Kinesis-Snowflake-Data-Pipeline

This project exemplifies a robust and scalable approach to building a modern data pipeline, integrating streaming data ingestion with advanced data processing and transformation, making it an impressive addition to any data engineering portfolio.

Kinesis-Snowflake Data Pipeline Project

The Kinesis-Snowflake Data Pipeline Project is an end-to-end data pipeline leveraging various AWS services and Snowflake to efficiently handle and process streaming data. This project showcases the seamless integration of cloud-based data ingestion, processing, and transformation workflows.

Architecture Overview

The architecture of the above data pipeline involves several key components, as illustrated in the diagram below:

1. Data Source (EC2 Instance):

   • The data pipeline starts with an EC2 instance, which serves as an application emitting logs. This instance holds customer and order datasets, simulating real-time data generation.

2. Kinesis Agent:

   • A Kinesis Agent installed on the EC2 instance captures the streaming data logs and sends them to Kinesis Data Firehose.

3. Kinesis Data Firehose:

   • Kinesis Data Firehose acts as the data transport layer, continuously delivering the streaming data to Amazon S3.

4. Amazon S3 Zones:

   • The data pipeline employs three distinct S3 zones:
     • Landing Zone: Initial storage location for raw data ingested from Kinesis Firehose.
     • Processing Zone: Intermediate storage where data transformation and processing take place.
     • Processed Zone: Final storage location for the transformed and processed data.

5. Apache Airflow (MWAA):

   • Apache Airflow, managed via Amazon Managed Workflows for Apache Airflow (MWAA), orchestrates the data pipeline. The Airflow Directed Acyclic Graphs (DAGs) manage the data transfer and processing workflows.
   • The DAGs are triggered to move data from the Landing Zone to the Processing Zone.

6. Snowflake:

   • Snowflake performs data ingestion and transformation operations in the Processing Zone. It processes the data, ensuring it is clean and structured.
   • Once the transformation is complete, the processed data is stored back in the S3 Processed Zone for further analysis or downstream processing.

Workflow

1. Data Ingestion:

   • The EC2 instance generates logs containing customer and order data.
   • The Kinesis Agent captures these logs and streams them to Kinesis Data Firehose.

2. Data Delivery to S3:

   • Kinesis Data Firehose delivers the raw data to the S3 Landing Zone.

3. Data Processing:

   • Apache Airflow DAGs trigger data transfer from the Landing Zone to the Processing Zone.
   • Within the Processing Zone, Snowflake ingests the data and performs necessary transformations.

4. Storing Processed Data:

   • The transformed data is moved to the S3 Processed Zone for storage.

Key Benefits

• Scalability: The architecture leverages AWS services that can scale with increasing data volumes.
• Real-Time Processing: Kinesis Data Firehose ensures real-time data streaming and ingestion.
• Efficient Orchestration: Apache Airflow automates and manages complex data workflows.
• Robust Data Transformation: Snowflake provides powerful data transformation capabilities.
• Cost-Effective Storage: Amazon S3 offers cost-effective and durable storage for raw and processed data.