This course is designed to provide you with a foundational understanding of how modern data ecosystems work. From data pipelines to ETL processes, and big data handling using Apache Spark, you’ll explore the essential tools, techniques, and technologies that drive decision-making in today’s data-driven world. Whether you’re an aspiring data engineer or someone interested in the mechanics of data handling, this course will lay the groundwork for your journey into the exciting field of data engineering.
This course is ideal for aspiring data engineers, software developers, database administrators, and IT professionals looking to expand their skills in data handling and processing. Additionally, analysts and business professionals interested in data technologies will find the course beneficial for enhancing their understanding of the fundamental processes behind data ecosystems and big data.
Participants should have a general interest in data and a basic understanding of programming concepts. Familiarity with database systems will be helpful, but prior experience with Spark is not required. An interest in big data and data analytics will enrich your learning experience throughout the course.
By the end of this course, participants will be able to identify the components and importance of data ecosystems, understand the structure and function of data pipelines, and recognize the critical steps involved in ETL workflows. Additionally, you'll gain introductory knowledge of big data handling with Apache Spark and its applications in large-scale data processing.
This course serves as an introductory course aimed at unraveling the complexities of data ecosystems. It's tailored for individuals at the onset of their data engineering journey, emphasizing the construction, management, and optimization of data pipelines, the essentials of ETL (Extract, Transform, Load) workflows, and an introduction to big data processing with Apache Spark.
涵盖的内容
12个视频4篇阅读材料3个作业
显示有关单元内容的信息
12个视频•总计61分钟
Introduction to the Course & Meet Your Instructor•2分钟
Explaining the Role of Data Ecosystems•5分钟
Identifying Data Sources and Design Principles•6分钟
Applying Tools and Technologies for Data Pipelines•4分钟
Examining ETL Principles•6分钟
Identifying Tools and Technologies for ETL•5分钟
Examining Big Data Challenges and Solutions•6分钟
Decoding Apache Spark and its features•7分钟
Applying insights for using Spark•8分钟
Analyse designing Scalable Data Solutions with Spark•5分钟
Implementing ETL Workflows with Spark•5分钟
Congratulations and Continuous Learning Journey•1分钟
4篇阅读材料•总计20分钟
Welcome to the Course: Course Overview•5分钟
The Crucial Role of Data Engineers: Data Management and Analysis•5分钟
Maximizing Business Value with ETL for Big Data•5分钟
First Steps With PySpark and Big Data Processing•5分钟
3个作业•总计80分钟
Engineering Data Ecosystems: Pipelines, ETL, Spark•20分钟
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In this course, a data pipeline is a connected process for moving data from its sources through preparation steps into a usable form. The emphasis is on understanding the main parts of that workflow, how ETL supports it, and how it fits into a modern data ecosystem.
When would you use a data pipeline?
You would use a data pipeline when data needs to be collected, prepared, and moved in a repeatable way instead of being handled as one-off tasks. In this course, that includes situations with multiple data sources, regular updates, or larger volumes of data that need a consistent workflow.
How does a data pipeline fit into a broader workflow?
A data pipeline connects the earlier stages of gathering data to the later stages where that data is stored, transformed, and used. The course places pipelines within a broader data ecosystem and shows how ETL fits inside that connected process.
How is a data pipeline different from handling data in separate manual steps?
A data pipeline is a connected workflow with defined stages, while separate manual steps are handled one at a time without the same structure or continuity. In this course, pipelines are presented as a way to organize data movement and transformation into a repeatable process.
Do you need any prerequisites before learning about data pipelines?
A basic understanding of programming concepts is helpful, and some familiarity with database systems can make the material easier to follow. The course is beginner level and does not assume prior Spark experience.
What tools, platforms, or methods are used in this course?
The course introduces ETL as the main data-handling method and Apache Spark as the main named platform for working with big data. It also surveys the basic tools and technologies used to build and manage data pipelines.
What specific tasks will you practice or complete in this course?
You will identify data ecosystem and pipeline components, examine ETL stages, and explore common big data challenges. You will also compare basic tool choices and use introductory Spark concepts to think through scalable data workflows.