Complete package Microsoft Certified: Azure Data Engineer Associate (DP-203) - incl. exam

• Microsoft Azure Blob storage is a container system for storing a

• variety of file types. In this course, you'll learn about the
• capabilities of blob storage and how to architect a deployment for
• optimal performance and scalability. Then, you'll explore the
• options for redundancy and how to recover from disasters. You'll
• discover where Azure Data Lake Storage Gen2, a feature set within
• blob storage, can be utilized for big data operations. You'll also
• learn how to plan for a data lake deployment, examine best
• practices, and explore how to deploy a Data Lake Gen2 account on
• Azure. This course is one in a collection that prepares learners
• for the Microsoft Data Engineering on Microsoft Azure (DP-203)
• exam.

• Planning the structure for data storage is integral to

• performance in big data operations. In this course, you'll learn
• about key considerations for data lakes and how to determine which
• file type and file format are the most appropriate for your use
• case. Then, you'll explore how to define how to design table
• storage for efficient querying and how data pruning can remove
• unnecessary data to accelerate transactions. You'll examine folder
• structures and data lake zones for organizing data effectively.
• Finally, you'll learn how to define storage tiers and how to manage
• the life cycle of data. This course is one in a collection that
• prepares learners for the Microsoft Data Engineering on Microsoft
• Azure (DP-203) exam.

• Partitioning data is key to ensuring efficient processing. In

• this course, you'll explore what data partitioning is and the
• strategies for implementation. You'll learn about transactional and
• analytical workloads and how to determine the best strategy for
• your files and table storage. Then, you'll examine design patterns
• for efficiency and performance. You'll learn about partitioning
• dedicated SQL pools in Azure Synapse Analytics and partitioning
• data lakes. Finally, you'll learn how data sharding across multiple
• data stores can be used for improving transaction performance. This
• course is one in a collection that prepares learners for the
• Microsoft Data Engineering on Microsoft Azure (DP-203) exam.

• The serving layer is where data is stored for consumption by

• processing services. In this course, you'll explore dimensional
• data modeling and hierarchies. You'll learn how to define slowly
• changing dimensions and temporal design within databases. Then,
• you'll learn about the differences between the star and snowflake
• schemas as well as how to design a star schema. Next, you'll
• examine incremental data loading for stream processing and the
• options for analytical data stores. Finally, you'll learn about
• options for creating metastores for use by Azure Databricks and
• Azure Synapse Analytics. This course is one in a collection that
• prepares learners for the Microsoft Data Engineering on Microsoft
• Azure (DP-203) exam.

• An effective storage structure is critical to big data

• implementation success. In this course, you'll explore data
• compression in databases and file storage. Then, you'll discover
• how partitioning and sharding are implemented in the database.
• Next, you'll explore designing tables in an Azure Synapse Analytics
• dedicated SQL pool, and implement geo-replication for redundancy in
• both databases and Azure Blob storage. You'll also discover
• implementing distribution schemes in Azure Synapse Analytics.
• Finally, you'll discover data archiving and long-term retention
• policies for Azure Blob storage and Azure SQL Databases. This
• course is one in a collection that prepares learners for the
• Microsoft Data Engineering on Microsoft Azure (DP-203) exam.

• Logical data structures, also called entity-relationship models,
• are models used to define a high-level model of data and the
• relationships contained within. In this course, you'll learn about
• the stages of data lake maturity. You'll explore temporal database
• tables and how to manage them. You'll also learn how to define
• slowly changing dimensions and how to implement them. You'll then
• move on to explore logical file and folder structures for data
• ingestion. You'll discover how PolyBase can be used to connect to
• external tables. Finally, you'll explore the best practices for
• accelerating queries. This course is one in a collection that
• prepares learners for the Data Engineering on Microsoft Azure
• (DP-203) exam.

• Implementing an effective serving layer requires consideration

• for the design, methods, and tools. In this course, you'll learn
• how traditional relational models can be replaced by the star
• schema and how to design a star schema. Then, you'll explore the
• purpose and structure of Parquet files used by Azure Databricks.
• You'll learn how to design and query a dimensional hierarchy.
• You'll move on to examine Azure Synapse Analytics, including
• deploying dedicated SQL pools and Apache Spark clusters. Finally
• you'll learn how to create shared metadata tables between Spark
• clusters. This course is one in a collection that prepares learners
• for the Microsoft Data Engineering on Microsoft Azure (DP-203)
• exam.

• Data policies and standards help to ensure a repeatable security

• standard is maintained. In this course, you'll learn about data
• encryption scenarios and best practices. You'll explore how Azure
• Transparent Database Encryption and Always Encrypted can be used to
• ensure data at rest is protected. Next, you'll examine how data
• classification and data masking can protect data being viewed.
• You'll learn to configure data retention and purging to ensure data
• is retained or removed. You'll also explore the various means of
• controlling access to Azure Data Lake Storage Gen2. Finally, you'll
• learn how to plan a data auditing strategy and how to limit access
• to data at the row level in a database. This course is one in a
• collection that prepares learners for the Microsoft Data
• Engineering on Microsoft Azure (DP-203) exam.

