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README.md

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# Tickit Data Lake : Building a Data Lake Using an Orchestrator + AWS Resources
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# Tickit Data Pipeline
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## Overview
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Welcome to the Tickit Data Lake project! The Tickit Data Lake project demonstrates the construction
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of a scalable and robust 3-tier data lake on AWS, leveraging the power of Apache Airflow for orchestration
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of a scalable and robust data pipeline, leveraging the power of Apache Airflow for orchestration
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and automation. This project provides a practical example of building a modern data pipeline capable of
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handling the extraction, loading, and transformation (ELT) of batch data, specifically designed to support
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the analytical needs of a business using the Tickit Dataset.
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the analytical needs of a business using the Tickit Dataset as a case study.
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## Key Features and Technologies:
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dependencies between tasks are correctly handled. Airflow's robust features enable task retries, logging,
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and alerting, ensuring pipeline reliability.
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- AWS Integration: The project seamlessly integrates with various AWS resources, including:
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1. EC2: Reliable and highly available computing for running the orchestrator.
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2. S3: Scalable object storage for the Bronze, Silver, and Gold layers.
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3. Redshift: Scalable data warehouse used for providing a high-performance analytical database.
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- Integration of Multiple Data Sources: The project seamlessly integrates with various data sources including:
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1. On-premises SQL and NoSQL databases
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2. Cloud-hosted SQL and NoSQL databases
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## Value
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This project serves as a valuable example of building a modern data lake on AWS using Airflow, showcasing best
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This project serves as a valuable example of building a modern data pipelines using Airflow, showcasing best
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practices for data ingestion, processing, and transformation. It provides a solid foundation for building a
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robust data platform to support a wide range of analytical needs.
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