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There are 4 modules in this course
Start your journey in one of the fastest growing professions today with this beginner-friendly Data Engineering course! You will be introduced to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering. as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in the ecosystem.
You will begin this course by understanding what is data engineering as well as the roles that Data Engineers, Data Scientists, and Data Analysts play in this exciting field. Next you will learn about the data engineering ecosystem, the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks.
You will become familiar with the components of a data platform and gain an understanding of several different types of data repositories such as Relational (RDBMS) and NoSQL databases, Data Warehouses, Data Marts, Data Lakes and Data Lakehouses. You’ll then learn about Big Data processing tools like Apache Hadoop and Spark. You will also become familiar with ETL, ELT, Data Pipelines and Data Integration.
This course provides you with an understanding of a typical Data Engineering lifecycle which includes architecting data platforms, designing data stores, and gathering, importing, wrangling, querying, and analyzing data. You will also learn about security, governance, and compliance.
You will learn about career opportunities in the field of Data Engineering and the different paths that you can take for getting skilled as a Data Engineer. You will hear from several experienced Data Engineers, sharing their insights and advice.
By the end of this course, you will also have completed several hands-on labs and worked with a relational database, loaded data into the database, and performed some basic querying operations.
In this module, you will learn about the different entities that come together to form a modern data ecosystem and the role Data Engineers, Data Scientists, Data Analysts, Business Analysts, and Business Intelligence Analysts play in this ecosystem. You will learn what data engineering is and the key tasks in a data engineering lifecycle. You will also gain an understanding of the responsibilities of a data engineer, the skillsets they need in order to be successful, and what a typical day in the life of a data engineer looks like.
What's included
10 videos2 readings6 assignments
Show info about module content
10 videos•Total 56 minutes
Welcome to Introduction to Data Engineering•3 minutes
Modern Data Ecosystem •5 minutes
Key Players in the Data Ecosystem•6 minutes
Specializations in Data Engineering •10 minutes
What is Data Engineering?•4 minutes
Viewpoints: Defining Data Engineering •4 minutes
Viewpoints: Evolution of Data Engineering •8 minutes
Responsibilities and Skillsets of a Data Engineer•5 minutes
Viewpoints: Skills and Qualities to be a Data Engineer•7 minutes
A Day in the Life of a Data Engineer•4 minutes
2 readings•Total 20 minutes
Summary and Highlights•10 minutes
Summary and Highlights•10 minutes
6 assignments•Total 72 minutes
Practice Quiz: Data Engineering Fundamentals•8 minutes
Practice Quiz: Understanding Data Roles and Responsibilities•10 minutes
Practice Quiz: Data Engineering Evolution and Roles•8 minutes
Practice Quiz: Data Engineering Responsibilities and Skills•10 minutes
Graded Quiz: Data Engineering•20 minutes
Graded Quiz: Application of Data Engineering Skills•16 minutes
The Data Engineering Ecosystem
Module 2•5 hours to complete
Module details
In this module, you will learn about the data engineering ecosystem, the different types of data structures, file formats, sources of data, and the languages data professionals use in their day-to-day tasks. You will gain an understanding of several different types of data repositories such as relational and non-relational databases, data warehouses, data marts, and data lakes. You will learn about ETL and ELT processes, data pipelines, and data integration platforms. You will also gain an understanding of what big data is, and the tools used for processing and storing big data. At the end of this module, you will be guided to create an IBM Cloud account, and provision an instance of IBM Db2.
What's included
18 videos7 readings6 assignments1 app item
Show info about module content
18 videos•Total 113 minutes
Overview of the Data Engineering Ecosystem•5 minutes
Types of Data•4 minutes
Understanding Different Types of File Formats •5 minutes
Sources of Data•8 minutes
Languages for Data Professionals•9 minutes
Viewpoints: Working with Varied Data Sources and Types•7 minutes
Overview of Data Repositories•5 minutes
RDBMS•8 minutes
NoSQL•8 minutes
Data Warehouses, Data Marts, and Data Lakes•7 minutes
(Optional): Data Lakehouses Explained•9 minutes
Viewpoints: Considerations for Choice of Data Repository•6 minutes
ETL, ELT, and Data Pipelines•7 minutes
Data Integration Platforms•5 minutes
Viewpoints: Tools, Databases, and Data Repositories of Choice•7 minutes
Foundations of Big Data•5 minutes
Big Data Processing Tools: Hadoop, HDFS, Hive, and Spark•7 minutes
Viewpoints: Impact of Big Data on Data Engineering•4 minutes
7 readings•Total 70 minutes
Metadata and Metadata Management•10 minutes
Summary and Highlights•10 minutes
Summary and Highlights•10 minutes
Optional Labs for IBM Cloud and Db2•10 minutes
[Optional] Hands-on Lab: Create your IBM Cloud account•10 minutes
[Optional ] Hands-on Lab: Provision an instance of IBM Db2 Lite plan•10 minutes
Summary and Highlights•10 minutes
6 assignments•Total 57 minutes
Practice Quiz•10 minutes
Practice Quiz•12 minutes
Practice Quiz•5 minutes
Graded Quiz•10 minutes
Graded Quiz•10 minutes
Graded Quiz•10 minutes
1 app item•Total 60 minutes
[Optional] Obtain IBM Cloud Feature Code and Activate Trial Account•60 minutes
Data Engineering Lifecycle
Module 3•4 hours to complete
Module details
In this module, we will walk you through the data engineering lifecycle. You will learn about the architecture of a data platform, factors for selecting and designing data stores, and the different facets of security as it applies to data platforms and data lifecycle management. You will also learn about the process, steps, and tools used for gathering, importing, wrangling, and querying data. You will gain an understanding of performance monitoring and the steps you can take to troubleshoot performance issues. We will also talk about governance regulations, why we need them, and how technology enables compliance to regulations. During the course of this module, you will be guided to load data from a CSV file into the IBM Db2 instance you created in the previous module. You will also be guided to explore your dataset using some basic SQL queries that will be provided to you.
