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Data Engineer

A Data Engineer collects, manages, and transforms raw data into valuable insights for data scientists. With Data Science in high demand, becoming a Data Engineer requires gaining industry-relevant skills. These include data manipulation, database management, data warehousing, ETL processes, programming proficiency, data modeling, data quality, cloud computing, data pipeline orchestration. Acquiring these skills with Data Engineer Courses can boosts your chances of getting noticed by recruiters.

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More About Data Engineer Path

Data Engineers manage raw data transformation for Data Science, needing skills in data manipulation, database management, warehousing, ETL processes, programming, data modeling, quality, cloud computing, pipeline orchestration, and communication. These skills are vital for career success and attracting recruiters.

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Begin your learning experience and become a data engineer with certificate courses curated to land your dream job.

Skills Covered in this Path

  • Big Data basics
  • Hadoop
  • HDFS
  • Hive basics
  • Hive querying
  • Hive data upload
  • Hive simple operations
  • Spark
  • RDDs
  • Hadoop
  • Hadoop
  • Spark
  • Cloud Fundamentals
  • Service Delivery Models
  • Service Deployment Models
  • Pricing & Scaling
  • Cloud Virtualization
  • Cloud Centralization
  • Distributed Computing
  • Cloud Applications
  • Cloud Service Models
  • Trends in Service Models
  • Basics of Cloud Computing
  • Cloud Computing Platforms
  • Types of cloud deployment
  • Cloud Computing Architecture
  • Amazon Web Services
  • Microsoft Azure
  • Google Cloud Platform
  • Cloud Computing Fundamentals
  • AWS Fundamentals
  • AWS Management
  • AWS Cloud Storage
  • Database Services on AWS
  • Understanding how to build an app on AWS
  • Deploying the App
  • Triggers
  • Data Analysis
  • SQL
  • SQL with Python
  • Programming Concepts
  • Python Basics
  • Variables and Data types in Python
  • Operators and Strings in Python
  • Python Data Structures
  • Control Flow Statements and Functions
  • OOPs
  • Introduction to Machine Learning
  • Supervised Machine Learning
  • Linear Regression
  • Pearson's Coefficient
  • Coefficient of Determinant
  • Machine learning
  • Cloud computing
  • AWS services
  • Building chatbots in Azure
  • Building websites using Azure
  • R Commands
  • R Packages
  • R Functions
  • R Datatypes
  • Operators in R
  • RStudio

Free Courses

1

Introduction to Big Data and Hadoop image

Introduction to Big Data and Hadoop

★
4.55
3 hours
40.5K+
Learners
Beginner
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2

Introduction to Apache Hive image

Introduction to Apache Hive

★
4.5
4 hours
10.9K+
Learners
Beginner
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3

Spark Basics image

Spark Basics

★
4.53
2 hours
17.2K+
Learners
Beginner
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4

Spark: PySpark image

Spark: PySpark

★
4.55
3 hours
13K+
Learners
Beginner
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5

Cloud Foundations image

Cloud Foundations

★
4.57
3 hours
206.8K+
Learners
Beginner
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6

Cloud Foundations - Advanced image

Cloud Foundations - Advanced

★
4.58
2 hours
33.3K+
Learners
Advanced
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7

Cloud Computing Service Models image

Cloud Computing Service Models

★
4.49
1 hours
14.7K+
Learners
Beginner
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8

Cloud Computing Architecture image

Cloud Computing Architecture

★
4.53
2 hours
23.7K+
Learners
Beginner
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9

Cloud Service Providers image

Cloud Service Providers

★
4.57
1 hours
6.5K+
Learners
Beginner
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10

AWS For Beginners image

AWS For Beginners

★
4.53
3 hours
148.7K+
Learners
Beginner
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11

Databases and Files Systems in AWS image

Databases and Files Systems in AWS

★
4.52
2 hours
11.2K+
Learners
Beginner
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12

AWS Project image

AWS Project

★
4.47
1 hours
12.6K+
Learners
Intermediate
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13

SQL Triggers for Beginners image

SQL Triggers for Beginners

★
4.47
1 hours
12.9K+
Learners
Intermediate
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14

SQL for Data Science image

SQL for Data Science

★
4.51
3 hours
134.3K+
Learners
Intermediate
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15

Python Fundamentals for Beginners image

Python Fundamentals for Beginners

★
4.55
8 hours
716.7K+
Learners
Beginner
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16

Basics of Machine Learning image

Basics of Machine Learning

★
4.39
3 hours
135.9K+
Learners
Beginner
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17

Introduction to Machine Learning in AWS image

Introduction to Machine Learning in AWS

★
4.49
2 hours
10.9K+
Learners
Beginner
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Optional Free Courses

1

Microsoft Azure Application image

Microsoft Azure Application

★
4.51
2 hours
18.5K+
Learners
Intermediate
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2

Introduction to R image

Introduction to R

★
4.56
1 hours
163.4K+
Learners
Beginner
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Industry relevant skills
Upskill yourself with 1000+ courses across different domains and earn a certificate.
Stand out to recruiters
Earn certificates for each course you complete and share it online to get noticed by recruiters.
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BROWSE BY DOMAINS Gain Industry Relevant Skills

Frequently Asked Questions

What skills do you need to become a Data Engineer?

