Engineering Data Ecosystems: Pipelines, ETL, Spark
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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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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.
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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, youβll explore the essential tools, techniques, and technologies that drive decision-making in todayβs data-driven world. Whether youβre 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.
What's included
12 videos4 readings3 assignments
12 videosβ’Total 61 minutes
- Introduction to the Course & Meet Your Instructorβ’2 minutes
- Explaining the Role of Data Ecosystemsβ’5 minutes
- Identifying Data Sources and Design Principlesβ’6 minutes
- Applying Tools and Technologies for Data Pipelinesβ’4 minutes
- Examining ETL Principlesβ’6 minutes
- Identifying Tools and Technologies for ETLβ’5 minutes
- Examining Big Data Challenges and Solutionsβ’6 minutes
- Decoding Apache Spark and its featuresβ’7 minutes
- Applying insights for using Sparkβ’8 minutes
- Analyse designing Scalable Data Solutions with Sparkβ’5 minutes
- Implementing ETL Workflows with Sparkβ’5 minutes
- Congratulations and Continuous Learning Journeyβ’1 minute
4 readingsβ’Total 20 minutes
- Welcome to the Course: Course Overviewβ’5 minutes
- The Crucial Role of Data Engineers: Data Management and Analysisβ’5 minutes
- Maximizing Business Value with ETL for Big Dataβ’5 minutes
- First Steps With PySpark and Big Data Processingβ’5 minutes
3 assignmentsβ’Total 80 minutes
- Engineering Data Ecosystems: Pipelines, ETL, Sparkβ’20 minutes
- Big Data Engineering Solutionsβ’30 minutes
- Apache Spark Implementation and Designβ’30 minutes
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Frequently asked questions
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.
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.
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.
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