the Quantity and Price columns for each row and then summing the results. AVERAGE: Calculates the average of a numeric expression over a table. Average Price = AVERAGE(Sales[Price]) This DAX formula calculates the average price of all products sold. COUNTROWS: Counts the number of rows in a table or table expression. Total Transactions = COUNTROWS(Sales) This DAX formula calculates the total number of transactions in the sales table. FILTER: Filters a table based on a specified condition. Filtered Sales = FILTER(Sales, Sales[Price] > 10) This DAX formula creates a new table that includes only the rows where the price is greater than 10. RELATED: Returns a related value from another table based on a specified relationship. Product Category = RELATED(Products[Category]) This DAX formula returns the product category for each row in the sales table, based on the relationship between the Sales and Products tables. CALCULATE: Evaluates an expression in a modified filter context. Discounted Sales = CALCULATE(SUM(Sales[Quantity] * Sales[Price]), Sales[Price] > 10) This DAX formula calculates the total sales amount for transactions where the price is greater than 10. These examples demonstrate some basic DAX functions and how they can be applied to the sample data. As you practice and gain familiarity with DAX, you can explore more advanced functions and combinations to perform complex calculations and analysis in your data models. Happy DAX-ing! 📈💡

Understanding Requirements: Power BI developers work closely with business stakeholders, data analysts, and decision-makers to understand the requirements for data analysis and reporting. They gather information about the data sources, metrics, KPIs, and the objectives of the reports/dashboards. Data Preparation: The developers then work on data preparation, which includes connecting to various data sources, transforming and cleaning the data, and building the necessary data models. Visualization Design: Based on the requirements and data available, the Power BI developer designs the visualizations and dashboards. They choose appropriate visualizations, layouts, and colors to make the insights easily understandable. Report Development: With the data model and visualizations ready, the developer creates reports and dashboards in Power BI using the Desktop application. Data Integration: For more complex projects, the Power BI developer might integrate Power BI with other tools, databases, or data warehouses. Testing and Debugging: Developers conduct thorough testing to ensure that the reports and visualizations are accurate and functional. Deployment and Sharing: Once the reports are ready, they are deployed to Power BI service, where they can be shared with relevant users or embedded in applications. Continuous Improvement: Power BI developers often receive feedback from users, which helps them refine and enhance the reports. They work on continuous improvement to ensure the insights are up-to-date and relevant. Team Dynamics: The teams involved in a Power BI developer role may vary depending on the organization and project size. Generally, the following teams are involved: Business Stakeholders: They provide the requirements and define the objectives for the Power BI reports and dashboards. Data Analysts: They work closely with Power BI developers to gather data and provide insights that inform the visualization design. Data Engineers: Data engineers are responsible for data extraction, transformation, and loading (ETL) processes, which provide the data for Power BI. Data Scientists: In projects involving advanced analytics or machine learning, data scientists might collaborate with Power BI developers to integrate AI insights. IT Team: The IT team plays a role in managing the infrastructure, security, and access control for the Power BI deployment. Sharing Requirements and Visuals: The requirements are typically shared through meetings, emails, or documented in project management tools. Business stakeholders and data analysts communicate their needs, data sources, and KPIs to the Power BI developer. Regarding visuals, Power BI developers use the Power BI Desktop application to create and modify visualizations interactively. They can also share drafts or mock-ups with stakeholders for feedback before finalizing the reports. Overall, Power BI developers collaborate with various teams and stakeholders throughout the project lifecycle to deliver effective and impactful data solutions that support decision-making processes within the organization.

Adding Broker API in Power BI Here's a general outline of the steps you can follow: Get API Access: Ensure you have access to your broker's API and the necessary authentication credentials (API key, secret, token, etc.). Open Power BI: Launch Power BI on your computer. Choose Data Source: Click on "Get Data" in the Home tab to choose your data source. Select Web or JSON: Depending on your broker's API, you can select either the "Web" option or the "JSON" option as your data source. Enter API Endpoint: Enter the API endpoint URL provided by your broker in the "URL" field. This URL is where you'll be making API requests to fetch the live feed data. Configure Authentication: Depending on the authentication method your broker uses (e.g., API key, OAuth), you may need to configure authentication settings. If it's API key-based, you can often include the API key in the URL itself or provide it in headers. If it's OAuth, you might need to enter the appropriate credentials. Transform Data (Optional): Power BI might load the data as-is or allow you to transform it. You can use the Power Query Editor to clean, filter, or modify the data before using it in your reports. Load Data: After configuring authentication and optionally transforming the data, click on the "Load" button to load the live feed data into Power BI. Create Visualizations: Once the data is loaded, you can create visualizations like charts, tables, and graphs using the "Visualizations" pane. Set Refresh Schedule: If you want the data to update automatically, you can set a refresh schedule in Power BI. Depending on your version of Power BI, there might be limitations on how often you can refresh data in the free version. Save and Publish: After creating your visualizations and setting up refresh, you can save your Power BI file and publish it to Power BI Service if you want to share it with others.

AI is at the forefront of innovation, transforming industries and revolutionizing the way we interact with machines. From AI-driven virtual assistants to smart homes and autonomous vehicles, the potential of AI is limitless. Let's share our thoughts on how AI impacts our daily lives and its ethical implications.