DP-203 Data Engineering on Microsoft Azure Training - OEM Certkit

• Language: English
• Target Audience: Data engineers, data architects, database administrators and other professionals responsible for designing, implementing and managing data solutions on Azure.
• Required Knowledge Level: To take full advantage of the DP-203 training and its exam, it is essential to have a solid foundation in data engineering and be familiar with the various Azure services. This knowledge will enable you to better understand and effectively apply the concepts and techniques covered during the training in real-world situations. A solid foundation in data engineering and familiarity with Azure services will help you overcome the complexities of DP-203 training and be successful on the exam.
• Course duration:26:09 hours (depending on personal speed)
• Online access:24/7, 365 days after activation
• Award Winning E-learning
• Contact with instructor by email and chat (Online Mentor)
• Complete training for the official Exam
• User-friendly environment
• Certificate of Participation included

DP-203 Data Engineering on Microsoft Azure Training

Once you have data in storage, you need a mechanism to transform the data into a usable format. Azure Data Factory is a data integration service used to create automated data pipelines that can be used to copy and transform data.

In this course, you will learn about Azure Data Factory and the Integration Runtime. You will explore Azure Data Factory features such as linked services and datasets, pipelines and activities, and triggers. Finally, you will learn how to create an Azure Data Factory using the Azure portal, create Azure Data Factory-linked services and data sets, create Azure Data Factory pipelines and activities, and trigger the pipeline manually or with scheduling.

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

CertKit content:

E-learning courses:

DP-203 - Data Engineering on Microsoft Azure: Storage Accounts

Course: 1 Hour, 12 Minutes

• Course Overview
• Azure Blob Storage
• Blob Storage Performance and Scalability
• Azure Storage Geo-redundancy
• Storage Account Disaster Recovery
• Azure Data Lake Storage Gen
• Big Data Processing with Data Lake
• Planning a Data Lake
• Data Lake Storage Best Practices
• Creating a Data Lake Gen2 Account
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Designing Data Storage Structures

Course: 1 Hour, 9 Minutes

• Course Overview
• Designing a Data Lake
• Big Data File Type Planning
• Big Data File Formats
• Designing Table Storage for Querying
• Dynamic Data Pruning
• Designing a Folder Structure
• Data Lake Zones
• Storage Archiving Tier
• Data Archiving, Rehydrating, and Life Cycle Management
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Partitioning

Course: 1 Hour, 3 Minutes

• Course Overview
• Partitioning Data
• Data Partitioning Strategies
• Transactional and Analytical Workloads
• File Partition Strategies
• Partitioning Strategies for Table Storage
• Designing for Efficiency and Performance
• Partitioning Azure Synapse Analytics Dedicated Pools
• Partitioning Data Lakes
• Data Sharding for Scaling
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Designing the Serving Layer

Course: 1 Hour, 13 Minutes

• Course Overview
• Dimensional Modeling
• Dimensional Hierarchies
• Slowly Changing Dimensions
• Temporal Design
• Star and Snowflake Schemas
• Designing a Star Schema
• Implementing Incremental Data Loading
• Choosing an Analytical Data Store
• Metastore Design
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Physical Data Storage Structures

Course: 1 Hour, 5 Minutes

• Course Overview
• Data Compression
• Partitioning Data
• Sharding Data
• Designing Tables
• Ensuring Database Data Redundancy
• File and Blob Data Redundancy
• Implementing Distribution Schemes
• Implementing Data Archiving
• Configuring Database Long-term Retention
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Logical Data Structures

Course: 1 Hour, 32 Minutes

• Course Overview
• Data Lake Maturity
• Temporal Database Tables
• Managing Temporal Tables
• Slowly Changing Dimensions
• Building a Slowly Changing Dimension Type
• Building a Slowly Changing Dimension Type
• Folder Structures
• Using External Tables
• Accelerating Queries
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: The Serving Layer

Course: 1 Hour, 5 Minutes

• Course Overview
• Relational and Star Schemas
• Designing a Star Schema Database
• Parquet Files
• Dimensional Hierarchies
• Deploying Azure Synapse Analytics
• Deploying Azure Synapse Analytics Dedicated SQL Pools
• Deploying Azure Synapse Analytics Apache Spark Clusters
• Shared Metadata Tables
• Implementing Shared Metadata Tables
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Policies & Standards