• Securing access to data is a fundamental part of any security

• strategy. In this course, you'll explore how Azure Key Vault can be
• used to store and manage keys and secrets for accessing data.
• You'll discover how to connect to Azure resources through private
• and service endpoints and managed virtual networks and how to use
• Azure managed identities for connections between Azure resources.
• Next, you'll learn how to utilize access control lists and Azure
• role-based access control to provide only the necessary permissions
• to users to access your data. You'll also learn how token-based
• authentication works in Azure Databricks. Finally, you'll examine
• how to audit an Azure SQL Database to monitor for unauthorized
• access. This course is one in a collection that prepares learners
• for the Microsoft Data Engineering on Microsoft Azure (DP-203)
• exam.

The final line of defense for protecting against a data breach is securing the data itself. With today's cloud environments, data is often in transit, duplicated, and stored in various data centers around the world, making effective data protection a challenge.

In this course, you'll explore the various methods available for encrypting data stored in SQL databases. You'll examine how to use DataFrames in Databricks, as well as how to implement Advanced Threat Protection and dynamic data masking in Azure databases. Finally, you'll learn how immutable blobs can be used to manage sensitive information.

This course is one in a collection that prepares learners for the Microsoft Data Engineering on Microsoft Azure (DP-203) exam.

• Azure Data Lake Storage Gen2 provides features to work with big
• data analytics using Azure Blob Storage. Azure Blob Storage systems
• provide performance, management, and security functionality. In
• this course, you'll learn about the features of the Azure Data Lake
• Storage Gen2 and when to use this storage type. You'll explore
• features and methods for securing data for the Azure Data Lake
• Storage Gen2 service and data at rest. You'll examine methods for
• processing big data using the Azure Data Lake Storage Gen2 service
• and monitoring Azure Blob Storage. You'll learn how to manage
• directories, files, and Access Control Lists in Azure Data Lake
• Storage Gen2 using the .NET framework, as well as how to perform
• extract, transform, and load operations using Azure Databricks from
• Azure Data Lake Storage Gen2. Finally, you'll learn how to access
• Azure Data Lake Storage Gen2 data using Azure Databricks and Spark.
• This course is one in a collection that prepares learners for the
• Microsoft Data Engineering on Microsoft Azure (DP-203) exam.

• One of the key components of the Azure Cloud platform is the

• ability to store and process large amounts of data. Azure Data Flow
• Transformations can be used to ingest and transform data. In this
• course, you'll learn about the types of Azure Data Flow
• transformations that are available. You'll explore how to
• transform, split, and flatten data, as well as handle duplicate
• data, using Azure Data Mapping Data Flows. Next, you'll examine the
• types of expression functions available in Azure Data Flow and how
• to perform error handling for data rows that would truncate data.
• Finally, you'll learn how to transform and use derived columns to
• normalize data values, and how to ingest and transform data using
• Azure Spark and Scala. This course is one in a collection that
• prepares learners for the Microsoft Data Engineering on Microsoft
• Azure (DP-203) exam.

• Once you have data in storage, you'll need to have some

• mechanism for transforming the data into a usable format. Azure
• Data Factory is a data integration service that is used to create
• automated data pipelines that can be used to copy and transform
• data. In this course, you'll learn about the Azure Data Factory and
• the Integration Runtime. You'll explore the features of the Azure
• Data Factory such as linked services and datasets, pipelines and
• activities, and triggers. Finally, you'll learn how to create an
• Azure Data Factory using the Azure portal, create Azure Data
• Factory linked services and datasets, create Azure Data Factory
• pipelines and activities, and trigger the pipeline manually or
• using a schedule. This course is one in a collection that prepares
• learners for the Microsoft Data Engineering on Microsoft Azure
• (DP-203) exam.

• When working with big data, there needs to be a mechanism to

• process and transform this data quickly and efficiently. Azure
• Databricks is a service that provides the latest version of Apache
• Spark, which provides functionality for machine learning and data
• warehousing. In this course, you'll learn about the features of
• Azure Databricks such as clusters, notebooks, and jobs. Next,
• you'll learn about autoscaling local storage when configuring
• clusters. Next, you'll explore how to create, manage, and configure
• Azure Databricks clusters, as well as how to create, open, use, and
• delete notebooks. Finally, you'll learn how to create, open, use,
• and delete jobs. This course is one in a collection that prepares
• learners for the Microsoft Data Engineering on Microsoft Azure
• (DP-203) exam.

• When working with big data there needs to be a mechanism to
• process and transform this data quickly and efficiently. Azure
• Databricks is a service that provides the latest version of Apache
• Spark that provides functionality processing data from Azure
• Storage. In this course, you will learn about the types of
• processing that can be performed with Azure Databricks such as
• stream, batch, image and parallel processing. Next, you'll learn
• how to create an Azure Databricks workspace using an Apache Spark
• cluster, run jobs in the Azure Databricks Workspace jobs using a
• service principal and query data in SQL server using an Azure
• Databricks notebook. Next, you'll learn how to retrieve data from
• an Azure Blob Storage using Azure Databricks and the Azure Key
• Vault, implement a Cosmos DB service endpoint for Azure Databricks,
• and extract, transform, and load data using Azure Databricks.
• Finally, you'll learn how to stream data into Azure Databricks by
• using Event Hubs and perform sentiment analysis for steam data by
• making use of Azure Databricks. This course is one in a collection
• that prepares learners for the Microsoft Data Engineering on
• Microsoft Azure (DP-203) exam.

