DP-700: Fabric Data Engineer Associate

This gives the code for this section.

In this chapter we will have an introduction onto the data warehouse.

In this chapter we will see how data is modelled in a data warehouse.

In this chapter we will see how to build a data warehouse.

In this chapter we will learn how to create a data warehouse.

In this chapter we will learn how to create an Azure Data Lake Gen2 storage account.

In this chapter we will learn how to ingest data via T-SQL

In this chapter we will learn how to ingest data using Data Pipelines.

In this chapter we will learn how to ingest data using Dataflow Gen2.

In this chapter we will view the data sets we are going to use.

In this chapter we will look into a lab onto configuring the source in Data flow Gen2.

In this chapter we will look into a lab on completing the Data flow Gen2 workflow.

In this chapter we will look into a lab into building dimension tables using Data Flow Gen2.

In this chapter we will look into a lab into building Date Dimension tables using Data Flow Gen2.

In this chapter we will look into a lab into running data flows in Data pipelines.

In this chapter we will look into a lab into running a stored procedure in Data pipelines.

In this chapter we will look into a lab into creating a new workspace in Microsoft Fabric.

In this chapter we will look into a lab into transferring data onto a data warehouse.

In this chapter we will look into a lab into building a semantic model.

In this chapter we will look into a lab into using T-SQL commands for a data warehouse.

In this chapter we will look into slowly changing dimensions.

This gives the code for this section.

This chapter goes into what is a lakehouse.

This chapter shows a lab into creating a lakehouse.

This chapter shows a lab on how to ingest files into the lakehouse.

This chapter talks about a delta lake.

This chapter shows a lab on the data setup part for ingesting data into a lakehouse via a data pipeline.

This chapter shows a lab on the running the pipeline for ingesting data into a lakehouse via a data pipeline.

This chapter just talks about running the data pipeline on a schedule.

This chapter talks about shortcuts onto Azure Data Lake.

This chapter talks about shortcuts onto AWS S3.

This chapter shows a lab for a data pipeline use case where we go through the overview.

This chapter shows a lab for a data pipeline use case where we go through creating the Azure SQL database.

This chapter shows a lab for a data pipeline use case where we go through the implementation.

This chapter goes through Apache Spark.

This chapter shows a lab on using Notebooks where we load data onto a data frame.

This chapter shows a lab on using Notebooks where we see how to detect NULL values.

This chapter shows a lab on using Notebooks where we check for duplicate rows.

This chapter shows a lab on using Notebooks where we have the initial setup for building a Fact table.

This chapter shows a lab on using Notebooks where we build the Fact table.

This chapter shows a lab on using Notebooks where we extract values.

This chapter shows a lab on using Notebooks where we build dimension tables.

This chapter shows a lab on using Notebooks where we run notebooks as part of a data pipeline.

This chapter shows a lab on using Notebooks where we merge data.

This gives the code for this section.

This chapter gives a description onto an Eventhouse.

This chapter goes into a lab on ingesting data into the eventhouse.

This chapter goes into a lab on ingesting sample data.

This chapter goes into a lab on ingesting data from Azure Data Lake.

This chapter goes into an introduction onto the Kusto Query Language.

This chapter goes into event streams in event house.

This chapter goes into a lab into creating an Azure Event hub.

This chapter goes into a lab into setting up an Eventstream.

This chapter goes into a lab into ingesting sample data into the event stream.

This chapter goes into a lab onto working with KQL queries.

This chapter goes into a lab into creating data tables.

This chapter goes into a lab into transforming data by choosing columns.

This chapter goes into a lab into transforming data by filtering data.

This chapter gives a note on OneLake availability.

This chapter gives a note on the change data capture feature.

This chapter goes into cleaning up resources.

This chapter has the code for this section.

This chapter goes into a lab for creating a new workspace.

This chapter goes into a lab into giving admin permissions over the tenant.

This chapter goes into a lab into assigning users to Microsoft Fabric.

This chapter goes into a lab onto what new users can do in Microsoft Fabric.

This chapter goes into a lab into giving access to update tables via T-SQL.

This chapter goes into a lab into giving access to update tables via Workspace permissions.

This chapter goes into a lab into column level security.

This chapter goes into a lab into row level security.

This chapter goes into data masking.

This chapter goes into a lab for data masking.

This chapter goes into sensitivity labels.

This chapter goes into endorsing items.

This chapter goes into domains in Microsoft Fabric.

This chapter goes into deployment pipelines in Microsoft Fabric.

This chapter goes into a lab into building deployment pipelines.

This chapter goes into additional notes on deployment pipelines.

This chapter goes into version control.

This chapter goes into an example on using version control.

This gives the code for this section.

This chapter goes into making decisions.

This chapter goes into environments in Microsoft Fabric.

This chapter goes into high concurrency in notebooks.

This chapter goes into lakehouse shortcuts cache.

This chapter goes into an overview of managed private connections.

This chapter goes into a lab onto Managed private connections.

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