Course: 1 Hour, 7 Minutes

• Course Overview
• Azure Key Vault
• Private and Service Endpoints
• Using Managed Virtual Networks and Private Endpoints
• Azure Managed Identities
• Managing Access Control Lists
• Using Azure Role-based Access Control
• Azure Databricks Token-based Authentication
• Implement Azure Databricks Token Authentication
• Managing Temporal Table Retention Policies
• Auditing on an Azure SQL Database
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Securing Data Access

Course: 1 Hour, 7 Minutes

• Course Overview
• Azure Key Vault
• Private and Service Endpoints
• Using Managed Virtual Networks and Private Endpoints
• Azure Managed Identities
• Managing Access Control Lists
• Using Azure Role-based Access Control
• Azure Databricks Token-based Authentication
• Implement Azure Databricks Token Authentication
• Managing Temporal Table Retention Policies
• Auditing on an Azure SQL Database
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Securing Data

Course: 1 Hour, 22 Minutes

• Course Overview
• Azure Data Encryption
• Managing Transparent Data Encryption
• Database Encryption
• Implementing Always Encrypted
• Encrypting Database Columns
• Applying Row-level Security
• Working with DataFrames
• Securing Azure Database Data
• Utilizing Immutable Blobs
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Lake Storage

Course: 1 Hour, 48 Minutes

• Course Overview
• Azure Data Lake Storage Gen
• Creating a Data Lake Storage Gen2 Storage Account
• Managing Azure Data Lake Storage Gen
• Extracting Data from Azure Data Lake Storage Gen
• Transforming Data Using Apache Spark
• Transforming Data Using Data Factory
• Transforming Azure Synapse Pipelines
• Streaming Data to Azure Databricks
• Transforming Data with Databricks
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Flow Transformations

Course: 1 Hour, 24 Minutes

• Course Overview
• Azure Data Flow Transformations
• Using Azure Data Mapping Data Flows
• Performing an Azure Data Flow Conditional Split
• Using Azure Data Flow Conditional Flatten
• Azure Data Flow Expression Functions
• Configuring Azure Data Flow Error Handling
• Transforming Azure Data Flow Derived Columns
• Transforming Data Using Azure Spark and Scala
• Handling Duplicate Data in Azure Data Flows
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Factory

Course: 2 Hours, 24 Minutes

• Course Overview
• Azure Data Factory Triggers
• Creating an Azure Data Factory
• Handling Schema Drift in Data Flow
• Creating Data Factory Pipelines
• Creating Data Factory Triggers
• Optimizing Pipelines in the Data Factory Pipeline
• Utilizing Upsert with Azure SQL
• Troubleshooting in Azure Stream Analytics
• Designing and Implementing Incremental Data Loads
• Designing and Developing Slowly Changing Dimensions
• Securing Data in Azure Data Factory
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Databricks

Course: 1 Hour, 3 Minutes

• Course Overview
• Partitioning Data
• Data Partitioning Strategies
• Transactional and Analytical Workloads
• File Partition Strategies
• Partitioning Strategies for Table Storage
• Designing for Efficiency and Performance
• Partitioning Azure Synapse Analytics Dedicated Pools
• Partitioning Data Lakes
• Data Sharding for Scaling
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Databrick Processing

Course: 1 Hour, 50 Minutes

• Course Overview
• Azure Databricks Processing
• Creating an Azure Databricks Workspace
• Running Azure Databricks Workspace Jobs
• Querying SQL Server
• Failed Batch Loads
• Implementing Cosmos DB Endpoints
• Extracting, Transforming, and Loading Data
• Performing Sentiment Analysis
• Debugging Spark Job
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Stream Analytics

Course: 1 Hour, 12 Minutes

• Course Overview
• Azure Stream Analytics
• Azure Stream Analytics Inputs
• Azure Stream Analytics Outputs
• Azure Stream Analytics User-defined Functions
• Processing Data by Using Spark Structured Streaming
• Stream Analytics Windowing Functions
• Setting Up and Processing Time Series Data
• Creating a Stream Analytics Job
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Synapse Analytics

Course: 46 Minutes

• Course Overview
• Azure Synapse Analytics
• Integrating With Pipelines
• Visualizing Data Using Power BI
• Monitoring a Synapse Workspace
• Exploring the Synapse Knowledge Center
• Azure Synapse Analytics and PolyBase
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Storage Monitoring

Course: 58 Minutes

• Course Overview
• Azure Data Storage Monitoring
• Monitoring Azure Blob Storage
• Monitoring Data Lake Storage Gen
• Monitoring Azure Synapse Analytics
• Monitoring Cosmos DB
• Configuring Azure Monitoring Alerts
• Azure Log Analytics
• Configuring Azure Log Analytics
• Monitoring Azure Stream Analytics
• Azure Cosmos DB Indexing
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Process Monitoring

