Data Engineering on Microsoft Azure

4 Days

Intermediate

Kuala Lumpur

Program Overview

In this DP-203T00 Data Engineering on Microsoft Azure course, the student will learn about the data engineering patterns and practices as it pertains to working with batch and real-time analytical solutions using Azure data platform technologies. Students will begin by
understanding the core compute and storage technologies that are used to build an analytical solution.

They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines.

The students will also learn the various ways they can transform the data using the same
technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems.
They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics.

Programme Module

Introduction to Azure Synapse Analytics

Explore Azure Databricks

Introduction to Azure Data Lake storage

Work with data streams by using Azure Stream Analytics

Use Azure Synapse serverless SQL pool to query files in a data lake

Create a lake database in Azure Synapse Analytics

Secure data and manage users in Azure Synapse serverless SQL pools

Use Apache Spark in Azure Databricks

Use Delta Lake in Azure Databrick

Use Delta Lake in Azure Databricks

Analyze data with Apache Spark in Azure Synapse Analytics

Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Use data loading best practices in Azure Synapse Analytics

Petabyte-scale ingestion with Azure Data Factory or Azure Synapse Pipeline

Integrate data with Azure Data Factory or Azure Synapse Pipeline

Perform code-free transformation at scale with Azure Data Factory or Azure Synapse Pipeline

Orchestrate data movement and transformation in Azure Data Factory or Azure Synapse Pipeline

Plan hybrid transactional and analytical processing using Azure Synapse Analytics

Implement Azure Synapse Link with Azure Cosmos DB

Secure a data warehouse in Azure Synapse Analytics

Configure and manage secrets in Azure Key Vault

Implement compliance controls for sensitive data

Enable reliable messaging for Big Data applications using Azure Event Hubs

Programme Objectives

Program Objectives – DP-203: Data Engineering on Microsoft Azure

Understand core data engineering concepts and architectures for batch and real-time analytics on Azure
Design and implement end-to-end analytical solutions using Microsoft Azure data platform services
Work with and optimize data storage systems, including Azure Data Lake Storage and lakehouse architectures
Process large-scale data using Apache Spark in Azure Databricks and Azure Synapse Analytics
Implement data ingestion pipelines using Azure Data Factory and Azure Synapse Pipelines
Transform, clean, and prepare data using both code-based and no-code approaches
Build and manage data warehouses and analytical serving layers
Enable real-time data processing and streaming analytics using Azure Stream Analytics and Event Hubs
Query and analyze data using serverless SQL pools and Spark SQL
Implement data security, governance, and compliance controls, including Key Vault, masking, RLS, and auditing
Manage hybrid transactional and analytical processing (HTAP) solutions using Azure Synapse and Cosmos DB integration
Apply best practices for performance tuning, monitoring, and optimization of data workloads
Design scalable solutions for petabyte-scale data ingestion and processing
Integrate analytics systems with downstream tools such as dashboards and machine learning models

Who Should Attend

The primary audience for this Azure certification is data professionals, data architects, and
business intelligence professionals who want to learn about data engineering and building
analytical solutions using data platform technologies that exist on Microsoft Azure. The
secondary audience for this course data analysts and data scientists who work with analytical
solutions built on Microsoft Azure

Secure your spot today

Get Certified
– Enroll Now

Speak to a consultant and get clarity before you sign up.