IBM: Data Engineering Basics for Everyone
Learn about data engineering concepts, ecosystem, and lifecycle. Also learn about the systems, processes, and tools you need as a Data Engineer in order to gather, transform, load, process, query, and manage data so that it can be leveraged by data consumers for operations, and decision-making.
There is one session available:
Data Engineering Basics for Everyone
About this courseSkip About this course
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.
At a glance
- Institution: IBM
- Subject: Data Analysis & Statistics
- Level: Introductory
- Basic computer skills.
- General familiarity of how computing works.
- Grounding in IT systems.
- Comfortable working in either Linux, Unix, Windows, or MacOs.
- Associated programs:
- Language: English
- Video Transcript: English
- Associated skills: Reporting Tools, Data Analysis, Big Data, Business Intelligence, Relational Databases, Data Lakes, Data Warehousing, IBM Cloud Computing, Data Store, Data Integration, Operations, Decision Making, Workflow Management, Data Engineering
What you'll learnSkip What you'll learn
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
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