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.
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- English, Hindi
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
- 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.
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
- 10 + Batches
- 4.8 Star Rating
- 369 Students Trained
- 300+ Successfully Placed
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.
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.
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:
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.