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The best way to Construct Easy ETL Pipelines With GitHub Actions

Photograph by Roman Synkevych 🇺🇦 on Unsplash

Should you’re into software program improvement, you’d know what GitHub actions are. It’s a utility by GitHub to automate dev duties. Or, in in style language, a DevOps device.

However folks hardly use it for constructing ETL pipelines.

The very first thing that involves thoughts when discussing ETLs is Airflow, Prefect, or associated instruments. They’re, certainly, one of the best available in the market for activity orchestration. However many ETLs we construct are easy, and internet hosting a separate device for them is usually overkill.

You need to use GitHub Actions as an alternative.

This text focuses on GitHub Actions. However if you happen to’re on Bitbucket or GitLab, you might use their respective alternate options too.

We will run our Python, R, or Julia scripts on GitHub Actions. In order a knowledge scientist, you don’t must be taught a brand new language or device for this matter. You can even get e mail notifications when any of your ETL duties fail.

You may nonetheless take pleasure in 2000min of computation month-to-month if you happen to’re on a free account. You may attempt GitHub motion if you happen to can estimate your ETL workload inside this vary.

How will we begin constructing ETLs on GitHub Actions?

Getting began with the GitHub actions is straightforward. You can comply with the official doc. Or the three easy steps are as follows.

In your repository, create a listing at .github/workflows . Then create the YAML config file actions.yaml inside it with the next content material.

title: ETL Pipeline

on:
schedule:
- cron: '0 0 * * *' # Runs at 12.00 AM day-after-day

jobs:
etl:
runs-on: ubuntu-latest
steps:
- title: Checkout code
makes use of: actions/checkout@v2

- title: Arrange Python
makes use of: actions/setup-python@v2
with:
python-version: '3.9'

- title: Extract knowledge
run: python extract.py

- title: Remodel knowledge
run: python rework.py

- title: Load knowledge
run: python load.py

The above YAML automates an ETL (Extract, Remodel, Load) pipeline. The workflow is triggered day-after-day at 12:00 AM UTC, and it consists of a single job that runs on the ubuntu-latest surroundings (No matter that’s out there on the time.)

The steps of those configurations are easy.

The job has 5 steps: the primary two steps try the code and arrange the Python surroundings, respectively, whereas the subsequent three steps execute the extract.py, rework.py, and load.py Python scripts sequentially.

This workflow supplies an automatic and environment friendly method of extracting, reworking, and loading knowledge every day utilizing GitHub Actions.

The Python scripts might fluctuate relying on the situation. Right here’s one in every of some ways.

# extract.py
# --------------------------------
import requests

response = requests.get("https://api.instance.com/knowledge")
with open("knowledge.json", "w") as f:
f.write(response.textual content)

# rework.py
# --------------------------------
import json

with open("knowledge.json", "r") as f:
knowledge = json.load(f)

# Carry out transformation
transformed_data = [item for item in data if item["key"] == "worth"]

# Save remodeled knowledge
with open("transformed_data.json", "w") as f:
json.dump(transformed_data, f)

# load.py
# --------------------------------
import json
from sqlalchemy import create_engine, Desk, Column, Integer, String, MetaData

# Connect with database
engine = create_engine("postgresql://myuser:mypassword@localhost:5432/mydatabase")

# Create metadata object
metadata = MetaData()

# Outline desk schema
mytable = Desk(
"mytable",
metadata,
Column("id", Integer, primary_key=True),
Column("column1", String),
Column("column2", String),
)

# Learn remodeled knowledge from file
with open("transformed_data.json", "r") as f:
knowledge = json.load(f)

# Load knowledge into database
with engine.join() as conn:
for merchandise in knowledge:
conn.execute(
mytable.insert().values(column1=merchandise["column1"], column2=merchandise["column2"])
)

The above scripts learn from a dummy API and push it to a Postgres database.

Issues to contemplate when deploying ETL pipelines to GitHub Actions.

