# san089/goodreads_etl_pipeline

An end-to-end GoodReads Data Pipeline for Building Data Lake, Data Warehouse and Analytics Platform.

Repository: https://github.com/san089/goodreads_etl_pipeline
Canonical: https://ross.abutalabs.com/products/goodreads_etl_pipeline
Language: Python
License: MIT
License Family: permissive
Topics: etl-pipeline, etl-framework, spark, apache-spark, apache-airflow, airflow, redshift, emr-cluster, livy, s3, warehouse, data-lake, scheduler, data-migration, data-engineering, data-engineering-pipeline, python, goodreads-data-pipeline, airflow-dag, etl-job
Last push: 2020-03-09T00:59:15+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2393, "days_push": 2369, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1542, forks 247 (observed 2026-08-28T04:05:01.142655+00:00)

## What it is
An end-to-end ETL pipeline that ingests Goodreads API data into an AWS S3 data lake, transforms it with Spark on EMR, and loads it into a Redshift data warehouse. Apache Airflow orchestrates the jobs, data quality checks, and analytics queries.

## Use cases
- build a data lake and warehouse from Goodreads API data
- learn how to orchestrate Spark ETL jobs with Airflow
- set up an end-to-end data engineering pipeline on AWS
- load S3 data into Redshift with upserts
- run scheduled data quality checks on warehouse tables
- practice building an analytics platform on cloud infrastructure

## When to choose
- you want a reference architecture for an AWS-based ETL pipeline
- you're learning Airflow, Spark, EMR, and Redshift integration
- you need a template for landing/working/processed zone data lake patterns

## When to avoid
- you need a production-ready, actively maintained pipeline (last release 2020)
- you don't use AWS services like S3, EMR, or Redshift
- you want a lightweight local ETL without cloud infrastructure costs

## Facets
- artifact type: application
- maturity: maintenance
- function: etl, streaming, data-science, analytics
- domain: big-data, cloud-computing
- platform: python, cloud
- tags: apache-airflow, apache-spark, aws-redshift, aws-s3, data-lake, data-warehouse, goodreads-api, emr, data-quality-checks, orchestration, data-engineering, aws, docker, linux

## Member repositories
- san089/goodreads_etl_pipeline (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:01.142655+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:45.783671+00:00, confidence not recorded.
  - readme: https://github.com/san089/goodreads_etl_pipeline (fetched 2026-08-28T04:05:01.142655+00:00, sha b726b96566a2)
- Data as of 2026-08-30T08:39:29.467469+00:00.