ETL stands for extract, transform, and load. It’s a collection of processes that combine data from various sources and load them into data warehouses or other data repositories. ETL is crucial for providing data used for business intelligence and analytics.
In this course, you’ll experiment with extracting data from various database solutions such as MySQL, PostgreSQL, and MongoDB. You’ll use query and scripting languages like SQL, Python, and Apache Spark to process data and load it to data repositories or cloud solutions like Google’s GCP. Finally, you’ll learn how to schedule your ETL pipelines using cronjobs or automate and monitor them using open-source tools like Apache Airflow and Python’s pandas library.
After completing this course, you’ll have a strong grasp of various methods, tools, and techniques for transferring data from a source to its destination using ETL pipelines.
ETL stands for extract, transform, and load. It’s a collection of processes that combine data from various sources and load them...Show More
WHAT YOU'LL LEARN
An understanding of the extract, transform, and load steps in an ETL pipeline
Hands-on experience implementing and orchestrating the ETL pipelines
Understanding of databases, data warehousing, data processing, and data ingestion
Hands-on experience with ETL tools such as Python, SQL, Apache Spark, and Apache Airflow
An understanding of the extract, transform, and load steps in an ETL pipeline
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Content
1.
Introduction
14 Lessons
Get familiar with building ETL pipelines, their stages, and practical data transformation examples.
Getting StartedETL Pipeline StagesWhat Is an ETL Pipeline?A New Paradigm—ELTETL Example—ExtractionETL Transformation Example: Addressing Data Quality IssueETL Transformation Example: Handling Missing Values and DataETL Transformation Example: Sorting and Finalizing the DataETL Example—LoadETL Example—SchedulingBatch vs. Stream ProcessingData WarehouseExamples and Use CasesQuiz: ETL Pipelines
2.
E: Extract
14 Lessons
Get started with techniques for data extraction from various sources including databases, APIs, and web scraping.
IntroductionData Extraction Methods OverviewExtracting Data with Web ScrapingWeb Scraping Exercise: Reading the DataWeb Scraping Exercise: DataFrames to CSVExtraction Using a REST APIExercise: Extracting Data with a REST APIFull Extraction From MySQL DatabaseIncremental Extraction From MySQL DatabaseExtraction From MySQL’s Binary LogExtract From PostgreSQL DatabaseExtraction From Google BigQueryExercise: DatabasesQuiz: Extracting Data
3.
T: Transform
11 Lessons
Master the steps to transform raw data into usable formats through cleaning, structuring, anonymizing, and aggregating.
4.
L: Load
9 Lessons
Grasp the fundamentals of loading transformed data into repositories, hosting options, and loading strategies.
5.
Orchestration
8 Lessons
Take a closer look at orchestrating ETL pipelines with Apache Airflow, deployment, and task management.
6.
Conclusion
1 Lessons
Implement essential ETL skills and tools for effective data architecture and automation.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
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