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Exam DP-203 Data Engineering on Microsoft Azure (Video)

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  • Copyright 2023
  • Edition: 1st
  • Online Video
  • ISBN-10: 0-13-783194-3
  • ISBN-13: 978-0-13-783194-4

12+ Hours of Video Instruction

Any IT professional who works with data inside Microsoft Azure should prepare and sit for Microsoft Exam DP-203 in order to demonstrate their knowledge of Azure data engineering. Azure data engineers help stakeholders understand data through exploration, and they build and maintain secure and compliant data-processing pipelines by using different tools and techniques. These professionals use various Azure data services and languages to store and produce cleansed and enhanced datasets for analysis.


Microsoft MVP and Microsoft Certified Azure Solutions Architect Tim Warner walks you through what to expect on the DP-203 Data Engineering on Microsoft Azure exam. The new Azure certifications align to industry job roles; earning Azure certification both validates your specific Azure skillset and increases your value in today's IT job market.

Azure data engineers help ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a set of business requirements and constraints. They deal with unanticipated issues swiftly, and they minimize data loss. They also design, implement, monitor, and optimize data platforms to meet data pipelines needs.

This training course covers every Exam DP-203 objective in a friendly and logical way.

Skill Level

  • Intermediate to Advanced

What You Will Learn

After completing this video, you will be able to:

  • Design and implement data storage
  • Design and implement data processing
  • Design and implement data security
  • Monitor and optimize data storage and data processing

Who Should Take This Course

  • Microsoft Certified Data Engineer
  • Any IT professional looking to pass the DP-203 exam

Course Requirement Prerequisite:

  • A candidate for this exam must have strong knowledge of data-processing languages such as SQL, Python, or Scala, and they need to understand parallel processing and data architecture patterns.

