Azure Data Engineer

Azure Data Engineer


1.      Introduction to Azure, Python Basics, and Linux Fundamentals


·         Introduction to Azure

·         Overview of Azure services

·         Creating an Azure account and setting up a project

·         Python Basics

·         Variables, data types, and operators

·         Control structures: if-else, loops, and functions

·         Working with Python libraries

·         Linux Fundamentals

·         Linux distributions and installation

·         Basic Linux commands

·         File system structure and navigation


2.      Advanced Python, Linux Concepts, and Introduction to DBMS


·         Python

·         Working with data structures: lists, tuples, dictionaries, and sets

·         Exception handling

·         File handling and operations

·         Linux

·         File permissions and ownership

·         Text editors: Vim and Nano

·         Basic shell scripting

·         Introduction to Database Management Systems (DBMS)

·         Types of DBMS: Relational, NoSQL, and NewSQL

·         Overview of SQL and NoSQL databases

·         Database design and normalization


3.      Python Libraries for Data Engineering and Azure Introduction


·         NumPy

·         Introduction to NumPy and arrays

·         Array operations and functions

·         pandas

·         Introduction to pandas and data manipulation

·         Series and DataFrame objects

·         Handling missing data and data cleaning

·         Introduction to Azure Data Engineering Services

·         Azure Blob Storage

·         Azure Data Lake Storage

·         Azure Synapse Analytics


4.      SQL and NoSQL Databases


·         SQL Databases


·         Indexes and performance optimization

·         Database transactions and ACID properties

·         NoSQL Databases

·         Overview of NoSQL database types: Document, Key-Value, Column-Family, and Graph

·         Introduction to popular NoSQL databases: MongoDB, Cassandra, Redis, and Neo4j

·         Use cases and trade-offs


5.      Data Processing with Azure Data Factory, Databricks, and HDInsight


·         Introduction to Azure Data Factory

·         Understanding Azure Data Factory concepts

·         Building data pipelines with Azure Data Factory

·         Azure Databricks

·         Introduction to Azure Databricks and Apache Spark

·         Creating and managing Databricks clusters

·         Running Spark jobs on Databricks

·         Azure HDInsight

·         Overview of Hadoop, Hive, and the Hadoop ecosystem

·         Creating and managing HDInsight clusters

·         Running Hadoop and Hive jobs on HDInsight


6.      Data Ingestion, Real-Time Data Processing, and Machine Learning


·         Introduction to Azure Event Hubs

·         Creating Event Hubs namespaces and instances

·         Publishing and consuming messages using Python

·         Real-Time Data Processing with Azure Stream Analytics

·         Introduction to Azure Stream Analytics

·         Developing streaming jobs

·         Integrating with Event Hubs and other sources

·         Machine Learning with Azure Machine Learning Service

·         Overview of Azure Machine Learning Service

·         Creating and managing workspaces

·         Training and deploying models

·         Serving predictions

·         Data Transformation, Integration with DBMS, and Final Project


7.      Data Transformation with Azure Data Factory and Mapping Data Flows

·         - Introduction to Mapping Data Flows

·         - Overview and use cases

·         - Creating and managing data flows in Azure Data Factory

·         - Data flow design and development

·         - Deploying and monitoring data flows


8.      Integration with SQL and NoSQL Databases

·         Connecting to SQL databases (Azure SQL Database) using Python

·         Connecting to NoSQL databases ( MongoDB) using Python

·         Data import and export between Azure services and external databases

·         Final Project

·         Project briefing and requirements

·         Project implementation

·         Project presentation and evaluation


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