You need a few critical Data Engineer skills to become a professional in Data Engineering, such as:

  • Programming Languages: A Data Engineer must master any specific programming language, such as Python, R, Java, C/C++, SQL, Ruby, Matlab, or any other extensively implemented programming language.
  • Database Systems: Popular databases include MySQL, MongoDB, and Cassandra. Data Engineers need to understand the execution of any database platforms mentioned above to design and manipulate database queries.
  • Operating Systems (OS): An OS is an interface between a user/developer and a computer. An aspirant must learn any extensively implemented OS, such as Linux, Windows, macOS, and Solaris.
  • Data Warehouses: A data warehouse is used to collect data from multiple sources and manage them to derive business decisions. An aspirant must be able to work with data warehousing solutions like Amazon Web Services (AWS) or Redshift.
  • Data Analytics: Data Analytics is used to collect the customers’ data and analyze them to learn about their behavior patterns, personal interests, and purchasing trends. It assists in boosting the decision-making skills, improving business processes, enhancing user engagement, reducing costs, and driving growth and profitability.
  • Apache Hadoop and Spark: They are essential frameworks for distributed processing of massive data sets across a network of computer systems. An aspirant must understand the working of these frameworks.
  • Machine Learning: A fundamental understanding of Machine Learning and its algorithms is necessary for a Data Engineer to understand data patterns.
  • Soft Skills: Soft skills are as essential as technical skills. A Data Engineer must incorporate these soft skills:
    • Lateral Thinking (Thinking Out of the Box)
    • Excellent communication and management skills
    • Curiosity for learning new skills

Furthermore, a Data Engineer aspirant must learn and grasp several other skill sets like ETL tools, Cloud platforms like AWS, Azure, and GCP, Data APIs, Distributed Systems, Business Intelligence, and Presentation skills.

How should I start learning Data Engineering?

Online platforms have flourished since the beginning of the COVID-19 pandemic. These platforms are ideal for beginners or professionals as they offer numerous benefits to aspirants.

Countless sources are available online for learning Data Engineering. A few principal sources include Books, Coding Bootcamps, YouTube, and E-Learning Platforms. For instance, you can kick-start with the following sources:

Which course is best for becoming a Data Engineer?

Great Learning’s Post Graduate Program in Data Science and Engineering is one of the best courses for becoming a Data Engineer. Great Learning is a top-tier e-learning institute that offers courses in multiple sectors like Artificial Intelligence, Software Development, Cyber Security, Cloud Computing, Digital Marketing, and much more. 

Immensely experienced faculty from Great Learning teach this course, and highly skilled mentors from different tech giants provide personalized career guidance, along with placement assistance. In addition, after successfully completing the program, it awards learners post-graduate certification in Data Science and Engineering. It covers exceptional tools like Python, SQL, Hadoop, Spack, and many more.

[Browse through the PG Program in Data Science and Engineering for in-detail information]

Is Data Engineering a good career?

Yes, Data Engineer is a good career since it has gained massive demand and popularity amongst tech goliaths throughout the globe. A report by MarketsandMarkets states that the Data Engineering market worldwide is projected to reach around USD 77.37 Billion by 2023, from USD 29.5 Billion in 2017, at a CAGR of 17.6% during the forecast period. 

Data Engineers are responsible for collecting, managing, and converting raw data into meaningful data to improve an organization's performance. Consequently, Data Engineer jobs are in tremendous demand, making a generous salary throughout the globe. Currently, numerous job positions are accessible in this discipline across multiple industries worldwide, and these openings can be found on platforms like Indeed, LinkedIn, and others.

What are the responsibilities of a Data Engineer?

The responsibilities of a Data Engineer include:

  • Data Engineers analyze and organize raw data.
  • They are responsible for building data systems and data pipelines.
  • They are responsible for evaluating business requirements and objectives.
  • They interpret data trends and patterns.
  • They use ML algorithms to prepare data for predictive modeling.
  • They collaborate with data scientists and analysts to make data-driven decisions.

How much does being a Data Engineer make?

The salary for a Data Engineer is stupendous throughout the globe. A few average Data Engineer salaries from various countries are mentioned below:

  • United States: $66k to $132k with a median salary of $93k per annum (PayScale)
  • United Kingdom: £48,000 to £81,000 with a median wage of £60,000 per annum (Reed.co.uk)
  • Singapore: SGD 30K to SGD 101K with a median salary of SGD 61K per annum (PayScale)
  • India: ₹2.9L to ₹22L with a median wage of ₹8.4L per annum (AmbitionBox)
  • Switzerland: CHF 60,000 to CHF 132,000 with a median salary of CHF 95,000 per annum (PayScale)

How long does it take to become a Data Engineer after the 12th standard?

It relies on the requirements of an organization, where few firms mandate a Bachelor’s Degree in Computer Science or Information Technology. Contrary to it, particular tech giants like Amazon, Microsoft, Apple, Google, etc., don’t require any degree as they chiefly focus on an aspirant’s talent and skill set. 

Nevertheless, it usually takes 3-4 years to become a Data Engineer after the 12th standard as most firms require a Bachelor’s Degree. However, an aspirant can instantly become a Data Engineer at a beginner’s level by nailing the jobs in tech giants like Amazon, Google, Microsoft, or Apple. But the aspirant must hold tremendous expertise in numerous technical skills like programming languages, operating systems, databases, Data APIs, Apache Hadoop, Apache Spark, Machine Learning, Data Warehousing, Cloud Services, or have implemented any projects related to Data Engineering.