If you are a BI developer, business analyst, data scientist who wants to push Power BI and transform it from being just a BI tool into an advanced data analytics tool, then this is session. R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration, but become easier through the application of data wrangling features that languages such as R and Python support. I will cover:
• Configuring Python with Power BI
• Solving Common Issues When Using Python and R in Power BI
• Importing Unhandled Data Objects
• Using Regular Expressions in Power BI
• Logging Data from Power BI to External Sources
• Loading Large Datasets beyond the Available RAM in Power BI
• Boosting Data Loading Speed in Power BI with Parquet Format
• Create data visualizations through R using the ggplot2 package
• Using the Grammar of Graphics in Python
• Ingest data using R and Python to overcome the limitations of Power Query
• Use String manipulations not otherwise possible in Power BI using R and Python
• Using Machine Learning Without Premium or Embedded Capacity
• Using the SQL Server Extensibility Framework’s R and Python engines with Power BI
• Use SQL 2019 2022 Polybase with Power Bi, R and Python to explore Hadoop, Oracle, MongoDB and SQL server data sets with 1 T-SQL Query to build your organization data lake with min effort and without ETL\ELT in real time .
• If time Permit: Apply pre-trained models in SQL Server Machine Learning Services models
This is a maximum demo session with minimum theory!