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.

5/5

Upcoming Batch Weekdays!!!

Starting from 19th April

09:00 am – 1:00 pm Weekends

Fully Interactive Classroom Training

  • 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 in AWS Data Engineering course Gurgaon in Palin analytics

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.

Want to discuss your roadmap to be a AWS 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

Industry Expret Trainers

Shareable Certificate

Learn from anywhere

Career Transition Guidance

Real-Time Projects

Industry Endorsed Curriculum

Interview Preparation Techniques

Class recordings

Course Mentor

Anurag Pandey

Hi I am Anurag 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. 

what is Course Content of AWS Data Engineering Course in Palin Analytics Gurgaon

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

  • Amazon S3 (Simple Storage Service) for scalable object storage
  • Amazon RDS (Relational Database Service) for managing relational databases
  • Amazon DynamoDB for NoSQL database storage
  • Amazon Redshift for data warehousing and analytics
  • AWS Glue for ETL (Extract, Transform, Load) and data preparation
  • Amazon EMR (Elastic MapReduce) for processing large amounts of data using Hadoop, Spark, or other big data frameworks
  • Amazon Kinesis for real-time data streaming and processing
  • 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

  • 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

  • Amazon Athena for querying data in S3 using SQL
  • Amazon QuickSight for business intelligence and data visualization
  • Implementing security best practices for data on AWS
  • Managing data governance policies on AWS
  • Monitoring data pipelines and optimizing performance and costs
  • Using AWS tools for monitoring and optimizing data processing
  • Hands-on experience with AWS services for data engineering
  • Building data pipelines, processing data, and analyzing data using AWS

What Our Students Say About Us

Data Analytcs Demo classes in Palin Analytics Gurgaon

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!

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.

Data analytics is the process of analyzing, interpreting, and gaining insights from data. It involves the use of statistical and computational methods to discover patterns, trends, and relationships in data sets.

Data analytics involves a variety of techniques, such as data mining, machine learning, and data visualization. Data mining is the process of discovering patterns and relationships in large data sets, while machine learning is a type of artificial intelligence that enables computer systems to learn from data and improve their performance over time. Data visualization is the process of presenting data in a visual format, such as charts and graphs, to help people understand complex data sets.

The goal of data analytics is to turn data into insights that can be used to make informed decisions. This can involve identifying opportunities for business growth, improving operational efficiency, or predicting future trends and outcomes. Data analytics is used in many industries, including finance, healthcare, marketing, and government, to name a few.

In summary, data analytics is the process of analyzing data to gain insights and make informed decisions. It involves a range of techniques and tools to extract valuable information from data sets.

 
 
 

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:

  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.

    This will close in 0 seconds

      This will close in 0 seconds

        This will close in 0 seconds

          This will close in 0 seconds

          Inquiry Form
          First Name
          Last Name
          Email
          Mobile
          Course Selected
          Qualification
          Center Location

          Welcome Back, We Missed You!

          Write Review

          Lets us know your experience, it will help others.