Azure Data Engineering Course In Gurgaon
Azure Data engineering Course Gurgaon : its a process of designing infrastructure, designing data pipelines and managing data pipelines and infrastructure on Microsoft’s Azure cloud platform. Which involves tasks such as data gathering, storing, preprocessing, and managing this data for use by data analysts, data scientists, data engineers and other stakeholders. We collaborate with one of the best trainers in data engineering course gurgaon or delhi ncr.
Azure Data engineering Course Gurgaon : its a process of designing infrastructure, designing data pipelines and managing data pipelines and infrastructure on Microsoft’s Azure cloud platform. Which involves tasks such as data gathering, storing, preprocessing, and managing this data for use by data analysts, data scientists, data engineers and other stakeholders. We collaborate with one of the best trainers in data engineering course gurgaon or delhi ncr.
What we will learn in Azure Data Engineering course Gurgaon in Palin analytics
- Connection, Navigation, Data Sources
- Data Extraction, Data Prep
- Data Pre-Processing
- Exploratory Data Analytics
- Data Wrangling
- Data Cleansing
- Data Munging
- Statistics For Business Analytics
- Data Analytics
- Machine Learning
- Data Visualization
- Python for everyone
Responsibility of Data Engineers
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.
Sources of Data
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.
Who can go for this
Data Engineering course gurgaon 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.
- 7 Sections
- 162 Lessons
- 10 Weeks
- Python39
- 1.1Introduction to Programming
- 1.2Basics of programming logic
- 1.3Understanding algorithms and flowcharts
- 1.4Overview of Python as a programming language
- 1.5Setting Up Python Environment
- 1.6Installing Python
- 1.7Working with Python IDEs
- 1.8(Integrated Development Environments)
- 1.9Writing and executing the first Python script
- 1.10Python Basics
- 1.11Variables and data types
- 1.12Basic operations (arithmetic, comparison, logical)
- 1.13Input and output (print, input)
- 1.14Control Flow
- 1.15Conditional statements (if, elif, else)
- 1.16Loops (for, while)
- 1.17Break and continue statements
- 1.18Functions in Python
- 1.19Defining functions
- 1.20Parameters and return values
- 1.21Scope and lifetime of variables
- 1.22Lists and Tuples
- 1.23Creating and manipulating lists
- 1.24Slicing and indexing
- 1.25Working with tuples
- 1.26Dictionaries and Sets
- 1.27Understanding dictionaries
- 1.28Operations on sets
- 1.29Use cases for dictionaries and sets
- 1.30File Handling
- 1.31Reading and Writing Files
- 1.32Opening and closing files
- 1.33Reading from and writing to files
- 1.34Working with different file formats (text, CSV)
- 1.35Error Handling and Modules
- 1.36Error Handling
- 1.37Introduction to exceptions
- 1.38Try, except, finally blocks
- 1.39Handling different types of errors
- Azure17
- 2.1Overview of Microsoft Azure
- 2.2History and evolution of Azure
- 2.3Azure services and products
- 2.4Azure global infrastructure
- 2.5Getting Started with Azure
- 2.6Creating an Azure account
- 2.7Azure Portal overview
- 2.8Azure pricing and cost management
- 2.9Azure Core Services
- 2.10Azure Virtual Machines (VMs)
- 2.11Azure Storage (Blobs, Files, Queues, Tables)
- 2.12Azure Networking (Virtual Network, Load Balancer, VPN Gateway
- 2.13Azure Database Services
- 2.14Azure SQL Database
- 2.15Azure Cosmos DB
- 2.16Azure Storage
- 2.17Azure Data Lake Storage
- ADF25
- 3.1Introduction to Azure Data Factory
- 3.2Overview of Azure Data
- 3.3Factory and its features
- 3.4Comparison with other data integration services
- 3.5Getting Started with Azure Data Factory
- 3.6Setting up an Azure Data Factory instance
- 3.7Exploring the Azure Data Factory user interface
- 3.8Data Movement in Azure Data Factory
- 3.9Copying data from various sources to destinations
- 3.10Transforming data during the copy process
- 3.11Data Orchestration in Azure Data Factory
- 3.12Creating and managing data pipelines
- 3.13Monitoring and managing pipeline runs
- 3.14Data Integration with Azure Data Factory
- 3.15Using datasets and linked services
- 3.16Building complex data integration workflows
- 3.17Data Transformation in Azure Data Factory
- 3.18Using data flows for data transformation
- 3.19Transforming data using mapping data flows
- 3.20Integration with Azure Services
- 3.21Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
- 3.22Using Azure Data Factory with Azure Databricks for advanced data processing
- 3.23Monitoring and Management
- 3.24Monitoring pipeline and activity runs
- 3.25Managing and optimizing data pipelines for performance
- MySQL27
- 4.1SQL Advance Queries
- 4.2SQL Data Models
- 4.3SQL
- 4.4Overview of Azure Data
- 4.5Factory and Its Features
- 4.6Comparison and 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 Warehouse11
- 5.1Data Modeling: Designing the structure of the data warehouse, including defining dimensions, facts, and relationships between them.
