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Master ETL processes, explore data extraction from MySQL, PostgreSQL, and MongoDB, and learn about scheduling and automating ETL pipelines using tools like Apache Airflow and Python.

testing.tar.gz

pymysql

Flask

mysql_fullExtract

binlog1 - connect_to_binglog

binlog2 - process_events

binlog3 - export_to_csv

gcloud

spark_MCAR

spark_MAR

spark_MNAR

spark_MNAR2

spark_MNAR3

spark_MNAR4

spark_duplicates1

spark_duplicates2

spark_duplicates3

spark_duplicates4

spark_duplicates5

spark_duplicates6

spark_duplicates7

spark_duplicates8

flight_data1

flight_data2

flight_data3

flight_data4

flight_data5

database_exercise

database_exercise1

spark_cleanData1

spark_cleanData2

spark_cleanData3

airflow_standalone

airflow_standalone-copy

airflow_standalone1

pipeline1_extract1

pipeline1_extract1-copy

pipeline1_extract

pipeline1_extract_data-copy

pipeline1_transform1

pipeline1_transform2

pipeline1_transform3

airflowtesting

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.

Transferring Data with ETL

What does the acronym ETL stand for?

Which data source is most likely to be used in an ETL pipeline?

Which data source is most likely to be used in an ETL pipeline?

Eliminating duplicate rows and fixing inconsistent data



Creating multiple indexes to optimize the performance of queries in the database

Aggregating the data using count, sum, min, max, or average functions

**(Select all that apply.)** What are some transformation tasks we might face in the transform stage of the ETL? 

(Select all that apply.) What are some transformation tasks we might face in the transform stage of the ETL?

ELT pipeline transforms the data before the extract stage. This provides better flexibility to the transformation stage.

ELT pipelines provide more flexibility to the transformation stage. Users can change the logic of the transformation without having to extract and load the data again.

ELT pipeline is suitable for working with big data because it is a user-friendly tool and can be operated by non-technical users.

ELT pipeline transforms the data after the load stage. Users don't have to process the data according to a predefined format or schema, which is valuable when working with raw and unstructured big data.

**(Select all that apply.)** Why is an ELT pipeline suitable for working with big data?

(Select all that apply.) Why is an ELT pipeline suitable for working with big data?

What’s the main performance metric in batch processing pipelines?

What’s the main performance metric in batch processing pipelines?

A data warehouse is a data repository that stores historical data for analytical and BI purposes.


A data warehouse is a data repository that stores real-time data about transactions.

A data warehouse is a data repository used for backing up other databases.

A data warehouse is a general term for any database, OLTP, or OLAP.

A data warehouse is better designed to process transactions and ongoing operations.

A data warehouse is better designed to process large amounts of data.

A data warehouse is designed to handle a large number of small CRUD (create, read, update, delete) queries.

The data in the data warehouse is denormalized, which requires less complex join queries that cause heavy load and overhead.

**(Select all that apply.)** What are some advantages of a data warehouse over a traditional database?


(Select all that apply.) What are some advantages of a data warehouse over a traditional database?

What’s the name of the schema design choice that many data warehouses choose to use?

What’s the name of the schema design choice that many data warehouses choose to use?

Data marts make it easy to access relevant and specific data for each department in the organization.

Querying and analyzing data from data marts is usually faster because they contain only a subset of the data that data warehouses store.

Data marts can help to reduce the complexity of the overall data warehouse system, making it easier to manage and maintain.

Data marts allow organizations to utilize parallel processing for heavy analytical tasks.

What’s not a benefit of data marts?

Test your knowledge of ETL pipelines with this quiz.

Introduction

E: Extract

T: Transform

L: Load

Orchestration

ETL Pipeline: Fraud Detection Preprocessing

Conclusion

Build a News ETL Data Pipeline Using Python and SQLite

Quiz: ETL Pipelines