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Azure-Data Engineering Course

Step into the booming field of cloud by using Azure-based cloud infrastructures with our Azure DevOps course.

Build expertise in managing and automating Azure-based cloud infrastructures with our Azure DevOps course. This course covers everything from CI/CD pipelines to infrastructure as code, making you proficient in Azure cloud technologies.

> Step 1: Practical Projects

2 live projects and 10+ demo projects to build your real-world experience.

> Step 2: Mock Interviews & Placement Support

Focused interview preparation and assistance with crafting a DevOps-ready resume.

> Step 3: Trainer Expertise

Delivered by Azure-certified DevOps engineers who bring real-world experience to the table.

  • 70 Hours Practical
  • 50 Hours Practical
  • 120 Total Hours

Step into the booming field of cloud by using Azure-based cloud infrastructures with our Azure DevOps course.

  • Online Training
  • Offline Training

Course Syllabus

Downlaod Syllabus PDF

The Azure-Data Engineering course syllabus is designed to provide you with a strong foundation in both theoretical and practical aspects of Azure – DevOps & Data Engineering.

  • create table
  • insert into
  • view table
  • sql clauses
  • sql operations
  • single row functions
  • arithmetic, relational and logical operations
  • joins
  • subqueries
  • set operators
  • string functions
  • pseudo columns
  • data type conversions
  • views
  • indexes
  • constraints
  • general functions
  • syntax
  • variables
  • operations
  • data types
  • functions
  • looping
  • ETL architecture
  • OLAP vs OLTP
  • Software Development Life Cycle (SDLC)
  • Software Testing Life Cycle (STLC)
  • Agile and Waterfall methodology
  • Snowflake and Star schema
  • Database and Data Warehouse
  • Fact and Dimensional Table
  • Full and Incremental load
  • Slowly Changing Dimension (SCD) Types
  • Jira
  • What is cloud
  • Cloud Deployment Models
  • Cloud providers
  • Storage types
  • Cloud Computing Models
  • Azure Portal installation
  • Subscription
  • Resource group
  • Resources
  • Storage Account creation
  • SQL Database Creation
  • Virtual Machine Creation
  • ADF Creation
  • ABD Creation
  • Synapse Analytics creation
  • Understanding Continuousunderstanding linked services, datasets, and pipelines Integration
  • Introduction to integration runtime
  • Copy activity
  • Blob to blob copy
  • Blob to sql copy
  • SQL to blob copy
  • Delete activity
  • Lookup transformation
  • Parameterization
  • Stored procedures
  • Triggers
  • Select transformations
  • Joins
  • Union and union all functions
  • Rank and dense_rank function
  • Derived new column
  • Filter and Sort function
  • Alter row function
  • If condition
  • Create Databricks community
  • Databricks Architecture
  • Databricks Overview
  • Spark SQL - part 1
  • Spark SQL - part 2
  • Pyspark Introduction
  • Pyspark Dataframe
  • How to apply filter and sort dataframe
  • Pyspark groupby and aggregate function
  • Join and Union in pyspark
  • Date functions
  • Date format
  • Windows function
  • Create temp view
  • Dataframe transformcache vs persist
  • Explode function
  • Partition by
  • UDF
  • Pivot upivot function
  • Structtype ()
  • Structfield ()
  • Collect ()
  • Fill and Fillna
  • Joining
  • Sort and Order by
  • What is big data
  • How big data supports and stores data
  • What are all issues big data faces
  • What is Hadoop
  • How Hadoop overcomes big data problems
  • Hadoop processing
  • introduction to azure synapse analytics
  • synapse architecture
  • create azure synapse analytics
  • serverless sql pool
  • dedicated sql pool
  • Overview of Power Bi
  • How to import data
  • How to visualize according to client requirements
  • Azure data factory project (incremental load project)
  • Azure data factory (data migration on-prem to cloud)
  • Azure data factory pipeline project (multiple csv file transfer)
  • Azure data factory (data transformation project)
  • Azure data factory (scd type-1)
  • pyspark project (sql to adls)
  • databricks end to end project
  • power bi project
  • industry level azure data engineer end to end project (sql + adf + databricks + power bi)