- 5.2ETL (Extract, Transform, Load): Processes for extracting data from source systems, transforming it into a format suitable for analysis, and loading it into the data warehouse.
- 5.3Dimensional Modeling: A technique for designing databases that are optimized for querying and analyzing data, often used in data warehousing.
- 5.4Star and Snowflake Schema: Common dimensional modeling schemas used in data warehousing to organize data into a central fact table and related dimension tables.
- 5.5Data Mart: A subset of the data warehouse that is designed for a specific department or business function, providing a more focused view of the data.
- 5.6Fact Table: A table in a data warehouse that contains the primary data for analysis, typically containing metrics or facts that can be analyzed.
- 5.7Dimension Table: A table in a data warehouse that contains descriptive information about the data, such as time, location, or product details.
- 5.8ETL Tools: Software tools used to extract data from various sources, transform it into a usable format, and load it into the data warehouse.
- 5.9Data Quality: Ensuring that data is accurate, consistent, and reliable, often through processes such as data cleansing and validation.
- 5.10Data Governance: Policies and procedures for managing data assets, ensuring data quality, and ensuring compliance with regulations and standards.
- 5.11Data Warehouse Architecture: The overall structure and components of a data warehouse, including data sources, ETL processes, storage, and access layers.
- Azure Data Bricks33
- 6.1Introduction to Azure Databricks
- 6.2Overview of Azure Databricks and its features
- 6.3Benefits of using Azure Databricks for data engineering and data science
- 6.4Getting Started with Azure Databricks
- 6.5Creating an Azure Databricks workspace
- 6.6Overview of the Azure Databricks workspace interface
- 6.7Apache Spark Basics
- 6.8Introduction to Apache Spark
- 6.9Understanding Spark RDDs, DataFrames, and Datasets
- 6.10Working with Azure Databricks Notebooks
- 6.11Creating and managing notebooks in Azure Databricks
- 6.12Writing and executing Spark code in notebooks
- 6.13Data Exploration and Preparation
- 6.14Loading and saving data in Azure Databricks
- 6.15Data exploration and basic data cleaning using Spark
- 6.16Data Processing with Spark
- 6.17Performing data transformations using Spark SQL and DataFrame API
- 6.18Working with structured and semi-structured data
- 6.19Advanced Analytics with Azure Databricks
- 6.20Running machine learning algorithms using MLlib in Azure Databricks
- 6.21Visualizing data and results in Azure Databricks
- 6.22Optimizing Performance
- 6.23Best practices for optimizing Spark jobs in Azure Databricks
- 6.24Understanding and tuning Spark configurations
- 6.25Integration with Azure Services
- 6.26Integrating Azure Databricks with Azure Storage (e.g., Azure Blob Storage, Azure Data Lake Storage)
- 6.27Using Azure Databricks in conjunction with other Azure services (e.g., Azure SQL Database, Azure Cosmos DB)
- 6.28Collaboration and Version Control
- 6.29Collaborating with team members using Azure Databricks
- 6.30Using version control with Azure Databricks notebooks
- 6.31Real-time Data Processing
- 6.32Processing streaming data using Spark Streaming in Azure Databricks
- 6.33Building real-time data pipelines
- Azure Synapse Analytics10
- 7.1Introduction to Azure Synapse Analytics
- 7.2What is Synapse Analytics Service?
- 7.3Create Dedicated SQL Pool Explore Synapse Studio V2
- 7.4Analyse Data using Apache Spark Notebook
- 7.5Analyse Data using Dedicated SQL Pool
- 7.6Monitor Synapse Studio
- 7.7Apache Spark
- 7.8Introduction of Spark
- 7.9Spark Architecture
- 7.10PySpark
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32 Students
-
10 Weeks