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Azure Data Engineering Course
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.
- English
- English, Hindi
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Upcoming Batch !!!
Starting from Coming Sat
10:00 am – 2:00 pm
- 90 Hours Online Classroom Sessions
- 11 Module 04 Projects 5 MCQ Test
- 6 Months Complete Access
- Access on Mobile and laptop
- Certificate of completion
65,000 Students Enrolled
What we will learn
- Azure data Factory
- Data Extraction, Data Prep
- Data Pre-Processing
- Data Transforming and Loading
- Azure Data Bricks
- Apache Spark
- Big Data Processings
- Data Analysis and Handling
- Azure Synapse Analytics:
- Unified Analytics
- Data Integration
- Data Warehousing
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.Â
Sources of DataÂ
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.
Want to discuss your roadmap to be a Data Engineer?
Are you interested in pursuing a career as a data engineer, it’s essential to create a roadmap that outlines the key steps and milestones along the way. Join us for an inspiring conversation where we will deep dive into your own journey and discuss the clear cut roadmap to become a data engineer. Let’s start the journey to be a data engineer in the exciting world of data together!
Advantages
Countless Batch Access
Learn from anywhere
Industry Endorsed Curriculum
Industry Expret Trainers
Career Transition Guidance
Interview Preparation Techniques
Shareable Certificate
Real-Time Projects
Class recordings
Course Mentor
Sahil Arora
- 10 + Batches
- 4.8 Star Rating
- 369 Students Trained
- 300+ Successfully Placed
Hi I am Sahil and I am super excited that you are reading this.
My Profile : Professionally, I am a data engineering management consultant with over 8+ years of experience specializing in Service and product based organization. Experienced in all the phases of SDLC, Requirement Gathering. System, Application, database designing and deployment. I was trained by best mentor at Microsoft and now a days I leverage Data Engineering to drive business strategy, revamp customer experience and revolutionize existing operational processes. Â
From this course you will get to know how I combine my working knowledge, experience and qualification background in computer science to deliver training step by step. Â
Course Content
| LESSONS | LECTURES | DURATION |
|---|
Introduction to Programming
Basics of programming logic
Understanding algorithms and flowcharts
Overview of Python as a programming language
Setting Up Python Environment
Installing Python
Working with Python IDEsÂ
(Integrated Development Environments)
Writing and executing the first Python script
Python Basics
Variables and data types
Basic operations (arithmetic, comparison, logical)
Input and output (print, input)
Control Flow
Conditional statements (if, elif, else)
Loops (for, while)
Break and continue statements
Functions in Python
Defining functions
Parameters and return values
Scope and lifetime of variables
Lists and Tuples
Creating and manipulating lists
Slicing and indexing
Working with tuples
Dictionaries and Sets
Understanding dictionaries
Operations on sets
Use cases for dictionaries and sets
File Handling
Reading and Writing Files
Opening and closing files
Reading from and writing to files
Working with different file formats (text, CSV)
Error Handling and Modules
Error Handling
Introduction to exceptions
Try, except, finally blocks
Handling different types of errors
Overview of Microsoft Azure
History and evolution of Azure
Azure services and products
Azure global infrastructure
Getting Started with Azure
Creating an Azure account
Azure Portal overview
Azure pricing and cost management
Azure Core Services
Azure Virtual Machines (VMs)
Azure Storage (Blobs, Files, Queues, Tables)
Azure Networking (Virtual Network, Load Balancer, VPN Gateway)
Azure Database Services
Azure SQL Database
Azure Cosmos DB
Azure Storage
Azure Data Lake Storage
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Introduction to Azure Data Factory
Overview of Azure Data
Factory and its features
Comparison with other data integration services
Getting Started with Azure Data Factory
Setting up an Azure Data Factory instance
Exploring the Azure Data Factory user interface
Data Movement in Azure Data Factory
Copying data from various sources to destinations
Transforming data during the copy process
Data Orchestration in Azure Data Factory
Creating and managing data pipelines
Monitoring and managing pipeline runs
Data Integration with Azure Data Factory
Using datasets and linked services
Building complex data integration workflows
Data Transformation in Azure Data Factory
Using data flows for data transformation
Transforming data using mapping data flows
Integration with Azure Services
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
Using Azure Data Factory with Azure Databricks for advanced data processing
Monitoring and Management
Monitoring pipeline and activity runs
Managing and optimizing data pipelines for performance
SQL Advance Queries
SQL Data Models
SQl
Overview of Azure Data
Factory and its features
Comparison with other data integration services
Getting Started with Azure Data Factory
Setting up an Azure Data Factory instance
Exploring the Azure Data Factory user interface
Data Movement in Azure Data Factory
Copying data from various sources to destinations
Transforming data during the copy process
Data Orchestration in Azure Data Factory
Creating and managing data pipelines
Monitoring and managing pipeline runs
Data Integration with Azure Data Factory
Using datasets and linked services
Building complex data integration workflows
Data Transformation in Azure Data Factory
Using data flows for data transformation
Transforming data using mapping data flows
Integration with Azure Services
Integrating Azure Data Factory with other Azure services like Azure Blob Storage, Azure SQL Database, etc.