Course: 1 Hour, 31 Minutes

• Course Overview
• Azure Monitor
• Creating Azure Monitor Metrics
• Analyzing Azure Resource Logs
• Querying Azure Monitor Logs
• Creating Log Analytics Dashboards
• Azure HDInsight
• Monitoring Data Factory Pipelines
• Monitoring and Tuning Query Performance
• Monitoring Azure Log Analytics
• Azure Data Factory Version Control
• Azure Monitor Cluster Monitoring
• Azure Monitor Custom Logging
• Azure Monitor Logic App
• Course Summary

DP-203 - Data Engineering on Microsoft Azure: Data Solution Optimization

Course: 58 Minutes

• Course Overview
• Azure Data Solution Optimization
• Azure Stream Analytics Optimization
• Data Partitioning Strategies
• Azure Data Lake Storage Gen2 Optimization
• Azure Synapse Analytics Optimization
• Azure Cosmos DB Optimization
• Azure Databricks Monitoring
• Azure Databricks Optimization
• Azure Data Troubleshooting
• Course Summary

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• 190+ questions

Online mentor

The online mentor can answer all your specific technical questions via chat or email related to your studies.
Online Mentor is active for 90 days or 365 days - after activation!

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What is DP-203 Data Engineering on Microsoft Azure Training?

DP-203, also known as "Data Engineering on Microsoft Azure: Data Policies & Standards," is a training course designed to train professionals in applying data policies and standards in a Microsoft Azure environment. This training focuses on the best practices and techniques needed to design and implement effective data engineering solutions with attention to data governance, privacy, compliance and security.

During DP-203 training, participants will learn how to define and implement data policies and standards in Azure services such as Azure Data Factory, Azure Databricks and Azure SQL Database. They will also learn how to ensure data security, ensure data privacy and meet regulatory and compliance requirements.

DP-203 training includes hands-on exercises, scenarios and case studies to help participants apply the concepts learned in real-world situations. The ultimate goal of the training is to equip professionals with the knowledge and skills needed to manage and process data securely and compliantly within Azure environments.

After completing the training, participants will have the opportunity to take the DP-203 exam, resulting in a certification in data engineering on Microsoft Azure: Data Policies & Standards. This certification recognizes the proficiency and expertise of professionals in implementing data policies and standards in Azure environments.

10 benefits of DP-203 Data Engineering on Microsoft Azure Training

1. In-depth understanding of data policies and standards: The training provides a thorough understanding of data policies and standards required to effectively and securely manage data in Azure.
2. Knowledge of best practices: The training covers best practices in data engineering and helps professionals identify and implement optimal solutions.
3. Data governance skills: Training focuses on understanding and applying data governance principles to ensure data management and security in Azure.
4. Compliance and regulation: Professionals learn how to meet compliance and regulatory requirements related to data management and privacy.
5. Data security: The training covers techniques and mechanisms to secure and protect data from unauthorized access and breaches.
6. Hands-on experience: The training includes hands-on exercises and case studies, allowing professionals to gain practical experience implementing data policies and standards in Azure.
7. Understanding Azure services: The training focuses specifically on using Azure services such as Azure Data Factory, Azure Databricks and Azure SQL Database, allowing professionals to become familiar with these tools
8. Career benefit: Earning the DP-203 certification can set professionals apart in the job market and provide them with valuable opportunities for career development and growth.
9. Applicability in various industries: The training is relevant to professionals in various industries because data management and compliance are important in almost every organization.
10. Up-to-date knowledge: The DP-203 training is delivered by certified trainers and takes into account the latest trends and developments in data engineering and Azure data solutions.

Why is DP-203 Data Engineering on Microsoft Azure Training important?

DP-203 - Data Engineering on Microsoft Azure: Data Policies & Standards is important because it focuses on teaching the skills needed to design and implement data solutions in Microsoft Azure, with an emphasis on understanding and applying data management policies and standards. This is important because it helps ensure data security, privacy and compliance in cloud environments, which is becoming increasingly important as more organizations move to cloud solutions.

Passing the DP-203 exam allows candidates to demonstrate that they have the required knowledge and skills to design and implement data solutions that meet policy and standards requirements, which can enhance their career opportunities and help them achieve the Microsoft Certified: Azure Data Engineer Associate certification.

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