Azure Data Engineering

Azure Data Engineer Training in Hyderabad-Become career-ready experts in Azure data engineer training online Hyderabad domain with the aid of V Cube Software Solutions, Azure Data Engineer Training in Kukatpally In Hyderabad by experts

Duration: 3 Months

Course's Key Highlights

100+ hours of learning
Real-time industry professionals curate the course.

Internships and live projects

A cutting-edge training facility

Dedicated staff of placement experts

Placement is guaranteed 100 percent Assistance

28+ Skills That Are Useful in the Workplace

Trainers with a minimum of 12 years of experience

Videos and backup classes

Subject Matter Experts Deliver Guest Lectures

Module 1 : Introduction to Data Engineer
• Data Engineer vs Data Analyst vs Data Scientist
• What is Data ?
• Types of data
o Structured Data
o Semi Structured Data
o Unstructured Data
o Streaming Data
• Why Data Engineering ?
• Who is Data Engineer ?
• What does Data Engineer do ?
• Data Engineering Architecture
• Batch vs Streaming
• OLTP vs OLAP
• Data Engineer Workflow
• Real world examples
• Tools Used by Data Engineer
Module 2 : DataBase Overview
• What is Database ?
• RDBMS fundamentals
• Key RDBMS concepts
o Primary key
o Foreign key
o ACID properties
o Normalization
• What is NoSQL ?
• Types of NoSQL Databases
• When to use SQL vs NoSQL
Module 3 : SQL Basics
• What is SQL ?
• CREATE
• INSERT INTO
• SELECT
• WHERE
• ORDER BY
• GROUP BY
• Operators (AND, OR, IN, LIKE, BETWEEN)
• Practical examples
• Hands-on queries
Module 4 : Joins & Functions :
• SQL Joins
• SQL String Functions
• Data Functions
• Constraints
• HAVING
• Aggregations
• Subquery
• Types of subqueries
• Stored procedures
• SQL Triggers
• SQL Transactions
Module 5 : Data Modeling
• What is Data Modeling ?
• Conceptual Data Model
• Logical Data Model
• Physical Data Model
• Frameworks built on data modelling
o Star Schema
o Snowflake Schema
• Star Schema vs Snowflake Schema
• When to use Star & Snowflake Schemas
Module 6 : ETL vs ELT
• Data Pipeline
• Why pipelines are required ?
• Core components of pipeline
• ETL
• ELT
• ETL vs ELT
Module 7 : Hands On
Module 8 : Python Basics(Python for Data Engineers)
• Variables
• Data Types
• Loops
• Functions
• List, Tuple, Collections
• Sets
• Dictionary
• Regular Expressions
Module 9 : File Handling
• CSV
• JSON
• XML
Module 10 : Pandas Basics
• DataFrames
• Filtering
• Joins
Module 11 : Python for ETL
• Cleaning
• Transformations
• Export
Module 12 : APIs
• REST APIs
• Pagination
Module 13 : Error Handling
• Logging
• Exceptions
Module 14 : Hands On
Module 15 : Azure Introduction
• What is Cloud Computing ?
• Why Azure ?
• Azure in Data Engineering
• Azure Global Infrastructure
• Availability Zones
• AZURE ACCOUNT & HIERARCHY
• Azure Tenant
• Azure Subscription
• Resource Groups Rules
• Azure Resource Manager (ARM)
• ARM core components
• ARM templates
• Azure IAM
• Azure services for Data Engineering (Data Engineering Architecture)
• Azure service roles
• Real – World Enterprise Example
Module 16 : Azure Storage
• Why storage is critical in Data Engineering ?
• What is Azure Storage Account ?
• Types of Azure Storage Account ?
• What is Azure Blob Storage ?
• Azure Storage Hierarchy
• Blob Storage Core Concepts
• Types of Blob Storage
• Types of Blob Storage Access Tier
• Blob Storage Security
• What is Azure Data Lake Storage Gen2
• Why ADLS Gen2
• ADLS Gen2 Hierarchy
• Data Formats stored in ADLS
• Data Lake Zone Architecture
• Azure Storage in Data Pipeline
• Real World Enterprise example
Module 17 : Azure Networking
• Why networking is important ?
• What is Azure Networking ?
• Core Azure Networking Components
o Vnet
o Subnet
o Network Security Group
o Public / Private IP
o Domain Name System
o Network Interface (NIC)
Module 18 : Azure IAM
• What is IAM in Azure ?
• Azure Active Directory (Azure AD / Entra ID)
• What is Azure AD ?
• Authentication vs Authorization
• Azure Identities
• Azure RBAC (Role Based Access Control)
• Azure Roles
• Multifactor Authentication
• Hands On
Module 19 : Azure Data Factory (ADF) Fundamentals :
• What is Azure Data Factory ?
• Key problems Data Engineer face & How ADF solve them
• Why ADF is preferred by Data Engineer ?
• Core Components of ADF
o Pipeline
o Data Flows
o Activities (Copy, Transformation, Control)
o Linked Services
o Datasets
o Integration Runtime
o Triggers
• Common Use-cases of ADF
• How ADF work ?
• Monitoring and Debugging
• ADF Architecture
• Hands-On
Module 20 : Databricks and PySpark:
• Sign up for the free edition
• Explore Databricks UI
• Dataframe Basics
• Distributed Computing, Spark, and Databricks
• SQL and joins in Spark
• Reading Spark plans with explain
• Spark Architecture
• Transformations vs Actions and Lazy Evaluation
• Narrow vs Wide Transformation
• Partition and Parallelism: Repartition
• Partition and Parallelism: Coalesce
• Managed vs External Tables
• Medallion architecture
• Unity Catalog
• Acid & Time Travel
• About AWS Account & Delta Lake
• Delta Lake and Delta format
• Intro & Tech Architecture
• Catalog Setup
• Data Understanding and Upload
• Dimension Data Processing
• Fact Data Processing
Module 21 : Azure Synapse :
• Synapse Overview
o SQL Pools
o Spark Pools
o Pipelines
• SQL Data Warehouse
o Partitioning
o Distribution types
• Synapse Studio
o Serverless SQL
• Ingestion
o COPY INTO for parquet/csv
• ADF + Synapse Integration
o Orchestration
• Data Modeling
o Dimensional modeling on Synapse
Module 22: Orchestration & Automation:
• Orchestration Concepts
o Workflows
o DAGs
• Apache Airflow Basics
o DAG writing
• Airflow on Azure
o MWAA
o ADF comparison
• Automation CI/CD
o Azure DevOps
• CI/CD for Data Pipelines
o Git
o ADF
o Databricks
• Hands On: CI/CD pipeline setup
Module 23: End-to-End Data Engineering Project
• Practice Tests
o 60–70 questions
• Case Studies
• Weak Areas Review
• Final Mock Test
• End-to-end DE Project.