Using Azure Data Factory with Azure Databricks for advanced data processing
Monitoring and Management
Monitoring pipeline and activity runs
Managing and optimizing data pipelines for performance
Data Modeling: Designing the structure of the data warehouse, including defining dimensions, facts, and relationships between them.
ETL (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.
Dimensional Modeling: A technique for designing databases that are optimized for querying and analyzing data, often used in data warehousing.
Star and Snowflake Schema: Common dimensional modeling schemas used in data warehousing to organize data into a central fact table and related dimension tables.
Data 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.
Fact Table: A table in a data warehouse that contains the primary data for analysis, typically containing metrics or facts that can be analyzed.
Dimension Table: A table in a data warehouse that contains descriptive information about the data, such as time, location, or product details.
ETL Tools: Software tools used to extract data from various sources, transform it into a usable format, and load it into the data warehouse.
Data Quality: Ensuring that data is accurate, consistent, and reliable, often through processes such as data cleansing and validation.
Data Governance: Policies and procedures for managing data assets, ensuring data quality, and ensuring compliance with regulations and standards.
Data Warehouse Architecture: The overall structure and components of a data warehouse, including data sources, ETL processes, storage, and access layers.
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Introduction to Azure Databricks
Overview of Azure Databricks and its features
Benefits of using Azure Databricks for data engineering and data science
Getting Started with Azure Databricks
Creating an Azure Databricks workspace
Overview of the Azure Databricks workspace interface
Apache Spark Basics
Introduction to Apache Spark
Understanding Spark RDDs, DataFrames, and Datasets
Working with Azure Databricks Notebooks
Creating and managing notebooks in Azure Databricks
Writing and executing Spark code in notebooks
Data Exploration and Preparation
Loading and saving data in Azure Databricks
Data exploration and basic data cleaning using Spark
Data Processing with Spark
Performing data transformations using Spark SQL and DataFrame API
Working with structured and semi-structured data
Advanced Analytics with Azure Databricks
Running machine learning algorithms using MLlib in Azure Databricks
Visualizing data and results in Azure Databricks
Optimizing Performance
Best practices for optimizing Spark jobs in Azure Databricks
Understanding and tuning Spark configurations
Integration with Azure Services
Integrating Azure Databricks with Azure Storage (e.g., Azure Blob Storage, Azure Data Lake Storage)
Using Azure Databricks in conjunction with other Azure services (e.g., Azure SQL Database, Azure Cosmos DB)
Collaboration and Version Control
Collaborating with team members using Azure Databricks
Using version control with Azure Databricks notebooks
Real-time Data Processing
Processing streaming data using Spark Streaming in Azure Databricks
Building real-time data pipelines
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Introduction to Azure Synapse Analytics
What is Synapse Analytics Service?
Create Dedicated SQL Pool Explore Synapse Studio V2
Analyse Data using Apache Spark Notebook
Analyse Data using Dedicated SQL Pool
Monitor Synapse Studio
Apache Spark
Introduction of Spark
Spark Architecture
PySpark
| TOTAL | 28 LECTURES | 84:20:00 |
|---|
What Our Students Say About Us
Palin Analytics
We are dedicated to empowering professionals as well as freshers with the skills and knowledge which is needed to upgrade in the field of Data Science. Whether you’re a beginner or a professional, our structured training programs are well designed to handle all levels of expertise.
Are you ready to explore your Data Science adventure? Watch a live recorded demo video now and discover the endless possibilities way of teaching, way of handling queries. Awaiting for you at Palin Analytics!
