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Azure Data Engineering

Azure Data engineering : 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 Azure data engineering training in gurgaon or delhi ncr.

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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

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.

Who can go for this

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.

Want to discuss your roadmap to be a Azure 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

Hi I am Sahil and I am super excited that you are reading this.

Professionally, I am a data engineering management consultant with over 7+ 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.  

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.  

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 IDEs 

(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

 

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.

 

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

 

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

TOTAL28 LECTURES84:20:00

What Our Students Say About Us

Vishal KumarVishal Kumar
09:41 15 Dec 23
Palin Analytics provides excellent support and resources throughout the Data Science Training. From comprehensive study materials to a responsive support team, I always felt well-equipped to tackle each module. The platform was user-friendly, enhancing the overall learning experience. I enrolled in Data Science course in Gurgaon location, my overall experience was awesome.
ramprakash kushwaharamprakash kushwaha
09:37 15 Dec 23
Palin Analytics’ Data Science training is a fantastic choice for anyone looking to enter or advance in the field. The comprehensive curriculum, coupled with expert instruction and practical applications, equips participants with the skills needed in today’s data-driven world. I highly recommend Palin Analytics Gurgaon for anyone serious about pursuing a career in Data Science.
Bijendra SinghBijendra Singh
09:32 15 Dec 23
I highly recommend Palin Analytics for anyone seeking top-notch data engineering training in gurgaon location. The dedication to providing quality education is evident throughout the program. The instructors at Palin Analytics are true industry experts. Their deep understanding of data engineering, coupled with their passion for teaching, creates an engaging and enriching learning experience. They are not just instructors but mentors who guide you every step of the way.The interactive nature of the classes fosters an environment of collaboration and knowledge sharing. The peer-to-peer interactions, group discussions, and collaborative projects added immense value to the learning process. It truly feels like a community of learners all striving for excellence.
Neha KashyapNeha Kashyap
08:37 15 Dec 23
The course content was exceptionally well-structured, covering all essential aspects of SAP FICO training in gurugram. From fundamentals to advanced topics, each module was presented with clarity and depth, making complex concepts easy to grasp. The hands-on exercises and real-world scenarios further enhanced my understanding.
MaddyMaddy
16:53 25 Nov 23
I am thrilled to share my profound satisfaction with the Data Engineering training program at Palin Analytics. This experience has been nothing short of exceptional, and I am genuinely grateful for the depth of knowledge and skills I’ve gained.Palin Analytics has crafted a curriculum that not only covers the fundamentals of data engineering but delves into advanced topics, ensuring a comprehensive understanding of the field. The inclusion of emerging technologies and industry-relevant tools demonstrates a commitment to staying at the forefront of the rapidly evolving data landscape.
KAVITA karnKAVITA karn
07:01 25 Nov 23
Palin Analytics goes beyond just providing education; they facilitate networking opportunities within the data engineering community. The connections I’ve made with fellow learners and industry professionals have been invaluable, opening doors to new insights and potential collaborations.As a result of this program, I feel confident in my ability to tackle complex data engineering challenges in a professional setting. The practical skills gained are directly applicable, and I can see the immediate impact on my work.
Manish KumarManish Kumar
06:48 25 Nov 23
I recently completed the Data Engineering training program with Palin Analytics, and I cannot express how impressed I am with the quality of the course and the expertise of the instructors.The course covers a wide range of topics, providing a thorough understanding of data engineering concepts and tools.Palin Analytics has built a supportive community of learners. The peer interactions and networking opportunities were enriching.I highly recommend Palin Analytics for anyone seeking top-notch data engineering training. The dedication to providing quality education is evident throughout the program.
Vardhaman KanodiaVardhaman Kanodia
06:27 25 Nov 23
I recently had the privilege of completing the Data Engineering training program at Palin Analytics, and the journey has been nothing short of transformative.The course content is exceptionally well-structured, covering a comprehensive range of data engineering principles and cutting-edge technologies. From foundational concepts to advanced techniques, the curriculum is designed to provide a holistic understanding of the field.What sets Palin Analytics apart is their commitment to practical application. The hands-on projects were instrumental in solidifying theoretical knowledge, and the real-world scenarios presented challenged me to think critically and creatively. The emphasis on practical skills ensures that graduates are not only knowledgeable but also industry-ready.
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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

4.5 OUT OF 5
4.1/5

Deepika

5/5
1 year ago

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. 

Deepak Jaiswal

5/5
1 year ago

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. 

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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Data Warehousing: Understand the principles of data warehousing, including ETL (Extract, Transform, Load) processes, data integration, and data modeling for analytics.

  7. 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.

  8. 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:

  1. Google: Google offers data analytics internships where you get to work on real-world data analysis projects and gain hands-on experience.

  2. Microsoft: Microsoft provides internships in data analytics where you can learn about big data and machine learning.

  3. Amazon: Amazon offers data analytics internships where you can learn how to analyze large datasets and use data to make business decisions.

  4. 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.

  5. Deloitte: Deloitte offers internships in data analytics where you can gain experience in areas such as data analytics strategy, data governance, and data management.

  6. PwC: PwC provides internships in data analytics where you can learn how to analyze data to identify trends, insights, and opportunities.

  7. Accenture: Accenture offers internships in data analytics where you can work on projects related to data analytics, data management, and data visualization.

  8. 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:

  1. 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.

  2. 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.

  3. 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.

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