What is a data pipeline in this course?

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.

Engineering Data Ecosystems: Pipelines, ETL, Spark

This course is part of Building Smarter Data Pipelines: SQL, Spark, Kafka & GenAI Specialization

Instructors: Soheil Haddadi

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1 module

Gain insight into a topic and learn the fundamentals.

10 reviews

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

1 module

Gain insight into a topic and learn the fundamentals.

10 reviews

Beginner level

Recommended experience

3 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Identify and describe the components and importance of data ecosystems.
Understand the basic structure and function of data pipelines.
Recognize the steps involved in ETL workflows and their role in data handling.
Gain an introductory knowledge of big data and the application of Apache Spark.

Skills you'll gain

Tools you'll learn

Apache Spark

Details to know

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Assessments

3 assignments¹

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Taught in English

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of learners achieved a positive career outcome

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This course is part of the Building Smarter Data Pipelines: SQL, Spark, Kafka & GenAI Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There is 1 module in this course

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.

Module details

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.