In BlueGranite’s recent webinar, you will see several examples of Python in action for data modeling and visualization in Power BI. You will also learn where and how Python fits into a Power BI development workflow.

You’ll also see how to balance Python with native Power BI functionality and determine what limitations must be considered when using Python in Power BI.

In this video, get answers to questions like: Why should you care about the data model? What kind of relations are available? How do they influence performance? Why redundancy is (not) a bad thing? Don’t just tell your friends that you’re a professional who works with models, but be great in doing it.

Data Lake Storage Gen 2 is the best storage solution for big data analytics in Azure. With its Hadoop compatible access, it is a perfect fit for existing platforms like Databricks, Cloudera, Hortonworks, Hadoop, HDInsight and many more. Take advantage of both blob storage and data lake in one service!

In this video, Azure 4 Everyone introduces to what Azure Data Lake Storage is, how it works and how can you leverage it in your big data workloads. I will also explain the differences between Blob and ADLS.

Sample code from demo: https://pastebin.com/ee7ULpwx

Next steps for you after watching the video
1. Azure Data Lake Storage documentation
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-introduction
2. Transform data using Databricks and ADLS demo tutorial
– https://docs.microsoft.com/en-us/azure/azure-databricks/databricks-extract-load-sql-data-warehouse
3. More on multi-protocol access
– https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-multi-protocol-access
4. Read more on ACL
– https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-access-control