This Data Engineering on Microsoft Azure (DP-203) course will introduce you to data engineering models and practices within the context of real-time and batch analytical solutions using Azure data platform technologies.
You will learn about the fundamental computing and storage technologies used to build an analytical solution and then explore how to design analytical service layers, focusing on data engineering considerations for working with source files.
Through this Data Engineering on Microsoft Azure course, you will learn to interactively explore data stored in files in a data lake, the different ingestion techniques that can be used to load data using Apache Spark functionality in Azure Synapse Analytics or Azure Databricks, or how to ingest data using Azure Data Factory or Azure Synapse pipelines.
You will also cover the various ways to transform data using the same technologies used for data acquisition and learn how to monitor and analyze the performance of analytical systems to optimize the performance of data loads or queries on the systems. Finally, you will understand the importance of implementing security to ensure data protection at rest or in transit.
Module 1: Explore compute and storage options for data engineering workloads
Module 2: Run interactive queries using Azure Synapse Analytics serverless SQL pools
Module 3: Data exploration and transformation in Azure Databricks
Module 4: Explore, transform, and load data into the Data Warehouse using Apache Spark
Module 5: Ingest and load data into the data warehouse
Module 6: Transform data with Azure Data Factory or Azure Synapse Pipelines
Module 7: Orchestrate data movement and transformation in Azure Synapse Pipelines
Module 8: End-to-end security with Azure Synapse Analytics
Module 9: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link
Module 10: Real-time Stream Processing with Stream Analytics
Module 11: Create a Stream Processing Solution with Event Hubs and Azure Databricks
Official Microsoft Labs
The Data Engineering on Microsoft Azure (DP-203) training offers you a unique opportunity to master the essential Azure tools and services for data engineering. Here is an overview of the main aspects of this training and the skills you will acquire.
Azure Synapse Analytics is an end-to-end solution for ingesting, preparing, managing, and providing data for immediate BI and machine learning needs. You will discover how this unified platform combines big data processing technologies and data warehouses, making it easier to manage complex analytical workloads.
Azure Databricks, based on Apache Spark, is designed for Big Data analytics. It allows data engineers to collaborate effectively while easily integrating with other Azure services for end-to-end analytics.
Data storage is crucial for any data infrastructure. Azure Data Lake offers a scalable and high-performance solution for storing and managing large amounts of structured and unstructured data. You will learn how to leverage its capabilities to optimize your data engineering workloads.
The Delta Lake architecture combines the advantages of data lakes and data warehouses, enabling more efficient data management and improved query performance. Additionally, with Azure Stream Analytics, you will be able to analyze real-time data streams, which is essential for gaining instant insights and making informed decisions.
A data engineer on Azure must be able to design optimized multidimensional schemas for analytical workloads. This training will help you understand how to create robust data architectures that support complex analytics while ensuring scalability and performance.
Data security is a priority. You will learn how to secure your data warehouses using Azure’s best practices, including Azure Key Vault for managing secrets and keys. The training also covers necessary compliance controls to protect sensitive data.
Data orchestration and transformation are essential for any effective data solution. With Azure Data Factory and Azure Synapse pipelines, you will be able to automate and manage large-scale data flows, using no-code transformations to simplify processes.
Query performance optimization is crucial for ensuring fast response times and efficient resource usage. You will learn techniques to optimize query performance in Azure Synapse Analytics, which is essential for effectively managing data warehouses.
Hybrid Transactional and Analytical Processing (HTAP) is supported by Azure Synapse Link, which allows direct connection of transactional databases with analytical services. This integration enables real-time analytics without impacting transactional operations.
Creating interactive reports with Power BI integrated into Azure Synapse Analytics will enable you to visualize and explore your data intuitively, facilitating data-driven decision-making with up-to-date and accurate data.
What is DP-203 in Azure?
DP-203 is a certification exam for data engineers on Microsoft Azure. It assesses your skills in designing and implementing data management solutions using Azure services such as Azure Synapse Analytics, Azure Data Factory, and Azure Databricks.
What does a Microsoft Azure Data Engineer do?
A data engineer on Microsoft Azure is responsible for designing, implementing, and managing data solutions that enable organizations to collect, store, transform, and analyze large amounts of data. They use Azure tools and services to automate data flows, ensure security, and optimize data system performance.
By following this Data Engineering on Microsoft Azure (DP-203) training, you will acquire the necessary skills to excel as a data engineer on Azure, preparing you to tackle the challenges of modern data engineering.
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