Azure Data Engineer Training in Hyderabad

Why Azure Data engineer is so popular?

“Azure data engineers are in charge of data-related implementation tasks such as providing data storage services, ingesting streaming and batch data, transforming data, implementing security requirements, implementing data retention policies, identifying performance bottlenecks, and gaining access to external data.” Sky Blue information engineers investigate and research explicit information questions presented by partners, and they construct and keep up with secure and agreeable handling pipelines by utilizing various devices and strategies. These experts utilize different Azure information administrations and dialects to store and create purged and improved datasets for examination.

Curriculum for the Azure Data Engineer

What is Microsoft Azure?

Microsoft Azure is a hardware and software-as-a-service cloud computing platform. The service provider creates a managed service here to give users on-demand access to these services.

What are data masking features available in Azure?

Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse Analytics are all supported.
It can be applied to all SQL Databases under an Azure subscription as a security policy.
Users can adjust the level of masking to suit their needs.
Only the query results for certain column values on which data masking has been performed are masked. It does not affect the database’s real stored data.

What is Polybase?

From Azure SQL Database or Azure Synapse Analytics, query data is stored in Hadoop, Azure Blob Storage, or Azure Data Lake Store. It eliminates the need for data to be imported from a third-party source.
Use a few easy T-SQL queries to import data from Hadoop, Azure Blob Storage, or Azure Data Lake Store without having to install a third-party ETL tool.
Export data to Hadoop, Azure Blob Storage, or Azure Data Lake Store with Azure Data Lake Store. It allows data to be exported and archived to external data repositories.

What is reserved capacity in Azure?

Microsoft offers the option of reserving storage capacity on Azure to reduce Azure Storage charges. The reserved storage on the Azure cloud provides users with a fixed amount of capacity for the reservation time. It may be used to store Gen 2 data in a normal storage account for Block Blobs and Azure Data Lake.

Explain the architecture of Azure Synapse Analytics

It’s built to handle tables with hundreds of millions of rows of data. Because Synapse SQL runs on a Massively Parallel Processing (MPP) architecture that distributes data processing across numerous nodes, Azure Synapse Analytics performs complicated queries and returns query results in seconds, even with large data.
The Synapse Analytics MPP engine is accessed through a control node, which acts as a point of entry for applications. The control node cuts down the Synapse SQL query into MPP MPP-optimized format when it receives it. Individual operations are also routed to compute nodes that can complete the processes in parallel, resulting in significantly improved query performance.

What are Dedicated SQL Pools?

Dedicated SQL Pool is a set of tools that allows you to use Azure Synapse Analytics to construct a more typical Enterprise Data Warehousing platform. Data Warehousing Units (DWU) are used to measure the resources, which are provisioned using Synapse SQL. A dedicated SQL pool stores data using columnar storage and relational tables, which improves query performance and reduces storage requirements.

How do you capture streaming data in Azure?

Azure has a dedicated analytics service called Azure Stream Analytics, which includes the Stream Analytics Query Language, a basic SQL-based language. It enables developers to extend the query language’s capabilities by introducing new ML (Machine Learning) functions. Azure Stream Analytics can process a massive quantity of data at over a million events per second and provide results with extremely low latency.

Explore Azure storage explorer and its uses

It’s a versatile standalone tool that lets you manage Azure Storage from any platform, and it’s available for Windows, Mac OS, and Linux. Microsoft Azure Storage is available for download.
It gives easy access to many Azure data stores, including ADLS Gen2, Cosmos DB, Blobs, Queues, Tables, and more.
One of the most important aspects of Azure Storage Explorer is that it allows users to operate even if they are not connected to the Azure cloud service by using local emulators.

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Azure Data Engineering