san089 / udacity-data-engineering-projects

Few projects related to Data Engineering including Data Modeling, Infrastructure setup on cloud, Data Warehousing and Data Lake development.

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

udacity-data-engineering-projects's Issues

No edits in the Airflow Cloud Formation script reflects in the

Hey!
I'm trying to replicate some of the steps you followed to create an Airflow server on CloudFormation.. but even editing the section "UserData" of the EC2 instance, my changes are not reflected on the machine, i.e. I don't have apache airflow installed, python is still the 2.7 version, ecc..
Do you have any idea how i can solve this?

Thanks!
Saverio

Can not connect the Sparkifykdb

Hi, can you connect with the Sparkifydb???

etl.py user table data overwritten in artists output directory

etl.py user table data overwritten in artists output directory

users_table.write.parquet(os.path.join(output_data, "artists/") , mode="overwrite")

Portfolio

Check if IAM role already exists and then skip creating new one.

Check if IAM role already exists and then skip creating a new one.

try:
        create_response = iam_client.create_role(
                    Path='/',
                    RoleName=role_name,
                    Description=role_description,
                    AssumeRolePolicyDocument = role_policy_document
        )
        logger.debug(f"Got response from IAM client for creating role : {create_response}")
        logger.info(f"Role create response code : {create_response['ResponseMetadata']['HTTPStatusCode']}")
    except Exception as e:
        logger.error(f"Error occured while creating role : {e}")
        return False