Extracting data from structured sources

ETL and ELT in Python

Jake Roach

Data Engineer

Source systems

In this course:

CSV files
Parquet files
JSON files
SQL databases

Data is also sourced from:

APIs
Data lakes
Data warehouses
Web scraping
... and so many more!

Reading in parquet files

Parquet files:

Open source, column-oriented file format designed for efficient field storage and retrieval
Similar to working with CSV files

import pandas as pd

# Read the parquet file into memory
raw_stock_data = pd.read_parquet("raw_stock_data.parquet", engine="fastparquet")

¹ https://www.databricks.com/glossary/what-is-parquet

Connecting to SQL databases

Data can be pulled from SQL databases into a pandas DataFrame
Requires a connection URI to build an engine, and connect to the database

import sqlalchemy
import pandas as pd

# Connection URI: schema_identifier://username:password@host:port/db
connection_uri = "postgresql+psycopg2://repl:password@localhost:5432/market"
db_engine = sqlalchemy.create_engine(connection_uri)

# Query the SQL database
raw_stock_data = pd.read_sql("SELECT * FROM raw_stock_data LIMIT 10", db_engine)

Modularity

Separating logic into functions

Increases readability within a pipeline
Adheres to the principle "don't repeat yourself"
Expedites troubleshooting

def extract_from_sql(connection_uri, query):
    # Create an engine, query data and return DataFrame
    db_engine = sqlalchemy.create_engine(connection_uri)
    return pd.read_sql(query, db_engine)

extract_from_sql("postgresql+psycopg2://.../market", "SELECT ... LIMIT 10;")

Let's practice!

ETL and ELT in Python