Microsoft Azure Training Online

DP203T00-Data Engineering on Microsoft Azure Certification Training

Exam Code: DP203

The certification on Data Engineering on Microsoft Azure imparts training on Azure Databricks, Data Lake Storage, Delta Lake Architecture, Schema, Designing Data Warehouse, big data engineering with Apache Spark in Azure Synapse Analytics, and so on.

  • 32 Hours Instructor­ led Online Training
  • Authorized Digital Learning Materials
  • Lifetime Free Content Access
  • Flexible Schedule Learn Anytime, Anywhere.
  • Training Completion Certificate
  • 24x7 After Course Support
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Program Calendar

  • Available Dates
    Live Virtual Training
    • cal.png04 May, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png11 May, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
    Live Virtual Training
    • cal.png18 May, 2024
    • time.png19:00 - 23:00 IST
    • week.pngWeekend
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Course Overview

Data Engineering on Microsoft Azure training and lab assignments skills students on the data engineering patterns, working with batch and real-time analytical solutions, understanding the core compute and storage technologies, and serving layers and focus on data engineering Azure Synapse pipelines.

Key Features

right.png Real-industry training
right.png Hands-on experience
right.png Robust lab assignments
right.png Lifetime access to digital materials

Learning Objectives

After completing this course, students will be able to:

  • Understand Azure Synapse serverless SQL pools capabilities
  • Query data in the lake using Azure Synapse serverless SQL pools
  • Create metadata objects in Azure Synapse serverless SQL pools
  • Secure data and manage users in Azure Synapse serverless SQL pools

Benefits

You learn about Azure Synapse Analytics, Databricks, Data Lake storage, Delta Lake architecture, Azure Stream Analytics, Analytical workload, Apache Spark notebooks in Azure Synapse Analytics, Transform data with DataFrames, Integrate SQL and Apache Spark pools in Azure Synapse Analytics, etc.

Prerequisites

  • Cloud computing
  • Core data concepts
  • Data solutions
  • Azure Fundamentals

Course Curriculum

  • Topic Covered:

    • 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

    Lab:

    • Combine streaming and batch processing with a single pipeline
    • Organize the data lake into levels of file transformation
    • Index data lake storage for query and workload acceleration
  • Topic Covered:

    • 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

    Lab:

    • Design a star schema for analytical workloads
    • Populate slowly changing dimensions with Azure Data Factory and mapping data flows
  • Topic Covered:

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

    Lab:

    • Managing files in an Azure data lake
    • Securing files stored in an Azure data lake
  • Topic Covered:

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

    Lab:

    • Query Parquet data with serverless SQL pools
    • Create external tables for Parquet and CSV files
    • Create views with serverless SQL pools
    • Secure access to data in a data lake when using serverless SQL pools
    • Configure data lake security using Role-Based Access Control (RBAC) and Access Control List (ACL)
  • Topic Covered:

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

    Lab:

    • Perform Data Exploration in Synapse Studio
    • Ingest data with Spark notebooks in Azure Synapse Analytics
    • Transform data with DataFrames in Spark pools in Azure Synapse Analytics
    • Integrate SQL and Spark pools in Azure Synapse Analytics
  • Topic Covered:

    • Describe Azure Databricks
    • Read and write data in Azure Databricks
    • Work with DataFrames in Azure Databricks
    • Work with DataFrames advanced methods in Azure Databricks

    Lab:

    • Use DataFrames in Azure Databricks to explore and filter data
    • Cache a DataFrame for faster subsequent queries
    • Remove duplicate data
    • Manipulate date/time values
    • Remove and rename DataFrame columns
    • Aggregate data stored in a DataFrame
  • Topic Covered:

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

    Lab:

    • Perform petabyte-scale ingestion with Azure Synapse Pipelines
    • Import data with PolyBase and COPY using T-SQL
    • Use data loading best practices in Azure Synapse Analytics
  • Topic Covered:

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

    Lab:

    • Execute code-free transformations at scale with Azure Synapse Pipelines
    • Create data pipeline to import poorly formatted CSV files
    • Create Mapping Data Flows
  • Topic Covered:

    • Orchestrate data movement and transformation in Azure Data Factory

    Lab:

    • Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines
  • Topic Covered:

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

    Lab:

    • Understand developer features of Azure Synapse Analytics
    • Optimize data warehouse query performance in Azure Synapse Analytics
    • Improve query performance
  • Topic Covered:

    • Analyze and optimize data warehouse storage in Azure Synapse Analytics

    Lab:

    • Check for skewed data and space usage
    • Understand column store storage details
    • Study the impact of materialized views
    • Explore rules for minimally logged operations
  • Topic Covered:

    • 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 serverless SQL pools

    Lab:

    • Configure Azure Synapse Link with Azure Cosmos DB
    • Query Azure Cosmos DB with Apache Spark for Synapse Analytics
    • Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics
  • Topic Covered:

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

    Lab:

    • Secure Azure Synapse Analytics supporting infrastructure
    • Secure the Azure Synapse Analytics workspace and managed services
    • Secure Azure Synapse Analytics workspace data
  • Topic Covered:

    • 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

    Lab:

    • Use Stream Analytics to process real-time data from Event Hubs
    • Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics
    • Scale the Azure Stream Analytics job to increase throughput through partitioning
    • Repartition the stream input to optimize parallelization
  • Topic Covered:

    • Process streaming data with Azure Databricks structured streaming

    Lab:

    • Explore key features and uses of Structured Streaming
    • Stream data from a file and write it out to a distributed file system
    • Use sliding windows to aggregate over chunks of data rather than all data
    • Apply watermarking to remove stale data
    • Connect to Event Hubs read and write streams
  • Topic Covered:

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

    Lab:

    • Integrate an Azure Synapse workspace and Power BI
    • Optimize integration with Power BI
    • Improve query performance with materialized views and result-set caching
    • Visualize data with SQL serverless and create a Power BI report
  • Topic Covered:

    • Use the integrated machine learning process in Azure Synapse Analytics

    Lab:

    • Create an Azure Machine Learning linked service
    • Trigger an Auto ML experiment using data from a Spark table
    • Enrich data using trained models
    • Serve prediction results using Power BI
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FAQs

You can enroll for this classroom training online. Payments can be made using any of the following options and receipt of the same will be issued to the candidate automatically via email. 1. Online ,By deposit the mildain bank account 2. Pay by cash team training center location
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You will never miss a lecture at Mildaintrainigs! You can choose either of the two options: View the recorded session of the class available in your LMS. You can attend the missed session, in any other live batch.
We have a limited number of participants in a live session to maintain the Quality Standards. So, unfortunately, participation in a live class without enrollment is not possible. However, you can go through the sample class recording and it would give you a clear insight about how are the classes conducted, quality of instructors and the level of interaction in a class.
Yes, you can cancel your enrollment if necessary prior to 3rd session i.e first two sessions will be for your evaluation. We will refund the full amount without deducting any fee for more details check our Refund Policy
Yes, the access to the course material will be available for lifetime once you have enrolled into the course.

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