• Azure Stream Analytics is a complex, serverless, and highly

• scalable processing engine that can be used to perform real-time
• analytics on multiple data streams. Alerts can be configured to
• forecast trends, trigger workflows, and detect irregularities. In
• this course, you'll learn to use Azure Stream Analytics to process
• streaming data. You'll examine how to implement security, create
• user-defined functions, and optimize jobs for Azure Stream
• Analytics, as well as explore the available inputs and outputs.
• Finally, you'll learn how to create an Azure Stream Analytics job,
• create an Azure Stream Analytics dedicated cluster, run Azure
• Functions from Azure Stream Analytics jobs, and monitor Azure
• Stream Analytics jobs. This course is one in a collection that
• prepares learners for the Microsoft Data Engineering on Microsoft
• Azure (DP-203) exam.

• Azure Synapse Analytics is an analytics service that provides

• functionality for data integration, enterprise data warehousing,
• and big data analytics. Services provided include ingesting,
• exploring, preparing, managing, and serving data for BI and machine
• learning needs. In this course, you'll learn about the Azure
• Synapse Analytics platform and how it is used for data warehousing
• and big data analytics. Next, you'll learn how to create a Synapse
• Workspace, a dedicated SQL pool, and a serverless Apache Spark
• pool. You'll move on to explore how to analyze data using a
• dedicated SQL pool, Apache Spark for Azure Synapse, Serverless SQL
• Pools, and a Spark database, as well as how to analyze data that is
• in a storage account. You'll learn how to integrate pipelines using
• Synapse Studio, visualize data using a Power BI workspace, and
• monitor a Synapse Workspace. Finally, you'll learn about the
• Synapse Knowledge Center and the features of Azure Synapse
• Analytics and PolyBase. This course is one in a collection that
• prepares learners for the Microsoft Data Engineering on Microsoft
• Azure (DP-203) exam.

• Being able to monitor data storage systems to ensure they are

• operational and working correctly is a crucial part of running your
• business. Azure provides the Azure Monitor service and the Azure
• Log Analytics service to perform this function. In this course,
• you'll learn about the features of Azure Log Analytics, as well as
• the Azure Monitor service and how it can be used to monitor storage
• data and monitor Azure Blob storage. Next, you'll explore how to
• access diagnostic logs to monitor Data Lake Storage Gen2, monitor
• the Azure Synapse Analytics jobs and the adaptive cache, and
• monitor Azure Cosmos DB using the portal and resource logs.
• Finally, you'll examine how to configure, manage, and view metric
• alerts using the Azure Monitor and activity log alerts using the
• Azure Monitor. This course is one in a collection that prepares
• learners for the Microsoft Data Engineering on Microsoft Azure
• (DP-203) exam.

• Being able to monitor data processes to ensure they are

• operational and working correctly is a crucial part of running your
• business. Azure provides the Azure Monitor service and the Azure
• Log Analytics service to perform this function. In this course,
• you'll learn about the features of the Azure Monitor tools and the
• concepts of continuous monitoring and visualization. Next, you'll
• examine how to create metric charts using the Azure Monitor, as
• well as how to collect and analyze Azure resource log data and
• perform queries against the Azure Monitor logs. You'll explore how
• to create and share dashboards that display data from Log
• Analytics, create Azure Monitor alerts, and use the Azure Data
• Factory Analytics solution to monitor pipelines. You'll learn how
• to send the Azure Databricks logs to the Azure Monitor and use the
• dashboard to analyze the Azure Databricks metrics. Finally, you'll
• learn how to enable monitoring for Azure Stream Analytics and
• configure alerts, and also query Azure Log Analytics and filter,
• sort, and group query results. This course is one in a collection
• that prepares learners for the Microsoft Data Engineering on
• Microsoft Azure (DP-203) exam.

• Ensuring that data storage and processing systems are operating

• efficiently will allow your organization to save both time and
• money. There are several tips and tricks that can be used to
• optimize both Azure Data Storage service and processes. In this
• course, you'll learn about cloud optimization, as well as some best
• practices for optimizing data using data partitions, Azure Data
• Lake Storage tuning, Azure Synapse Analytics tuning, and Azure
• Databricks auto-optimizing. Next, you'll learn about strategies for
• partitioning data using Azure-based storage solutions. You'll learn
• about the stages of the Azure Blob lifecycle management and how to
• optimize Azure Data Lake Storage Gen2, Azure Stream Analytics, and
• Azure Synapse Analytics. Finally, you'll explore how to optimize
• Azure Data Storage services such Azure Cosmos DB using indexing and
• partitioning, as well as Azure Blob Storage and Azure Databricks.
• This course is one in a collection that prepares learners for the
• Microsoft Data Engineering on Microsoft Azure (DP-203) exam.