DP-203: Data Engineering on Microsoft Azure Certification Training

Today, everything depends on thorough data analysis to understand the customer pain points and also to identify new opportunities to gain market share. In such a challenging business landscape, it is critical for individuals and enterprises to know how to integrate, transform, and consolidate data across platforms. This Data Engineering on Microsoft to Azure certification (DP-203) is one such certification that helps professionals to build some of the best analytics solutions using Microsoft Azure as a platform. Check out the dates below to enroll for the DP-203 certification training course.

Key Features

Instructor-led Data Engineering on Microsoft Azure Certification Training
Get access to a DP-203 course preview to begin your preparation
Expert Microsoft instructors across the globe with real-world expertise
Accredited DP-203 course material prepared by SMEs
Data Engineering on Microsoft Training resources provided to learners from Microsoft and Koenig Solutions
DP-203 course completion certificate provided after the training
Learners can take up DP-203 training in 4 different learning modes
Data Engineering on Microsoft Azure training provided across 100+ locations globally

Who can take up Data Engineering on Microsoft Azure Certification Training?

Job roles that can find Data Engineering on Microsoft Azure training include:

Data Professionals
Data Architects
Business Intelligence Professionals
Data Engineers
Business Analysts
Data Analysts
Data Scientists
Aspiring Data Engineers who are looking to build analytical solutions on the Microsoft Azure platform
Professionals who are looking to clear their DP-203 certification exam

Module 1: Explore compute and storage options for data engineering workloads

Introduction to Azure Synapse Analytics
Describe Azure Databricks
Introduction to Azure Data Lake storage
Describe Delta Lake architecture
Work with data streams by using Azure Stream Analytics
Explore compute and storage options for data engineering workloads

Module 2: Design and implement the serving layer

Design a multidimensional schema to optimize analytical workloads
Code-free transformation at scale with Azure Data Factory
Populate slowly changing dimensions in Azure Synapse Analytics pipelines
Designing and Implementing the Serving Layer

Module 3: Data engineering considerations for source files

Design a Modern Data Warehouse using Azure Synapse Analytics
Secure a data warehouse in Azure Synapse Analytics

Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools

Explore Azure Synapse serverless SQL pools capabilities
Query data in the lake using Azure Synapse serverless SQL pools
Secure data and manage users in Azure Synapse serverless SQL pools

Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark

Understand big data engineering with Apache Spark in Azure Synapse Analytics
Ingest data with Apache Spark notebooks in Azure Synapse Analytics
Transform data with Data Frames in Apache Spark Pools in Azure Synapse Analytics
Integrate SQL and Apache Spark pools in Azure Synapse Analytics

Module 6: Data exploration and transformation in Azure Data bricks

Describe Azure Data bricks
Read and write data in Azure Data bricks
Work with Data Frames in Azure Data bricks
Work with Data Frames advanced methods in Azure Data bricks

Module 7: Ingest and load data into the data warehouse

Use data loading best practices in Azure Synapse Analytics
Petabyte-scale ingestion with Azure Data Factory

Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines

Data integration with Azure Data Factory or Azure Synapse Pipelines
Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines

Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines

Orchestrate data movement and transformation in Azure Data Factory

Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse

Optimize data warehouse query performance in Azure Synapse Analytics
Understand data warehouse developer features of Azure Synapse Analytics

Module 11: Analyze and Optimize Data Warehouse Storage

Analyze and optimize data warehouse storage in Azure Synapse Analytics

Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

Design hybrid transactional and analytical processing using Azure Synapse Analytics
Configure Azure Synapse Link with Azure Cosmos DB
Query Azure Cosmos DB with Apache Spark pools
Query Azure Cosmos DB with server less SQL pools

Module 13: End-to-end security with Azure Synapse Analytics

Secure a data warehouse in Azure Synapse Analytics
Configure and manage secrets in Azure Key Vault
Implement compliance controls for sensitive data

Module 14: Real-time Stream Processing with Stream Analytics

Enable reliable messaging for Big Data applications using Azure Event Hubs
Work with data streams by using Azure Stream Analytics
Ingest data streams with Azure Stream Analytics

Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks

Process streaming data with Azure Databricks structured streaming

Module 16: Build reports using Power BI integration with Azure Synapase Analytics

Create reports with Power BI using its integration with Azure Synapse Analytics

Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

Use the integrated machine learning process in Azure Synapse Analytics

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Cash/Cheque/DD ( Not for Online Training )

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* All of the classes are conducted live online. They are interactive sessions that enable you to ask questions and participate in discussions during class time. We do, however, provide recordings of each session you attend for your future reference.

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Key Features

Who can take up Data Engineering on Microsoft Azure Certification Training?

Curriculum

Module 1: Explore compute and storage options for data engineering workloads

Module 2: Design and implement the serving layer

Module 3: Data engineering considerations for source files

Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools

Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark

Module 6: Data exploration and transformation in Azure Data bricks

Module 7: Ingest and load data into the data warehouse

Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines

Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines

Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse

Module 11: Analyze and Optimize Data Warehouse Storage

Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link

Module 13: End-to-end security with Azure Synapse Analytics

Module 14: Real-time Stream Processing with Stream Analytics

Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks

Module 16: Build reports using Power BI integration with Azure Synapase Analytics

Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics

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