Implement a data lakehouse analytics solution with Azure Databricks

In this module, you will:

• Understand the Azure Databricks platform
• Create your own Azure Databricks workspace
• Create a notebook inside your home folder in Databricks
• Understand the fundamentals of Apache Spark notebook
• Create, or attach to, a Spark cluster
• Identify the types of tasks well-suited to the unified analytics engine Apache Spark

In this module, you will:

• Understand the architecture of an Azure Databricks Spark Cluster
• Understand the architecture of a Spark Job

In this module, you will:

• Use Azure Databricks to read multiple file types, both with and without a Schema.
• Combine inputs from files and data stores, such as Azure SQL Database.
• Transform and store that data for advanced analytics.

In this module, you will:

• Use the count() method to count rows in a DataFrame
• Use the display() function to display a DataFrame in the Notebook
• Cache a DataFrame for quicker operations if the data is needed a second time
• Use the limit function to display a small set of rows from a larger DataFrame
• Use select() to select a subset of columns from a DataFrame
• Use distinct() and dropDuplicates to remove duplicate data
• Use drop() to remove columns from a DataFrame

In this module, you will:

• Describe the difference between eager and lazy execution
• Define and identify transformations
• Define and identify actions
• Describe the fundamentals of how the Catalyst Optimizer works
• Differentiate between wide and narrow transformations

In this module, you will:

• Learn the syntax for specifying column values for filtering and aggregations
• Understand the use of the Column Class
• Sort and filter a DataFrame based on Column Values
• Use collect() and take() to return records from a Dataframe to the driver of the cluster

In this module, you will:

• Manipulate date and time values in Azure Databricks
• Rename columns in Azure Databricks
• Aggregate data in Azure Databricks DataFrames

In this module, you will:

• Learn the Azure Databricks platform architecture and how it is secured.
• Use Azure Key Vault to store secrets used by Azure Databricks and other services.
• Access Azure Storage with Key Vault-based secrets.

In this module, you will:

• Learn about the key features and use cases of Delta Lake.
• Use Delta Lake to create, append, and upsert tables.
• Perform optimizations in Delta Lake.
• Compare different versions of a Delta table using Time Machine.

In this module, you will:

• Learn the key features and uses of Structured Streaming.
• Stream data from a file and write it out to a distributed file system.
• Use sliding windows to aggregate over chunks of data rather than all data.
• Apply watermarking to throw away stale old data that you do not have space to keep.
• Connect to Event Hubs read and write streams.

In this module, you will:

• Process batch and streaming data with Delta Lake.
• Learn how Delta Lake architecture enables unified streaming and batch analytics with transactional guarantees within a data lake.

In this module, you'll:

• Create an Azure Data Factory pipeline with a Databricks activity.
• Execute a Databricks notebook with a parameter.
• Retrieve and log a parameter passed back from the notebook.
• Monitor your Data Factory pipeline.

In this module, you will:

• Learn about CI/CD and how it applies to data engineering.
• Use Azure DevOps as a source code repository for Azure Databricks notebooks.
• Create build and release pipelines in Azure DevOps to automatically deploy a notebook from a development to a production Azure Databricks workspace.

In this module, you will:

• Access Azure Synapse Analytics from Azure Databricks by using the - SQL Data Warehouse connector.

In this module, you will learn best practices in the following categories:

• Workspace administration
• Security
• Tools & integration
• Databricks runtime
• HA/DR
• Clusters