What exactly does a Data Engineer do?

A Data Engineer is responsible for collecting, managing, and organizing data so that businesses can use it for decision-making. You'll build systems that store and process data efficiently.

Is coding required for this course?

Yes, you will need to learn some basic coding, mainly in Python and SQL, but don’t worry, the course will guide you step by step.

Will this course teach me how to work with big data?

Yes, Data Engineering Training in Noida covers tools and techniques used to handle large sets of data, also known as "big data," like Hadoop and Spark.

Do I need a background in IT to join this course?

No, you don’t need a background in IT. The course is designed for beginners, and you will learn everything from scratch.

Will I be able to build my own data systems after completing this course?

Yes, by the end of the course, you’ll be able to create and manage data pipelines, helping businesses process and store data more effectively.

Data Engineer Course in Noida

Name: Data Engineer- Professional Training Program
Item: Data Engineer- Professional Training Program
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Master the Fundamental Concepts of Data Engineer. Enroll today and become a skilled Data Engineer expert.

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The Data Engineer Course in Noida is designed for students and professionals who want to build a career in data engineering. This course covers everything from data collection and storage to processing and analysis using industry-standard tools. You'll learn how to handle large data sets, create data pipelines, and work with cloud platforms like AWS, Azure, and Google Cloud. By the end of Data Engineer Training in Noida, you'll be able to design, build, and maintain efficient data architectures, which are crucial for businesses today. The training is hands-on and includes real-world projects to ensure you gain practical experience.

This course aims to teach essential data engineering skills, including data management, processing, and optimization. Youll work with databases, cloud technologies, and data processing frameworks.
Key Objectives:

Understand the role of a data engineer in an organization.

Learn how to collect, store, and process large volumes of data efficiently.

Gain expertise in SQL, NoSQL, and cloud databases like MySQL, MongoDB, and BigQuery.

Work with big data tools like Apache Hadoop, Spark, and Kafka.

Learn ETL (Extract, Transform, Load) techniques for data processing.

Build scalable data pipelines to move data across systems.

Implement data security and governance best practices.

Use Python and Scala for data manipulation.

Learn cloud-based data engineering using AWS, Google Cloud, and Azure.

Work on real-world projects and case studies for hands-on experience.

After completing Data Engineer Classes in Noida, freshers can expect competitive salaries in the data engineering field. Salaries may vary based on skills, certifications, and the hiring company.

Entry-level Data Engineer: 4-6 LPA

Data Analyst transitioning to Data Engineering: 5-8 LPA

Cloud Data Engineer: 6-10 LPA

Big Data Engineer: 8-12 LPA

With experience, professionals can earn significantly higher salaries as they gain expertise and work on advanced projects.

Data engineering offers excellent career growth as businesses increasingly rely on data for decision-making.

Junior Data Engineer Working with SQL, databases, and basic ETL tools.

Data Engineer Handling complex data pipelines and processing large datasets.

Senior Data Engineer Leading teams and optimizing data workflows.

Data Architect Designing end-to-end data infrastructures.

Machine Learning Engineer Using data to train and deploy AI models.

This field offers continuous learning opportunities, and professionals can advance by gaining certifications and learning new tools.

Data engineering is one of the most in-demand careers today, and the Data Engineering Course in Noida is tailored to meet industry requirements.

High demand for skilled data engineers in tech companies.

Growing big data industry requires structured and optimized data management.

Cloud computing adoption in companies, creating demand for cloud data engineers.

Career stability and high salary potential compared to other IT fields.

Hands-on learning with live projects and case studies.

After completing this course, you can apply for different data engineering roles in top companies.
Key Responsibilities:

Building and managing data pipelines for structured and unstructured data.

Optimizing database performance and improving query efficiency.

Handling big data tools like Apache Spark, Hadoop, and Kafka.

Integrating data sources from multiple systems into a unified platform.

Ensuring data security, privacy, and compliance with industry standards.

Collaborating with data scientists to provide clean and structured datasets.

Working with cloud platforms to store and process data efficiently.

Many industries are looking for data engineers to manage and process their data efficiently.

IT and Software Development Building scalable data architectures.

E-commerce Managing huge customer and sales data.

Banking and Finance Handling financial transactions securely.

Healthcare Processing and analyzing patient data.

Retail and Supply Chain Managing inventory and sales forecasting.

Telecommunications Optimizing network performance using data.

Certifications validate your skills and improve job prospects. After completing Data Engineering Classes in Noida, you can earn certificates from leading cloud and data platforms.

