• Length:
    4 Weeks
  • Effort:
    9–10 hours per week
  • Price:

    Add a Verified Certificate for $99 USD

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  • Course Type:
    Self-paced on your time

Associated Programs:


  • Basic computer skills.
  • General familiarity of how computing works.
  • Grounding in IT systems.
  • Comfortable working in either Linux, Unix, Windows, or MacOs.

About this course

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Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

Welcome to Data Engineering Basics. This course is designed to familiarize you with data engineering concepts, ecosystem, lifecycle, processes, and tools.

The Data Engineering Ecosystem includes several different components. It includes data, data repositories, data integration platforms, data pipelines, different types of languages, and BI and Reporting tools. Data pipelines gather raw data from disparate data sources. Data repositories, such as relational and non-relational databases, data warehouses, data marts, data lakes, and big data stores, store and process this data. Data Integration Platforms combine data into a unified view for secure and easy access by data consumers. Data consumers use BI, reporting, and analytical tools on data so they can glean insights for better decision-making. You will learn about each of these components in this course.

A typical Data Engineering lifecycle includes architecting data platforms and designing data stores. It also includes the process of gathering, importing, wrangling, cleaning, querying, and analyzing data. Systems and workflows need to be monitored and finetuned for performance at optimal levels. In this course, you will learn about the architecture of data platforms and things you need to consider in order to design and select the right data store for your needs. You will also learn about the processes and tools a data engineer employs in order to gather, import, wrangle, clean, query, and analyze data.

Through a series of hands-on labs, you will be guided to provision a data store on IBM cloud, prepare and load data into the data store, and perform some basic operations on data.

Data Engineering is recognized as one of the fastest-growing fields today. The career opportunities available, and the different paths you can take to become a data engineer, are discussed in the course. Seasoned data professionals advice you on the practical and day-to-day aspects of being a data engineer and the skills and qualities employers look for in a data engineer.

What you'll learn

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The objective of this course is to give you a solid understanding of what Data Engineering is.

In this course you will learn about:

Module 1: What is Data Engineering ****

Modern Data Ecosystem

Key Players in the Data Ecosystem

What is Data Engineering?

Responsibilities and Skillsets of a Data Engineer

A day in the life of a Data Engineer

Module 2: Data Engineering Ecosystem ****

Overview of the Data Engineering Ecosystem

Types of Data

Understanding different types of File Formats

Sources of Data

Languages for Data Professionals

Overview of Data Repositories



Data Warehouses, Data Marts, and Data Lakes

ETL, ELT, and Data Pipelines

Data Integration Platforms

Foundations of Big Data

Big Data processing tools: Hadoop, HDFS, Hive, and Spark

Module 3: Data Engineering Lifecycle

Architecting the Data Platform

Factors for Selecting and Designing Data Stores


How to Gather and Import Data

Data Wrangling

Tools for Data Wrangling

Querying and Analyzing data

Performance Tuning and Troubleshooting

Governance and Compliance

Module 4: Career Opportunities and Learning Paths

Career Opportunities in Data Engineering

Data Engineering Learning Path

  • Module 1: What is Data Engineering
  • Module 2: Data Engineering Ecosystem
  • Module 3: Data Engineering Lifecycle
  • Module 4: Career Opportunities and Learning Paths

Meet your instructors

Rav Ahuja
AI and Data Science Program Director

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