What's included
10 videos7 readings8 assignments2 app items
Show info about module content
10 videos•Total 64 minutes
Architecting the Data Platform•7 minutes
Factors for Selecting and Designing Data Stores•7 minutes
Security•6 minutes
Viewpoints: Importance of Data Security•4 minutes
How to Gather and Import Data•7 minutes
Data Wrangling•7 minutes
Tools for Data Wrangling•6 minutes
Querying and Analyzing Data•6 minutes
Performance Tuning and Troubleshooting•7 minutes
Governance and Compliance•8 minutes
7 readings•Total 80 minutes
Summary and Highlights•10 minutes
[Optional] Hands-on Lab: Load data into the Db2 Database from a CSV file•15 minutes
Summary and Highlights•10 minutes
[Optional] Hands-on Lab: Explore Your Dataset Using SQL Queries in DB2•15 minutes
Summary and Highlights•10 minutes
Summary and Highlights•10 minutes
Optional: Overview of the DataOps Methodology•10 minutes
8 assignments•Total 54 minutes
Practice Quiz•6 minutes
Practice Quiz•6 minutes
Practice Quiz•5 minutes
Practice Quiz•2 minutes
Graded Quiz•10 minutes
Graded Quiz•10 minutes
Graded Quiz •10 minutes
Graded Quiz•5 minutes
2 app items•Total 30 minutes
Hands-On Lab: Load data into the Datasette from a CSV file•15 minutes
Lab: Explore your dataset using SQL queries using Datasette•15 minutes
Career Opportunities and Data Engineering in Action
Module 4•3 hours to complete
Module details
In this module, you will learn about career opportunities in the field of Data Engineering and the different paths that you can take for getting skilled as a Data Engineer. At the end of the module, you will be presented with the final graded assignment which is divided into two parts. The first part consists of a set of quiz questions, and the second part includes open-ended questions that will be reviewed and graded through an AI-graded, auto-scored assignment.
What's included
6 videos5 readings3 assignments1 app item
Show info about module content
6 videos•Total 36 minutes
Career Opportunities in Data Engineering•6 minutes
Viewpoints: Get into Data Engineering•8 minutes
Data Engineering Learning Path•4 minutes
Viewpoints: What Do Employers Look for in a Data Engineer•6 minutes
Viewpoints: The Many Paths to Data Engineering•6 minutes
Viewpoints: Advice to Aspiring Data Engineers•7 minutes
5 readings•Total 39 minutes
Data Warehousing Specialist•15 minutes
Data Manager•10 minutes
Summary and Highlights•10 minutes
Final Project Submission Guidelines and Deliverables•2 minutes
Congratulations and Next Steps•2 minutes
3 assignments•Total 75 minutes
Practice Quiz•5 minutes
Graded Quiz•20 minutes
Final Quiz•50 minutes
1 app item•Total 20 minutes
AI-Graded - Final Submission and Evaluation•20 minutes
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Instructors
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4.7 (1,220 ratings)
Rav Ahuja
IBM
55 Courses•5,043,978 learners
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Learner reviews
4.7
3,646 reviews
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AR
5·
Reviewed on Sep 25, 2021
I felt the course was the perfect introduction for someone like me that has been on the sales side of most software. I feel that it wasnt ovewhelming at all, and I mostly enjoyed the labs.
M
MF
5·
Reviewed on Mar 25, 2022
This course is optimal for the foundations and understanding of Data Engineering. Please take it and absorb all the knowledge and be amazed about your future with the Data Engineer certification.
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PT
5·
Reviewed on Apr 7, 2024
Very good introduction to Data Engineering. Not too technical, but a very good foundation of both technical concepts and soft skills required in becoming a successful data engineer.
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