We provide complete placement assistance to help students get hired after Data Engineering Training in Noida completion.
Placement Process:

Resume Building and Interview Preparation

Mock Interviews with Industry Experts

Job Referrals and Application Assistance

Placement Drives with Hiring Companies

Internship Opportunities for Hands-on Experience

You May Also Read:

Data Science Course in Noida

Python Training in Noida

Deep Learning Training in Noida

Machine Learning Training in Noida

Why Should You Choose Data Engineering Course?

Data Engineering helps out in creating better real-life solutions.

The concept of Data Engineering is all-pervasive.

Helps out in getting understanding of Maths, Statistics, Programming & others.

By 2026 there will 11.5 million jobs gets available in Data Engineering Course.

Data Engineering helps out in getting top position in organizations.

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CURRICULUM & PROJECTS

Data Engineer- Professional Training Program

Python for Data Engineering:

Data types, loops, and conditionals

File handling (CSV, JSON, TXT)

Functions, lambda expressions, error handling

Working with libraries: Pandas, NumPy

Data manipulation and cleaning

Working with APIs and JSON data

Python for automation and scripting

Scala (for Apache Spark):

Basics of Scala syntax and structure

Functional programming (map, reduce, filter)

Case classes and immutability

Using Scala with Spark (RDDs, DataFrames)

Java Basics:

OOP principles: class, object, inheritance

Working with Hadoop Java APIs (MapReduce basics)

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Apache Spark:

Spark architecture and cluster modes

RDDs vs DataFrames vs Datasets

Spark SQL: working with structured data

Transformations and actions

Spark Streaming: real-time data processing

Machine learning pipelines with MLlib (intro level)

HDFS (Hadoop Distributed File System):

Blocks and replication

NameNode and DataNode

File storage commands and usage

Integration with Spark, Hive, Sqoop

YARN:

YARN architecture

ResourceManager and NodeManager

Application lifecycle and scheduling

Apache Mesos:

Mesos vs YARN

Resource allocation and scheduling

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Relational Databases:

Database design concepts

SQL queries (SELECT, JOIN, GROUP BY)

Indexing, constraints, normalization

NoSQL Databases:

Concepts: CAP Theorem, BASE vs ACID

Key-Value, Document, Columnar types

Cassandra:

Data model, keyspaces, replication, CQL

Write-heavy optimizations

MongoDB:

BSON, collections, indexing, aggregation framework

CRUD operations and performance tuning

HBase:

Column-family model, integration with Hadoop

Use cases for wide-column storage

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Apache Hive:

Hive architecture: Metastore, Driver, Executor

HiveQL syntax: DDL, DML, Joins, UDFs

Partitions and Bucketing

Integration with HDFS and Spark

Snowflake:

Cloud-based architecture: compute vs storage

Warehouses, databases, and schemas

Time Travel, cloning, and zero-copy restore

Working with semi-structured data (JSON, XML)

Performance tuning and caching

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Apache Kafka:

Producer-consumer architecture

Topics, partitions, brokers

Kafka Streams basics

Apache Flink:

Stream vs batch processing

Windows, time semantics (event vs processing time)

Stateful operators

Fault-tolerant streaming with checkpoints

Apache Storm:

Storm architecture: Spouts and Bolts

Building topologies

Real-time analytics pipelines

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Apache Airflow:

DAGs, Tasks, Operators (PythonOperator, BashOperator, etc.)

Scheduling and triggering jobs

XComs, task dependencies, retries

Airflow UI for monitoring and management

Integrating with DBs, APIs, Spark, and cloud services

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AWS:

S3, IAM, EC2 basics

Redshift for data warehousing

AWS Glue for ETL

EMR for big data

Azure:

Azure Data Factory (pipelines)

Azure Blob Storage

Azure Synapse for analytics

GCP:

BigQuery for analytics

Google Cloud Storage

Dataflow for stream and batch pipelines

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OLTP vs OLAP systems

Entity-Relationship (ER) modeling

Star Schema and Snowflake Schema

Dimension and Fact tables

Slowly Changing Dimensions (SCD Types 1, 2, 3)

Data Vault modeling basics

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Designing batch and stream pipelines

Data lake vs data warehouse architectures

Scalable ingestion pipelines

Fault tolerance, latency, high availability

Case studies: Uber, Netflix, Spotify pipelines

Choosing tools for use cases

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Apache Sqoop:

Sqoop architecture and setup

Incremental loads (append, last-modified)

Performance tuning using mappers

Capstone Projects

Batch ETL Pipeline using Airflow + Hive + MySQL

Real-Time Streaming using Kafka + Spark + MongoDB

Cloud Data Pipeline using AWS S3 + Glue + Redshift

System Design Case Study (architecture presentation)

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