Full automated data pipeline using docker images

Last update: Nov 21, 2021

Related tags

Overview

Create postgres tables from CSV files

This first section is only relate to creating tables from CSV files using postgres container alone. Just one of my experiments. If you interest, you can just follow these steps (only if your working environment support bash):

sh scripts/prep.sh

The prep.sh will handle everything for you by doing follwing:

# Start postgres db container
docker-compose -f postgres.yaml up -d
# Sleep to make sure the container is fully up running
sleep 3 

# I have problem with mouting csv files via docker compose, so here we go
# Copy csv and setup.sql to create required tables
docker cp ./csv/ my_postgres:
docker cp ./scripts/setup.sql my_postgres:setup.sql

# Execute the script in postgres db
docker exec -it my_postgres psql -p5432 --dbname=postgres --username=postgres --file=setup.sql 

# Shutdown the container
docker-compose -f postgres.yaml down --remove-orphans

I had problem with mount volumn that I can't mount the files under csv and scripts folders. Which still can be improved with a proper mount. But let's skip it for now to save time.

Initial Setup/Start Airflow container

This section will use a separate docker-compose.yaml than the above test. It will be relate due to the fact that we want to use airflow to schedule the tasks above (create table and load data). To do so, do the following. First prepare folders. You can call a new folder specifically for this if you want.

# (optional) mkdir airflow && cd airflow
mkdir ./dags ./logs ./plugins

Next we need the airflow docker-compose.yaml in our airflow directory

curl -O https://airflow.apache.org/docs/apache-airflow/stable/docker-compose.yaml

Next make sure we will have a proper permission to initial Airflow

.env ">

echo -e "AIRFLOW_UID=$(id -u)\nAIRFLOW_GID=0" > .env

Then we must initial Airflow instance

docker-compose up airflow-init

Wait until the initial finished then (you can use -d to detach if you want)

docker-compose up

Now you will be able to connect to Airflow GUI via http://localhost:8080/

Create Airflow DAG task

First thing, you need to setup connection for postgres database. Go to tab Admin > Connection > +, wow you have to fill details of the connection:

Connection Id: postgres_default
Connection Type: 'Postgres'
Host: 
   
    
Schema: postgres (default)
Login: 
    
     
Password: 
     
      
Port:

Click "Test" button to check your connection then save. Now click at the Airflow icon to return to home page. You should see task name "create_postgres_tables". Try to run by clicking start button select "Trigger DAG".

In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift.

ETL Pipeline for AWS Project Description In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift. The data is loaded from S3 t

1 Nov 1, 2021

Demonstrate a Dataflow pipeline that saves data from an API into BigQuery table

Overview dataflow-mvp provides a basic example pipeline that pulls data from an API and writes it to a BigQuery table using GCP's Dataflow (i.e., Apac

1 Dec 3, 2021

X-news - Pipeline data use scrapy, kafka, spark streaming, spark ML and elasticsearch, Kibana

5 Sep 28, 2022

An ETL Pipeline of a large data set from a fictitious music streaming service named Sparkify.

An ETL Pipeline of a large data set from a fictitious music streaming service named Sparkify. The ETL process flows from AWS's S3 into staging tables in AWS Redshift.

1 Feb 11, 2022

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities. This is aimed at those looking to get into the field of Data Science or those who are already in the field and looking to solve a real-world project with python.

1 Dec 26, 2021

Flenser is a simple, minimal, automated exploratory data analysis tool.

Flenser Have you ever been handed a dataset you've never seen before? Flenser is a simple, minimal, automated exploratory data analysis tool. It runs

79 Sep 20, 2022

Lale is a Python library for semi-automated data science.

Lale is a Python library for semi-automated data science. Lale makes it easy to automatically select algorithms and tune hyperparameters of pipelines that are compatible with scikit-learn, in a type-safe fashion.

293 Dec 29, 2022

Data Competition: automated systems that can detect whether people are not wearing masks or are wearing masks incorrectly

Table of contents Introduction Dataset Model & Metrics How to Run Quickstart Install Training Evaluation Detection DATA COMPETITION The COVID-19 pande

1 Feb 27, 2022

Pipeline and Dataset helpers for complex algorithm evaluation.

tpcp - Tiny Pipelines for Complex Problems A generic way to build object-oriented datasets and algorithm pipelines and tools to evaluate them pip inst

Machine Learning and Data Analytics Lab FAU

3 Dec 7, 2022

Releases(airflow-postgres-dag)

airflow-postgres-dag(Nov 22, 2021)

Allow you to start Airflow container from official page and running you first DAG to load data into postgres tables.
Source code(tar.gz)
Source code(zip)
postgres-csv(Nov 21, 2021)

v0.1.0-postgres-csv
Source code(tar.gz)
Source code(zip)

Full automated data pipeline using docker images

Related tags

Overview

Create postgres tables from CSV files

Initial Setup/Start Airflow container

Create Airflow DAG task

You might also like...

In this project, ETL pipeline is build on data warehouse hosted on AWS Redshift.

Demonstrate a Dataflow pipeline that saves data from an API into BigQuery table

X-news - Pipeline data use scrapy, kafka, spark streaming, spark ML and elasticsearch, Kibana

An ETL Pipeline of a large data set from a fictitious music streaming service named Sparkify.

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Flenser is a simple, minimal, automated exploratory data analysis tool.

Lale is a Python library for semi-automated data science.

Data Competition: automated systems that can detect whether people are not wearing masks or are wearing masks incorrectly

Pipeline and Dataset helpers for complex algorithm evaluation.

Releases(airflow-postgres-dag)

airflow-postgres-dag(Nov 22, 2021)

postgres-csv(Nov 21, 2021)

Owner

Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required)

A real data analysis and modeling project - restaurant inspections

Churn prediction with PySpark

MDAnalysis is a Python library to analyze molecular dynamics simulations.

Py-price-monitoring - A Python price monitor

WithPipe is a simple utility for functional piping in Python.

Performance analysis of predictive (alpha) stock factors

This mini project showcase how to build and debug Apache Spark application using Python

This tool parses log data and allows to define analysis pipelines for anomaly detection.

Parses data out of your Google Takeout (History, Activity, Youtube, Locations, etc...)

This repo contains a simple but effective tool made using python which can be used for quality control in statistical approach.

Describing statistical models in Python using symbolic formulas

Autopsy Module to analyze Registry Hives based on bookmarks provided by EricZimmerman for his tool RegistryExplorer

DenseClus is a Python module for clustering mixed type data using UMAP and HDBSCAN

Used for data processing in machine learning, and help us to construct ML model more easily from scratch

[CVPR2022] This repository contains code for the paper "Nested Collaborative Learning for Long-Tailed Visual Recognition", published at CVPR 2022

A Python Tools to imaging the shallow seismic structure

Processo de ETL (extração, transformação, carregamento) realizado pela equipe no projeto final do curso da Soul Code Academy.

Top 50 best selling books on amazon

Package for decomposing EMG signals into motor unit firings, as used in Formento et al 2021.