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Sample Content

Table of Contents


Module 1: Design & Implement Data Storage

Lesson 1: Design a Data Storage Structure

1.1 Design an Azure Data Lake solution

1.2 Recommend file types for storage

1.3 Recommend file types for analytical queries

1.4 Design for efficient querying

Lesson 2: Design for Data Pruning

2.1 Design a folder structure that represents levels of data transformation

2.2 Design a distribution strategy

2.3 Design a data archiving solution

Lesson 3: Design a Partition Strategy

3.1 Design a partition strategy for files

3.2 Design a partition strategy for analytical workloads

3.3 Design a partition strategy for efficiency/performance

3.4 Design a partition strategy for Azure Synapse Analytics

3.5 Identify when partitioning is needed in Azure Data Lake Storage Gen2

Lesson 4: Design the Serving Layer

4.1 Design star schemas

4.2 Design slowly changing dimensions

4.3 Design a dimensional hierarchy

4.4 Design a solution for temporal data

4.5 Design for incremental loading

4.6 Design analytical stores

4.7 Design metastores in Azure Synapse Analytics & Azure Databricks

Lesson 5: Implement Physical Data Storage Structures

5.1 Implement compression

5.2 Implement partitioning

5.3 Implement sharding

5.4 Implement different table geometries with Azure Synapse Analytics pools

5.5 Implement data redundancy

5.6 Implement distributions

5.7 Implement data archiving

Lesson 6: Implement Logical Data Structures

6.1 Build a temporal data solution

6.2 Build a slowly changing dimension

6.3 Build a logical folder structure

6.4 Build external tables

6.5 Implement file & folder structures for efficient querying and data pruning

Lesson 7: Implement the Serving Layer

7.1 Deliver data in a relational star schema

7.2 Deliver data in Parquet files

7.3 Maintain metadata

7.4 Implement a dimensional hierarchy

Module 2: Design & Develop Data Processing

Lesson 8: Ingest & Transform Data

8.1 Transform data by using Apache Spark

8.2 Transform data by using Transact-SQL

8.3 Transform data by using Data Factory

8.4 Transform data by using Azure Synapse Pipelines

8.5 Transform data by using Stream Analytics

Lesson 9: Work with Transformed Data

9.1 Cleanse data

9.2 Split data

9.3 Shred JSON

9.4 Encode & decode data

Lesson 10: Troubleshoot Data Transformations

10.1 Configure error handling for the transformation

10.2 Normalize & denormalize values

10.3 Transform data by using Scala

10.4 Perform data exploratory analysis

Lesson 11: Design a Batch Processing Solution

11.1 Develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, & Azure Databricks

11.2 Create data pipelines

11.3 Design & implement incremental data loads

11.4 Design & develop slowly changing dimensions

11.5 Handle security & compliance requirements

11.6 Scale resources

Lesson 12: Develop a Batch Processing Solution

12.1 Configure the batch size

12.2 Design & create tests for data pipelines

12.3 Integrate Jupyter/Python notebooks into a data pipeline

12.4 Handle duplicate data

12.5 Handle missing data

12.6 Handle late-arriving data

Lesson 13: Configure a Batch Processing Solution

13.1 Upsert data

13.2 Regress to a previous state

13.3 Design & configure exception handling

13.4 Configure batch retention

13.5 Revisit batch processing solution design

13.6 Debug Spark jobs by using the Spark UI

Lesson 14: Design a Stream Processing Solution

14.1 Develop a stream processing solution by using Stream Analytics, Azure Databricks, & Azure Event Hubs

14.2 Process data by using Spark structured streaming

14.3 Monitor for performance & functional regressions

14.4 Design & create windowed aggregates

14.5 Handle schema drift

Lesson 15: Process Data in a Stream Processing Solution

15.1 Process time series data

15.2 Process across partitions

15.3 Process within one partition

15.4 Configure checkpoints/watermarking during processing

15.5 Scale resources

15.6 Design & create tests for data pipelines

15.7 Optimize pipelines for analytical or transactional purposes

Lesson 16: Troubleshoot a Stream Processing Solution

16.1 Handle interruptions

16.2 Design & configure exception handling

16.3 Upsert data

16.4 Replay archived stream data

16.5 Design a stream processing solution

Lesson 17: Manage Batches and Pipelines

17.1 Trigger batches

17.2 Handle failed batch loads

17.3 Validate batch loads

17.4 Manage data pipelines in Data Factory/Synapse Pipelines

17.5 Schedule data pipelines in Data Factory/Synapse Pipelines

17.6 Implement version control for pipeline artifacts

17.7 Manage Spark jobs in a pipeline

Module 3: Design & Implement Data Security

Lesson 18: Design Security for Data Policies

18.1 Design data encryption for data at rest and in transit

18.2 Design a data auditing strategy

18.3 Design a data masking strategy

18.4 Design for data privacy

Lesson 19: Design Security for Data Standards

19.1 Design a data retention policy

19.2 Design to purge data based on business requirements

19.3 Design Azure RBAC & POSIX-like ACL for Data Lake Storage Gen2

19.4 Design row-level & column-level security

Lesson 20: Implement Data Security Protection

20.1 Implement data masking

20.2 Encrypt data at rest & in motion

20.3 Implement row-level & column-level security

20.4 Implement Azure RBAC

20.5 Implement POSIX-like ACLs for Data Lake Storage Gen2

20.6 Implement a data retention policy

20.7 Implement a data auditing strategy

Lesson 21: Implement Data Security Access

21.1 Manage identities, keys, & secrets across different data platforms

21.2 Implement secure endpoints (private & public)

21.3 Implement resource tokens in Azure Databricks

21.4 Load a DataFrame with sensitive information

21.5 Write encrypted data to tables or Parquet files

21.6 Manage sensitive information

Module 4: Monitor & Optimize Data Storage & Data Processing

Lesson 22: Monitor Data Storage

22.1 Implement logging used by Azure Monitor

22.2 Configure monitoring services

22.3 Measure performance of data movement

22.4 Monitor & update statistics about data across a system

22.5 Monitor data pipeline performance

22.6 Measure query performance

Lesson 23: Monitor Data Processing

23.1 Monitor cluster performance

23.2 Understand custom logging options

23.3 Schedule & monitor pipeline tests

23.4 Interpret Azure Monitor metrics & logs

23.5 Interpret a Spark Directed Acyclic Graph (DAG)

Lesson 24: Tune Data Storage

24.1 Compact small files

24.2 Rewrite user-defined functions (UDFs)

24.3 Handle skew in data

24.4 Handle data spill

24.5 Tune shuffle partitions

24.6 Find shuffling in a pipeline

24.7 Optimize resource management

Lesson 25: Optimize & Troubleshoot Data Processing

25.1 Tune queries by using indexers

25.2 Tune queries by using cache

25.3 Optimize pipelines for analytical or transactional purposes

25.4 Optimize pipeline for descriptive versus analytical workloads

25.5 Troubleshoot failed Spark jobs

25.6 Troubleshoot failed pipeline runs


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