Microsoft Certified: Azure Data Scientist Associate

Microsoft Certified: Azure Data Scientist Associate (DP-100)

The Data Engineering on Microsoft Azure - DP-203 course prepares professionals to design and implement data solutions using Azure data platform technologies. Participants will learn to work with Azure Data Lake Storage Gen2, manage data with Azure Cosm

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Date

09 Jun - 12 Jun

Time

10:00 AM - 6:00 PM (EDT)

$ 350

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10:00 AM - 6:00 PM (EDT)

09 Jun - 12 Jun

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23 Jun - 26 Jun

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Overview

This comprehensive course equips professionals with the skills to design, implement, and manage scalable data solutions using Microsoft Azure’s data platform technologies. Through hands-on labs and real-world scenarios, participants will learn to work with Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure Databricks, Azure Data Factory, and more, preparing them for the Azure Data Engineer role and certification.

Microsoft Certified: Azure Data Scientist Associate Curriculum

Introduction to data engineering on Azure

Identify common data engineering tasks

Describe common data engineering concepts

Identify Azure services for data engineering

Introduction to Azure Data Lake Storage Gen2

Describe the key features and benefits of Azure Data Lake Storage Gen2

Enable Azure Data Lake Storage Gen2 in an Azure Storage account

Compare Azure Data Lake Storage Gen2 and Azure Blob storage

Describe where Azure Data Lake Storage Gen2 fits in the stages of analytical processing

Describe how Azure data Lake Storage Gen2 is used in common analytical workloads

Introduction to Azure Synapse Analytics

Identify the business problems that Azure Synapse Analytics addresses

Describe core capabilities of Azure Synapse Analytics

Determine when to use Azure Synapse Analytics

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

Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics

Query CSV, JSON, and Parquet files using a serverless SQL pool

Create external database objects in a serverless SQL pool

Use Azure Synapse serverless SQL pools to transform data in a data lake

Use a CREATE EXTERNAL TABLE AS SELECT (CETAS) statement to transform data

Encapsulate a CETAS statement in a stored procedure

Include a data transformation stored procedure in a pipeline

Create a lake database in Azure Synapse Analytics

Understand lake database concepts and components

Describe database templates in Azure Synapse Analytics

Create a lake database

Secure data and manage users in Azure Synapse serverless SQL pools

Choose an authentication method in Azure Synapse serverless SQL pools

Manage users in Azure Synapse serverless SQL pools

Manage user permissions in Azure Synapse serverless SQL pools

Analyze data with Apache Spark in Azure Synapse Analytics

Identify core features and capabilities of Apache Spark

Configure a Spark pool in Azure Synapse Analytics

Run code to load, analyze, and visualize data in a Spark notebook

Transform data with Spark in Azure Synapse Analytics

Use Apache Spark to modify and save dataframes

Partition data files for improved performance and scalability

Transform data with SQL

Use Delta Lake in Azure Synapse Analytics

Describe core features and capabilities of Delta Lake

Create and use Delta Lake tables in a Synapse Analytics Spark pool

Create Spark catalog tables for Delta Lake data

Use Delta Lake tables for streaming data

Query Delta Lake tables from a Synapse Analytics SQL pool

Build a data pipeline in Azure Synapse Analytics

Describe core concepts for Azure Synapse Analytics pipelines

Create a pipeline in Azure Synapse Studio

Implement a data flow activity in a pipeline

Initiate and monitor pipeline runs

Use Spark Notebooks in an Azure Synapse Pipeline

Describe notebook and pipeline integration

Use a Synapse notebook activity in a pipeline

Use parameters with a notebook activity

Introduction to Azure Synapse Analytics

Identify the business problems that Azure Synapse Analytics addresses

Describe core capabilities of Azure Synapse Analytics

Determine when to use Azure Synapse Analytics

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

Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics

Query CSV, JSON, and Parquet files using a serverless SQL pool

Create external database objects in a serverless SQL pool

Analyze data with Apache Spark in Azure Synapse Analytics

Identify core features and capabilities of Apache Spark

Configure a Spark pool in Azure Synapse Analytics

Run code to load, analyze, and visualize data in a Spark notebook

Use Delta Lake in Azure Synapse Analytics

Describe core features and capabilities of Delta Lake

Create and use Delta Lake tables in a Synapse Analytics Spark pool

Create Spark catalog tables for Delta Lake data

Use Delta Lake tables for streaming data

Query Delta Lake tables from a Synapse Analytics SQL pool

Analyze data in a relational data warehouse

Design a schema for a relational data warehouse

Create fact, dimension, and staging tables

Use SQL to load data into data warehouse tables

Use SQL to query relational data warehouse tables

Build a data pipeline in Azure Synapse Analytics

Describe core concepts for Azure Synapse Analytics pipelines

Create a pipeline in Azure Synapse Studio

Implement a data flow activity in a pipeline

Initiate and monitor pipeline runs

Analyze data in a relational data warehouse

Design a schema for a relational data warehouse

Create fact, dimension, and staging tables

Use SQL to load data into data warehouse tables

Use SQL to query relational data warehouse tables

Load data into a relational data warehouse

Load staging tables in a data warehouse

Load dimension tables in a data warehouse

Load time dimensions in a data warehouse

Load slowly changing dimensions in a data warehouse

Load fact tables in a data warehouse

Perform post-load optimizations in a data warehouse

Manage and monitor data warehouse activities in Azure Synapse Analytics

Scale compute resources in Azure Synapse Analytics

Pause compute in Azure Synapse Analytics

Manage workloads in Azure Synapse Analytics

Use Azure Advisor to review recommendations

Use Dynamic Management Views to identify and troubleshoot query performance

Secure a data warehouse in Azure Synapse Analytics

Understand network security options for Azure Synapse Analytics

Configure Conditional Access

Configure Authentication

Manage authorization through column and row level security

Manage sensitive data with Dynamic Data masking

Implement encryption in Azure Synapse Analytics

Plan hybrid transactional and analytical processing using Azure Synapse Analytics

