Introduction to Data Ingestion and ETL

Data ingestion and ETL (Extract, Transform, Load) form the foundation of data engineering. In this lesson, we will explore the basic concepts of data ingestion and ETL and understand their role in data engineering.

Data ingestion refers to the process of taking data from various sources and bringing it into a storage system, such as a data lake or a data warehouse. It involves extracting data from source systems, transforming it into a consistent format, and loading it into the target storage system.

ETL, on the other hand, is the process of extracting data from source systems, transforming it to meet the desired requirements, and loading it into the target system. ETL encompasses a series of steps including data extraction, data transformation, and data loading.

As a data engineer, you will often work with different tools and technologies to perform data ingestion and ETL tasks. Some of the commonly used tools include:

• Snowflake: A cloud-based data warehousing platform
• SQL: A programming language for managing relational databases
• Spark: A fast and general-purpose cluster computing system
• Docker: A platform for automating the deployment of applications in containers

Let's take a look at an example of data ingestion using Python and Pandas:

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This code snippet demonstrates how to read data from a CSV file using Pandas, a popular data manipulation library in Python. We first import the pandas library, then use the read_csv function to read the data from a CSV file named data.csv. Finally, we print the first 5 rows of the data using the head function.

By understanding the concepts of data ingestion and ETL, you will be well-equipped to handle the challenges of managing and processing data in a data engineering role.

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import pandas as pd

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# Read data from a CSV file

data = pd.read_csv('data.csv')

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# Print the first 5 rows of the data

print(data.head())