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README

Data Engineering Projects

Project 1: Data Modeling with Postgres

In this project, we apply Data Modeling with Postgres and build an ETL pipeline using Python. A startup wants to analyze the data they've been collecting on songs and user activity on their new music streaming app. Currently, they are collecting data in json format and the analytics team is particularly interested in understanding what songs users are listening to.

Link: Data_Modeling_with_Postgres

Project 2: Data Modeling with Cassandra

In this project, we apply Data Modeling with Cassandra and build an ETL pipeline using Python. We will build a Data Model around our queries that we want to get answers for. For our use case we want below answers:

  • Get details of a song that was herad on the music app history during a particular session.
  • Get songs played by a user during particular session on music app.
  • Get all users from the music app history who listened to a particular song.

Link : Data_Modeling_with_Apache_Cassandra

Project 3: Data Warehouse

In this project, we apply the Data Warehouse architectures we learnt and build a Data Warehouse on AWS cloud. We build an ETL pipeline to extract and transform data stored in json format in s3 buckets and move the data to Warehouse hosted on Amazon Redshift.

Use Redshift IaC script - Redshift_IaC_README

Link - Data_Warehouse

Project 4: Data Lake

In this project, we will build a Data Lake on AWS cloud using Spark and AWS EMR cluster. The data lake will serve as a Single Source of Truth for the Analytics Platform. We will write spark jobs to perform ELT operations that picks data from landing zone on S3 and transform and stores data on the S3 processed zone.

Link: Data_Lake

Project 5: Data Pipelines with Airflow

In this project, we will orchestrate our Data Pipeline workflow using an open-source Apache project called Apache Airflow. We will schedule our ETL jobs in Airflow, create project related custom plugins and operators and automate the pipeline execution.

Link: Airflow_Data_Pipelines

Project 6: Api Data to Postgres

In this project, we build an etl pipeline to fetch data from yelp API and insert it into the Postgres Database. This project is a very basic example of fetching real time data from an open source API.

Link: API to Postgres

CAPSTONE PROJECT

Udacity provides their own crafted Capstone project with dataset that include data on immigration to the United States, and supplementary datasets that include data on airport codes, U.S. city demographics, and temperature data.

I worked on my own open-ended project.

Here is the link - goodreads_etl_pipeline

Core symbols most depended-on inside this repo

execute
called by 18
Airflow_Data_Pipelines/plugins/operators/load_fact.py
load_dimension_subdag
called by 4
Airflow_Data_Pipelines/dags/sparkify_dimension_subdag.py
get_group
called by 3
Redshift_Cluster_IaC.py
execute_query
called by 3
Data_Api_to_Postgres/databasedriver.py
process_data
called by 2
Data_Modeling_with_Postgres/etl.py
create_IAM_role
called by 1
Redshift_Cluster_IaC.py
delete_IAM_role
called by 1
Redshift_Cluster_IaC.py
create_cluster
called by 1
Redshift_Cluster_IaC.py

Shape

Function 31
Method 20
Class 10

Languages

Python100%

Modules by API surface

Redshift_Cluster_IaC.py9 symbols
Data_Api_to_Postgres/businesssearch.py6 symbols
Data_Modeling_with_Postgres/etl.py4 symbols
Data_Modeling_with_Postgres/create_tables.py4 symbols
Data_Lake/etl.py4 symbols
Data_Api_to_Postgres/databasedriver.py4 symbols
Data_Warehouse/etl.py3 symbols
Data_Warehouse/create_tables.py3 symbols
Data_Api_to_Postgres/request.py3 symbols
Airflow_Data_Pipelines/plugins/operators/stage_redshift.py3 symbols
Airflow_Data_Pipelines/plugins/operators/load_fact.py3 symbols
Airflow_Data_Pipelines/plugins/operators/load_dimension.py3 symbols

Datastores touched

airflowdbDatabase · 1 repos

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