Exploring the Advanced Dataset for Power BI

expertiseexcelerate

Exploring the Advanced Dataset for Power BI  Unlock the full potential of your data with our deep dive into advanced datasets in Power BI. This playlist is tailored for professionals, data enthusiasts, and Power BI users looking to enhance their data modeling and analysis skills. This course guides you through the complexities of working with advanced datasets, helping you harness Power BI's power for more insightful and impactful reporting.  This playlist covers everything you need to take your data skills to the next level, from data transformation to advanced query techniques.  What You’ll Learn: • Navigating and optimizing advanced datasets in Power BI • Techniques for effective data transformation and modeling • Leveraging Power BI’s advanced features for in-depth analysis • Best practices for building complex reports from rich datasets  Ideal For: • Data Analysts and Business Intelligence Professionals • Power BI users looking to master advanced data features • Anyone interested in enhancing their data analysis capabilities  Subscribe for more tutorials on the latest software and advanced Power BI techniques to stay ahead in data analysis.  Power BI advanced features walkthrough Advanced dataset analysis with Power BI Power BI advanced data visualization tutorial Exploring advanced Power BI capabilities Mastering advanced Power BI techniques Deep dive into Power BI advanced dataset management Advanced Power BI dashboard design tips Harnessing the full potential of Power BI advanced features Advanced data modeling in Power BI Creating advanced reports in Power BI  #VisualAnalytics #PowerBI #BusinessIntelligence #DataScience #DataVisualization #DataAnalytics #DataManagement #PowerBIDashboard #DataAnalysis #DataReporting #PowerBITutorial #MicrosoftPowerBI #PowerBINewFeatures #BusinessAnalytics #DataInsights #PowerBITips #PowerBIFeatures #AnalystTools #PowerBIforAnalysts #AdvancedDataset #DataAnalyst #PowerQuery #PowerBIProject #Dataset #Dashboard #DataTransformation #DataMining #PowerBIdataset

Transcript

00:00Hey friends, welcome back.

00:07Now I really hope you had a little bit more time to dive deeper into building your first

00:12report and try out different visualization types, formatting options, and so on.

00:16Now, if you're done with this, just make sure that you click the save disc here again.

00:20And also if you close the report without saving and Power BI would just create a pop-up window

00:26and ask you whether we want to save the data.

00:28And for now, I'd like to do this, let me just click on the save option here and then close

00:32it because now we would like to dive deeper into a more advanced data set.

00:37Because remember, so far, what we did was just importing one simple Excel file, which

00:42was this small table and create our report.

00:46But now of course, we would like to know and learn how we can use a more advanced data

00:51set where we need to combine tables together, create relationships, and so on.

00:56And in this video here, I just would like to show you what kind of data we deal with

01:00the next couple of videos.

01:02So let me just open this one.

01:03And that is the Excel file called PBI, so Power BI Training 1.

01:09And you should also find it in the resource section to follow along with me.

01:13So what you could see here is in this Excel file, we have three tabs.

01:18So we have a sales tab for 2023, and I mean three sales tabs.

01:22We have a sales tab for 2022 sales and for 2021 sales.

01:28What I'd like to highlight here is the structure of the table is exactly the same, right?

01:34So we have the same column names, which is important for setup, but we have the sales

01:39data in three different tables, right?

01:42For each of the years separately.

01:44And of course, what we want to accomplish in Power BI is getting this into one big table,

01:50which we can then analyze, slice and dice, and figure out, for instance, different kind

01:54of sales numbers across various products, for instance.

01:58But to do this, we need to combine these three tables.

02:01Also, we can see that for the product, for instance, we have a product ID in this table.

02:08And for the location, for instance, we have a location ID.

02:13Or for the salespeople, we have a salespeople ID, we have a customer ID, we have an order

02:17ID, right?

02:18So we have these RID numbers, but they don't give us much information because so far, okay,

02:23I can see this is product PID 2029.

02:27But what kind of product is it, right?

02:29From this table, we can't see it.

02:31And also for the other sales tables.

02:33Now this is a common structure, which you will find when we are talking about fact and

02:37dimension tables in, well, in data models normally, right?

02:41And also in databases.

02:43So we have a kind of key here that is actually the right word for it.

02:47We have a key, and we can look up this key in different tables.

02:51So for instance, to figure out what about this transaction here, right, we have a quantity

02:55of two, a price of 1522.

02:59So a total sales of two times this value here.

03:02This was generated by this product in this location by this salesperson to this customer.

03:10And we can look this up because we have a product table here as well, products.

03:14And here we have the product name, right?

03:16These are the product names.

03:17And we have additional information, like for instance, what is the cost of the product?

03:22What was the original sale price?

03:24What is the discount?

03:25What's the current price?

03:26What are the taxes?

03:27All the information for the product are listed here.

03:31And we can get this information for the product as well as the name and add it to the sales

03:36table by creating our relationships.

03:38And when I say adding those values, I do not mean adding additional columns here directly,

03:44but combining the tables with relationships so that the Power BI model knows, for instance,

03:50for the product ID to look the data up in the products table in this one.

03:56And the same is true for locations.

03:58Here we have the locations, which we're going to deal with.

04:00They also have the location ID.

04:02This is the common field we have to our sales table where we all have a location ID.

04:07And that's how we can combine those tables with relationships to make sure that we can

04:11look up exactly the right information for each of the transactions.

04:16And hopefully it's clear how that works, because for customers, for instance, and also for

04:20salespeople, that's exactly the same.

04:22We have the ID, and this is our lookup table, more or less, where we can then search, okay,

04:27based on the salesperson ID, which we find in sales transactions.

04:31We know now, for instance, that Kevin was the guy who sold the product to our customers.

04:37And that's it actually for exploring this data set.

04:41So hopefully it is clear what the data looks like.

04:43And of course, feel free to open it and have a look at it yourself.

04:47But in the next couple of videos, we're going to dive deeper into importing the data in

04:51Power BI, transform it, slice and dice it the way we need it in the query editor.

04:56So we basically prepare the data, and then we can use it in order to create our report.

05:01So hopefully you're excited, as I am, and I can't wait to see you in the next video.

05:06Until then, best guys.

Category

Transcript

Recommended