Student feedback
Deepika
Kushal is a good instructor for Data science. He cover all real world projects. He provided very good study materials and high support provided by him for interview prepration. Overall best online course for Data Science.Â
Deepak Jaiswal
This is a very good place to jump start on your focus area. I wanted to learn python with a focus on data science and i choose this online course. Kushal who is the faculty, is an accomplished and learned professional. He is a very good trainer. This is a very rare combination to find.Â
Instructor
Thank you Deepak…
Add Reviews about your experience with us.
FAQ's
Data engineering is a field of data science that focuses on the practical application of data collection and analysis. It involves designing, building, and maintaining the architecture and infrastructure that allows for the processing and storage of large volumes of data. Data engineers are responsible for developing data pipelines, which are workflows that extract data from various sources, transform it into a usable format, and load it into a data store, such as a data warehouse or database.
Fundamentals of Data Science: Start by understanding the basics of data science, including data types, data structures, and basic statistical analysis. This will provide you with a foundation for more advanced data engineering concepts.
Programming Languages: Learn a programming language commonly used in data engineering, such as Python or Scala. Focus on libraries and frameworks relevant to data processing and manipulation, such as pandas, NumPy, or Apache Spark.
Databases and SQL: Gain an understanding of relational databases and SQL (Structured Query Language). Learn how to design and query databases, as well as how to optimize database performance.
Data Modeling: Learn about data modeling concepts, including relational, dimensional, and NoSQL data models. Understand how to design effective data models for different types of data.
Big Data Technologies: Familiarize yourself with big data technologies, such as Apache Hadoop, Apache Spark, and distributed computing concepts. Learn how to process and analyze large volumes of data efficiently.
Data Warehousing: Understand the principles of data warehousing, including ETL (Extract, Transform, Load) processes, data integration, and data modeling for analytics.
Cloud Computing: Learn about cloud computing platforms, such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP). Understand how to use cloud services for data storage, processing, and analytics.
Data Pipelines and ETL: Learn how to design and build data pipelines for extracting, transforming, and loading data. Understand best practices for building scalable and efficient data pipelines.
Along with the high quality training you will get a chance to work on real time projects as well, with a proven record of high placement support. We Provide one of the best online data engineering course.
Its Live interactive training, Ask your quesries on the go, no need to wait for doubt clearing.
you will have access to all the recordings, you can go through the recording as many times as you want.
During the training and after as well we will be on the same slack channel, where trainer and admin team will share study material, data, project, assignment.
There are many companies that offer internships in data analytics. Some of the well-known companies that provide internships in data analytics are:
Google: Google offers data analytics internships where you get to work on real-world data analysis projects and gain hands-on experience.
Microsoft: Microsoft provides internships in data analytics where you can learn about big data and machine learning.
Amazon: Amazon offers data analytics internships where you can learn how to analyze large datasets and use data to make business decisions.
IBM: IBM provides internships in data analytics where you can work on real-world projects and learn about data visualization, machine learning, and predictive modeling.
Deloitte: Deloitte offers internships in data analytics where you can gain experience in areas such as data analytics strategy, data governance, and data management.
PwC: PwC provides internships in data analytics where you can learn how to analyze data to identify trends, insights, and opportunities.
Accenture: Accenture offers internships in data analytics where you can work on projects related to data analytics, data management, and data visualization.
Facebook: Facebook provides internships in data analytics where you can gain experience in areas such as data modeling, data visualization, and data analysis.
These are just a few examples of companies that provide internships in data analytics. You can also search for internships in data analytics on job boards, company websites, and LinkedIn.
SQL (Structured Query Language) is a popular language used for managing and manipulating relational databases. The difficulty of learning SQL depends on your previous experience with programming, databases, and the complexity of the queries you want to create. Here are a few factors that can affect the difficulty of learning SQL:
Prior programming experience: If you have experience with other programming languages, you may find it easier to learn SQL as it shares some similarities with other languages. However, if you are new to programming, it may take you longer to grasp the concepts.
Familiarity with databases: If you are familiar with databases and data modeling concepts, you may find it easier to understand SQL queries. However, if you are new to databases, you may need to spend some time learning the basics.
Complexity of queries: SQL queries can range from simple SELECT statements to complex joins, subqueries, and window functions. The complexity of the queries you want to create can affect how difficult it is to learn SQL.
Overall, SQL is considered to be one of the easier programming languages to learn. It has a straightforward syntax and many resources available for learning, such as online courses, tutorials, and documentation. With some dedication and practice, most people can learn the basics of SQL in a relatively short amount of time.
you can write your questions at info@palin.co.in we will address your questions there.