AWS Data Engineering Course In Gurgaon
AWS Data engineering Training: AWS data engineering course typically includes a variety of topics related to designing, building, and maintaining data processing systems on the Cloud platform. wheather its AWS, Azure, GCP Or OCP designing infrastructure, designing data pipelines and managing data pipelines and infrastructure, involves tasks such as data gathering, storing, preprocessing, and managing this data for use by data analysts, data scientists, data engineers and other stakeholders.
What you'll learn
- Amazon S3
- Amazon RDS
- Amazon Redshift
- Amazon Dynamo
- Amazon Glue
- Amazon EMR
- Amazon Kinesis
- AWS Pipeline
- Amazon Athena
- Amazon Lambda
- AWS Data Sync
- Amazon Step Function
Data Engineers Job Responsibilities:
Data engineers primarily ensures that data is clean, reliable, and consistent, which is essential for accurate data analysis and decision-making. By designing and maintaining data pipelines, data engineers make data accessible to everyone like data scientists, data analysts, and other stakeholders who need it for their work, as a data engineers we try to enable organizations to scale their data processing capabilities to handle large volumes of data efficiently. Data engineers integrate data from various sources, such as databases, APIs, Social Media. flat files and streaming platforms, to provide a unified view of the data for analysis. Efficient data pipelines and infrastructure designed by data engineers improve the overall operational efficiency of an organization. Data engineering ensures that data is available in a timely manner, enabling data-driven decision-making across the organization.
Overall, data engineering plays a crucial role in enabling organizations to leverage data effectively and derive valuable insights from it.
What is the Eligibility to go for AWS Data Engineering Course
Data Engineering is meant for all and anyone can learn who is working as a Software engineer, DBA, Data Analyst, Mathematician, Data Scientist, IT Professional, ETL developer. learn to play with data and grasping required skills isn’t just valuable, its essential now. Does not matter from which field you – economics, computer science, chemical, electrical, are statistics, mathematics, operations you will have to learn this.
Advantages
Countless Batch Access
Industry Expret Trainers
Shareable Certificate
Learn from Anywhere
Career Transition Guidance
Real-Time Projects
Industry Endorsed Curriculum
Interview Preparation Techniques
Class recordings
- 9 Sections
- 107 Lessons
- 10 Weeks
- Python38
- 1.1Basics of programming logic
- 1.2Understanding algorithms and flowcharts
- 1.3Overview of Python as a programming language
- 1.4Setting Up Python Environment
- 1.5Installing Python
- 1.6Working with Python IDEs
- 1.7(Integrated Development Environments)
- 1.8Writing and executing the first Python script
- 1.9Python Basics
- 1.10Variables and data types
- 1.11Basic operations (arithmetic, comparison, logical)
- 1.12Input and output (print, input)
- 1.13Control Flow
- 1.14Conditional statements (if, elif, else)
- 1.15Loops (for, while)
- 1.16Break and continue statements
- 1.17Functions in Python
- 1.18Defining functions
- 1.19Parameters and return values
- 1.20Scope and lifetime of variables
- 1.21Lists and Tuples
- 1.22Creating and manipulating lists
- 1.23Slicing and indexing
- 1.24Working with tuples
- 1.25Dictionaries and Sets
- 1.26Understanding dictionaries
- 1.27Operations on sets
- 1.28Use cases for dictionaries and sets
- 1.29File Handling
- 1.30Reading and Writing Files
- 1.31Opening and closing files
- 1.32Reading from and writing to files
- 1.33Working with different file formats (text, CSV)
- 1.34Error Handling and Modules
- 1.35Error Handling
- 1.36Introduction to exceptions
- 1.37Try, except, finally blocks
- 1.38Handling different types of errors
- AWS Data Storage4
- AWS Data Processing3
- MySQL27
- 4.1SQL Advance Queries
- 4.2SQL Data Models
- 4.3SQL
- 4.4Overview of Azure Data
- 4.5Factory and its features
- 4.6Comparison with other data integration services
- 4.7Getting Started with Azure Data Factory
- 4.8Setting up an Azure Data Factory instance
- 4.9Exploring the Azure Data Factory user interface
- 4.10Data Movement in Azure Data Factory
- 4.11Copying data from various sources to destinations
- 4.12Transforming data during the copy process
- 4.13Data Orchestration in Azure Data Factory
- 4.14Creating and managing data pipelines
- 4.15Monitoring and managing pipeline runs
- 4.16Data Integration with Azure Data Factory
- 4.17Using datasets and linked services
- 4.18Building complex data integration workflows
- 4.19Data Transformation in Azure Data Factory
- 4.20Using data flows for data transformation
- 4.21Transforming data using mapping data flows
- 4.22Integration with Azure Services
- 4.23Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
- 4.24Using Azure Data Factory with Azure Databricks for advanced data processing
- 4.25Monitoring and Management
- 4.26Monitoring pipeline and activity runs
- 4.27Managing and optimizing data pipelines for performance
- Data Pipeline Orchestration27
- 5.1SQL Advance Queries
- 5.2SQL Data Models
- 5.3SQl
- 5.4Overview of Azure Data
- 5.5Factory and its features
- 5.6Comparison with other data integration services
- 5.7Getting Started with Azure Data Factory
- 5.8Setting up an Azure Data Factory instance
- 5.9Exploring the Azure Data Factory user interface
- 5.10Data Movement in Azure Data Factory
- 5.11Copying data from various sources to destinations
- 5.12Transforming data during the copy process
- 5.13Data Orchestration in Azure Data Factory
- 5.14Creating and managing data pipelines
- 5.15Monitoring and managing pipeline runs
- 5.16Data Integration with Azure Data Factory
- 5.17Using datasets and linked services
- 5.18Building complex data integration workflows
- 5.19Data Transformation in Azure Data Factory
- 5.20Using data flows for data transformation
- 5.21Transforming data using mapping data flows
- 5.22Integration with Azure Services
- 5.23Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
- 5.24Using Azure Data Factory with Azure Databricks for advanced data processing
- 5.25Monitoring and Management
- 5.26Monitoring pipeline and activity runs
- 5.27Managing and optimizing data pipelines for performance
- Data Analytics2
- Security and Governance2
- Monitoring and Optimization2
- Hands-on Labs and Projects2
You might be intersted in
-
28 Students
-
10 Weeks