Describe Hybrid Transactional / Analytical Processing patterns

Identify Azure Synapse Link services for HTAP

Implement Azure Synapse Link with Azure Cosmos DB

Configure an Azure Cosmos DB Account to use Azure Synapse Link

Create an analytical store enabled container

Create a linked service for Azure Cosmos DB

Analyze linked data using Spark

Analyze linked data using Synapse SQL

Implement Azure Synapse Link for SQL

Understand key concepts and capabilities of Azure Synapse Link for SQL

Configure Azure Synapse Link for Azure SQL Database

Configure Azure Synapse Link for Microsoft SQL Server

Understand data streams

Understand event processing

Understand window functions

Get started with Azure Stream Analytics

Ingest streaming data using Azure Stream Analytics and Azure Synapse Analytics

Describe common stream ingestion scenarios for Azure Synapse Analytics

Configure inputs and outputs for an Azure Stream Analytics job

Define a query to ingest real-time data into Azure Synapse Analytics

Run a job to ingest real-time data, and consume that data in Azure Synapse Analytics

Visualize real-time data with Azure Stream Analytics and Power BI

Configure a Stream Analytics output for Power BI

Use a Stream Analytics query to write data to Power BI

Create a real-time data visualization in Power BI

Introduction to Microsoft Purview

Evaluate whether Microsoft Purview is appropriate for your data discovery and governance needs

Describe how the features of Microsoft Purview work to provide data discovery and governance

Discover trusted data using Microsoft Purview

Browse, search, and manage data catalog assets

Use data catalog assets with Power BI

Use Microsoft Purview in Azure Synapse Studio

Catalog data artifacts by using Microsoft Purview

Describe asset classification in Microsoft Purview

Manage Power BI assets by using Microsoft Purview

Register and scan a Power BI tenant

Use the search and browse functions to find data assets

Describe the schema details and data lineage tracing of Power BI data assets

Integrate Microsoft Purview and Azure Synapse Analytics

Catalog Azure Synapse Analytics database assets in Microsoft Purview

Configure Microsoft Purview integration in Azure Synapse Analytics

Search the Microsoft Purview catalog from Synapse Studio

Track data lineage in Azure Synapse Analytics pipelines activities

Course Benefits

  • Master end-to-end data engineering workflows on Microsoft Azure, from ingestion to analytics.
  • Gain hands-on experience with leading Azure services like Synapse Analytics, Data Lake, and Databricks.
  • Learn to build and optimize data pipelines for both batch and real-time processing.
  • Develop skills to secure, monitor, and govern enterprise data solutions in the cloud.
  • Prepare for the Microsoft DP-203 certification and boost your career opportunities.
  • Flexible, expert-led virtual training with practical labs and industry-recognized certification.
Microsoft Certified: Azure Data Scientist Associate

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Microsoft Certified: Azure Data Scientist Associate

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Course Certification Process

Microsoft Certified: Azure Data Scientist Associate

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  • Complete the instructor-led training course, including hands-on labs and practical exercises.
  • Register for the official Data Engineering on Microsoft Azure certification exam.
  • Pass the exam, which tests your knowledge and skills in designing and implementing data solutions on Azure.
  • Receive your Microsoft Certified: Azure Data Engineer Associate credential upon successful exam completion.

Our Instructor

Gaurav Rajwanshi

Gaurav Rajwanshi

Lean Business Leadership and AI Coach

Amogh Joshi

Amogh Joshi

Director of Agile Product Delivery & Transformation Coach

Ashish Joshi

Ashish Joshi

Business Agility and Transformation Coach

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Microsoft Certified: Azure Data Scientist Associate

Frequently asked questions

- Data Engineering on Microsoft Azure training is ideal for individuals aiming to enhance their skills in designing and implementing data solutions using Azure data platform technologies. It is particularly beneficial for professionals in roles such as Data Scientists, Data Analysts, and Database Analysts, as well as those seeking to enter the data engineering sector. course is also suitable for anyone looking to gain a comprehensive understanding of Azure Data Lake Storage Gen2, Azure Cosmos DB, and serverless SQL pools in Azure Synapse Analytics and achieve Microsoft Azure Technology skills.

Yes, Data Engineering on Microsoft Azure course includes 4 Days (32 Hours) of hands-on labs and practical exercises. These sessions are designed to provide real-world experience in working with Azure Data Lake Storage Gen2, Azure Cosmos DB, serverless SQL pools in Azure Synapse Analytics, Azure Databricks, Azure Data Factory, and Stream Analytics, allowing you to apply theoretical knowledge to practical scenarios. You will work on building and managing data engineering solutions on the Azure cloud, including data ingestion, transformation, and storage to reinforce your learning

Upon completing the Data Engineering on Microsoft Azure training, you can pursue various career opportunities, including Azure Data Engineer, Data Architect, and Cloud Data Engineer. The  course opens doors to roles in organizations utilizing Microsoft Azure for their data infrastructure and analytics, where your skills will be highly valued.

The instructors for the Microsoft course are Microsoft Certified Trainers (MCTs) and industry experts with many years of experience in Azure data platform technologies and data engineering practices. They are selected based on their expertise, teaching experience, and certifications. Our instructors undergo a rigorous selection process to ensure they provide high-quality training.

Yes, you will receive a certificate of completion upon successfully completing training. This certificate verifies your participation and mastery of the course content.

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