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Parquet to redshift data types11/1/2022 The final data model includes 4-dimension tables and 1 Fact table. But SPARK application job status can also be monitored using Spark UI on the EMR page. Here I didn’t include the SPARK EMR Cluster in the Airflow pipeline.Airflow UI allows us to monitor the status, logs, task details.To orchestrate the overall data pipeline, I used Apache Airflow as it provides an intuitive UI helping us to track the progress of our pipelines.Create fact and dimension tables according to our use.The cleaned datasets are staged in the AWS Redshift data warehouse.Ease of schema design and availability to a wide range of users.Also, it has the flexibility in adding and removing additional data.S3 is perfect for storing data partitioned and grouped in files for low cost and with a lot of flexibility.The cleaned datasets are converted to CSV and Parquet files and loaded to S3 for storage, an object storage service that offers scalability, data availability, security, and performance.Spark provides superior performance as it stores data in memory shared across the cluster. Spark is better at handling huge data records.To explore the data and clean the data set, I used Spark EMR.The data is extracted and stored in S3.The tools used in this project are notebooks, Apache Spark, Amazon S3, Amazon Redshift, Apache Airflow. The main goal of this project is to build an end-to-end data pipeline that is capable of big volumes of data. Analytical Table that can be used by analysts to explore more and develop recommendations for users.ĭeveloping a data pipeline that creates an analytical database for querying information about the reviews and ratings hosted on the Redshift.Query-based analytical tables that can be used by decision-makers.As a data engineer of the company, I took up the task of building an ETL pipeline that extracts the relevant data like listings, properties, hosts details, and load it to a data warehouse that makes querying for the decision-makers and analysts easier. To do this, they need to gather the average rating, number of ratings, and prices of the Airbnb listings over the years. Develop data processing ETL pipeline using AWS EMR, Redshift, Airflow, Data modelsĪirbnb wants to analyze the historical data of all the listings on its platform since its initial stages and improve its recommendations to its customers.Build and understand a data processing framework used for batch data loading.ETL data pipeline design using cloud data engineering tech stack and data models Introduction
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