what is power bi
powerbi is visulization and reporting tool from microsoft.
why power bi
less cost
Power bi
power
power query - 7 days - ETL - Extrat transformation tool
power view - 7 days - view - report
power pivot - 7 days - data modeling
Power bi service - 3 days - sharing and
How power bi works -
we dont need any programming skills
1.sql 2. basic excel 3. data modeling
power bi can connect 99 data sourcec
Power Query - ETL - tranformations
Excel
sql server PoWerbi desktop - power query - ETL - - POWERService - users
Power bi install - licences
power bi desktop
power bi licence
Free
we can deveop the report
Pro
We can develop the report and share the report -
cost 9.99 $
1GB
Premium
5000$
pOWER Bi connection methods
Import - offline - Excel , text , csv
Direct - any database - sql server, postgre, oracle
Live - Azure,ssas
How exactly power bi works
why should
Powerbi - trending - we can connect 99 data sources -
Power bi cost - very less - compare to other tools - other tools - 100$
Power bi - User friendly tool
Power bi desktop -
Excel -
Sql server -
Live -
Will have one project
after this course if you want to do certification - PL 300 certification -
Premium - online files - twitter - Trending
Along with Power BI - Add on advancatges courses
1. ETL - SSIS
2. Reporting tool - Power BI Fullstack - powerbi report builder
3. Power automate
4. power apps
5. data warehouse - snowflake
6. database - Sql server, oracle,
****Day 2
Data connection methods
Import direct Live
1.We will import the data 1.Data will be source 1.data will be source
2.We can connect 99 data sources 2.Only databases (sql server) 2.SSAS
3.Wecan Perfome all DAX funcetion 3.Limited DAX 3.All dax fucntions
4.1GB - Pro & Premium - 10gb 4.No data limit 4.1GB
5.Fast perfomrance 5.slow performance 5.fast performance
6.Not fresh data 6.Fresh latest data 6.Latest data
Day 3***
Merge queries combine tables horizontally, while append queries combine tables vertically.
Merge queries require at least one matching column in each table, while append queries require matching columns with the same data types
Power Query - Append and Merge - Rows
Apeend - we can join 2 or more tables (sheets)
1. Normal Append -
2. Case sensitive
3. Column order may not be the same
Merge -to join the 2 or more tables. - Columns
Left join - All the records from left table and matching records from right table
right join - Allt the records from right table and matchig from the left table
Full join - Allthe records from both the tables
Inner join - Matching records from the both the tables
Right anti join - NoT MACTHING Records from right table
left anti join -not matcing records from left table
Duplicate table
rEFERANCE TABLE
Advance Editor - M- launguage - M code
Day 4***
Power Query -
Conditional Column - (IF else) - 2 or more conditions
Coulmn from examples - given exmaples
Manage parameter - Dynamic filter in source level
Advance Editor - M Luanguage CODE -
Day 8- Data Modeling - Relationship between fact and dimension table.
Fact - Transcation table which more num we can perform - Agrregate - Measure (Metric)
Dimension - More Text data
Product table - customer table
Product table - Dim
Prod id - Produ name - pro Qty - Pro price - mb
001 - Ramayana- 1 - 50
002 - mahabaha- 1 - 40
Custmer table
cusoter code- produ code - date - price - Qty - Fact - 500 mb
134 - 001 - 14 - 1
123 - 002- 15- 2
145 - 123
Schema - Star Schema - Fact table is connecting directly with Dim table
Snow flake - Fact table to connectionf dim table and this dim is connectiing another dim table
Fact table is indirectly conect with another dim table is called snowfale schema.
Fact - Dim
More num - More text - Primary
Foreign key
Star schema - SNow flake
Large date - Less date
2. tables are combine - 1. More table
3. denormalize 2. Table spi
4 Reading fast 3. Normalization
4. writing slow 4. Reading slow and writing fast.
Relationships -
to get the proper results in report - we should give the relationship
Autodetect -
Creta relations ship-
Edit -
Delete relationship
How many cordinalities
1. One to one
2. One to many
3. Many to one
4. Many to many
If we have multiple databases how to give relationships- tomrow i need to
Day 9- Data modeling - Relationships
1. create relationship - Manually created relationship
2. Auto detect relationship - automatically
3.Edit relationship - Edit relationship
4. delte relationship - delete the relationship
Cardinalites -
1. one to many - wil create one to many - Single - both
2. Mnay to one - will create Many to one - single - both
3. One to one -
4. Manay to many
Cross filter - single and both (BI)
ONE TO ONE - BI DIRECTIONAL HOWEVER WE CANT CHANGE TO SINGLE
Many to many - by deafult i twill be bi directional
we cant change to single direction
Active and inactive relationship .....tomrow will discuss
taking one example f mulitple relationships
Bar Charts and Column Charts:these visuals to display financial metrics, market trends, or industry performance.
Line Charts:visualize changes in key performance indicators, economic indicators, or business growth rates.
Pie Charts and Donut Charts: these visuals to represent market share, industry distribution, or customer segmentation.
Map Visuals:visuals to analyze regional economic indicators or market penetration.
Matrix Visuals:matrix visuals to show complex relationships, such as industry performance by sector and region.
Scatter Plots:to analyze relationships between economic factors or business metrics.
Gauges and Cards:visuals to display critical information like financial ratios, credit scores, or risk assessments.
KPI Indicators:use KPI indicators for metrics such as profitability ratios, liquidity ratios, or industry benchmarks.
Hierarchy and Drill-Down Visuals:visuals to provide a top-level overview of industries, markets, and then allow users to explore specific companies or segments.
Sankey charts: Financial analysts can use Sankey charts to visualize cash flow within a business, showing how money is allocated to different expenses, investments, or revenue streams.
Relational Database Design:
Think of it like a spreadsheet with tables, rows, and columns.
Good for storing everyday data, like names, addresses, and orders.
Helps keep data organized and prevents repeating the same information.
Works well for applications where data needs to be changed frequently.
Multidimensional Database Design:
Imagine a data cube with different layers.
Great for analyzing and finding patterns in data.
Especially useful when you want to look at data from different angles or perspectives.
Perfect for making reports and finding insights from big datasets.
