Pratique pour l'examen | Ingénierie des données Azure DP-203

Azure Data Engineer: Master Data Integration, Processing & Storage

This course content is designed to prepare you for the challenges of an Azure Data Engineer role. It focuses on the skills needed to build and maintain robust, scalable, and secure data solutions for data analytics. This isn't a direct replica of the official exam questions, but covers the core concepts and technologies. Expect variations in question order and content updates.

Core Responsibilities of an Azure Data Engineer:

As an Azure Data Engineer, you'll be instrumental in helping stakeholders understand data through data exploration and building/maintaining secure and compliant data pipelines. You'll leverage various Azure data services to store and transform data for analysis. Common data architectures you'll work with include:

• Data Warehouse (MDW)
• Lakehouse Architecture
• Big Data Architectures

You'll also be responsible for ensuring the operational efficiency, performance, and reliability of your data pipelines and data stores, addressing operational and data quality issues. This includes designing, implementing, monitoring, and optimizing data platforms.

Essential Skills & Technologies:

A strong foundation in computer languages is crucial:

• SQL
• Python

You need to understand parallel processing models and data architecture. Mastery of the following Azure services is essential for building data processing solutions:

• Azure Data Factory
• Azure Synapse Analytics
• Azure Stream Analytics
• Azure Event Hubs
• Azure Data Lake Storage
• Azure Databricks

Key Skill Areas (Weighting Approximate):

• Data Storage Design & Implementation (15-20%):

  • Implementing partitioning strategies for files, analytical workloads, and streaming workloads.
  • Understanding when partitioning is necessary in Azure Data Lake Storage Gen2.
  • Designing and implementing data exploration layers.
  • Creating and executing queries using SQL (serverless and Spark).
  • Recommending and implementing Azure Synapse Analytics database models.
  • Data lineage tracking with Microsoft Purview.
  • Metadata discovery and search in Microsoft Purview Data Catalog.

• Data Processing Development (40-45%):

  • Data Ingestion & Transformation:
    • Designing and implementing incremental loads.
    • Transforming data with Apache Spark.
    • Transforming data with Transact-SQL (T-SQL) in Azure Synapse Analytics.
    • Ingesting and transforming data with Azure Synapse Pipelines or Azure Data Factory.
    • Transforming data with Azure Stream Analytics.
  • Data Cleaning:
    • Handling duplicate data.
    • Managing missing data.
    • Handling late-arriving data.
    • Data splitting.
    • JSON encoding/decoding.
    • Error handling during transformations.
    • Data normalization and denormalization.
    • Performing data exploratory analysis.
  • Batch Processing:
    • Developing batch processing solutions using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory.
    • Using PolyBase to load data into SQL pools.
    • Implementing Azure Synapse Link for replicated data.
    • Creating data pipelines.
    • Scaling resources (batch).
    • Creating pipeline tests.
    • Integrating Jupyter Notebooks or Python into pipelines.
    • Data insertion, data retrieval, and state management.
    • Exception handling and batch management.
    • Reading and writing to Azure Data Lake Delta.
  • Stream Processing:
    • Creating stream processing solutions using Stream Analytics and Azure Event Hubs.
    • Processing structured streaming data with Spark.
    • Creating windowed aggregations.
    • Handling schema drift.
    • Processing time-series data.
    • Processing data across partitions.
    • Configuring checkpoints and watermarks.
    • Scaling resources (stream).
    • Creating pipeline tests.
    • Optimizing pipelines for analytical or transactional workloads.
    • Managing interruptions and exceptions.
    • Data archival and batch management.
    • Triggering batches and validating loads.
    • Managing pipelines in Azure Data Factory or Azure Synapse Pipelines.
    • Implementing pipeline version control.
    • Managing Spark tasks within pipelines.

• Data Storage Security, Monitoring & Optimization (30-35%):

  • Implementing data security measures.