1. Safety: Hold your secrets and techniques safe through the use of GitHub’s secret retailer and keep away from hardcoding secrets and techniques into your workflows.

Have you ever already observed that the pattern code I’ve given above has database credentials? It’s not proper for a manufacturing system.

We now have different methods to securely embed secrets and techniques, like database credentials.

Should you don’t encrypt your secrets and techniques in GitHub Actions, they are going to be seen to anybody who has entry to the repository’s supply code. Which means that if an attacker positive aspects entry to the repository or the repository’s supply code is leaked; the attacker will be capable to see your secret values.

To guard your secrets and techniques, GitHub supplies a characteristic known as encrypted secrets and techniques, which lets you retailer your secret values securely within the repository settings. Encrypted secrets and techniques are solely accessible to licensed customers and are by no means uncovered in plaintext in your GitHub Actions workflows.

Right here’s the way it works.

Within the repository settings sidebar, you could find the secrets and techniques and variables for Actions. You may create your variables right here.

Screenshot by the creator.

Secrets and techniques created right here usually are not seen to anybody. They’re encrypted and can be utilized within the workflow. Even you may’t learn them. However you may replace them with a brand new worth.

When you created the secrets and techniques, you may cross in them utilizing the GitHub Actions configuration as an surroundings variable. Right here’s the way it works:

title: ETL Pipeline

on:
schedule:
- cron: '0 0 * * *' # Runs at 12.00 AM day-after-day

jobs:
etl:
runs-on: ubuntu-latest
steps:
...

- title: Load knowledge
env: # Or as an surroundings variable
DB_USER: ${{ secrets and techniques.DB_USER }}
DB_PASS: ${{ secrets and techniques.DB_PASS }}
run: python load.py

Now, we are able to modify the Python scripts to learn credentials from surroundings variables.

# load.py
# --------------------------------
import json
import os
from sqlalchemy import create_engine, Desk, Column, Integer, String, MetaData

# Connect with database
engine = create_engine(
f"postgresql://{os.environ['DB_USER']}:{os.environ['DB_PASS']}@localhost:5432/mydatabase"
)

2. Dependencies: Make certain to make use of the proper model of dependencies to keep away from any points.

Your Python undertaking might have already got a necessities.txt file that specifies dependencies together with their variations. Or, for extra refined initiatives, it’s possible you’ll be utilizing fashionable dependency administration instruments like Poetry.

You need to have a step to arrange your surroundings earlier than you run the opposite items of your ETL. You are able to do this by specifying the next in your YAML configuration.

- title: Set up dependencies
run: pip set up -r necessities.txt

3. Timezone settings: GitHub actions use UTC timezone, and as of penning this put up, you may’t change it.

Thus you could make sure you’re utilizing the proper timezone. You need to use a web-based converter or manually modify your native time to UTC earlier than configuring.

The most important caveat of GitHub motion scheduling is its uncertainty within the execution time. Although you’ve configured it to run at a particular cut-off date, if the demand is excessive at that time, your job will likely be qued. Thus, there will likely be a brief delay within the precise job beginning time.

In case your job is dependent upon precise execution time, utilizing GitHub Actions scheduling might be not possibility. Utilizing a self-hosted runner in GitHub actions might assist.

4. Useful resource Utilization: Keep away from overloading the sources supplied by GitHub.

Although GitHub actions, even with a free account, has 2000 minutes of free run time, if you happen to use a special OS than Linux, guidelines change a bit.

Should you’re utilizing a Home windows runtime, you’ll get solely half of it. In a MacOS surroundings, you’ll solely get one-tenth of it.

Conclusion

GitHub actions is a DevOps device. However we are able to use it to run any scheduled duties. On this put up, we’ve mentioned the right way to create an ETL that periodically fetches an API and pushes the information to a dataframe.

For easy ETLs, this method is straightforward to develop and deploy.

However scheduled jobs in GitHub actions don’t must run at the very same time. Therefore for time bounded duties, this isn’t appropriate.

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