Building a Simple Data Pipeline for Streaming Chat Conversations on GCP using terraform

Leveraging Google Cloud Platform's (GCP) robust suite of native services, including Dataflow, Pub/Sub, BigQuery, and the flexibility of Python, I've created a streamlined data pipeline to process streaming chat conversations efficiently.
Architecture Overview:
Pub/Sub as the Messaging Backbone: Incoming chat messages are ingested into Pub/Sub, providing a scalable and reliable messaging infrastructure.
Dataflow for Stream Processing: Using Apache Beam, I've developed Python code to deploy data transformation pipelines on Dataflow. This allows for real-time processing of chat messages, enabling actions like sentiment analysis, keyword extraction, and more.
BigQuery for Storage and Analysis: Processed data is seamlessly stored in BigQuery, Google's enterprise data warehouse. This enables quick and powerful analysis using SQL-like queries and integration with visualization tools.
Key Benefits:
Real-time Insights: By processing data as it arrives, the pipeline provides immediate insights into chat trends, customer sentiments, and other valuable metrics.
Scalability and Reliability: GCP's scalable infrastructure ensures the pipeline can handle varying loads, maintaining high performance and reliability.
GitHub Repo: github.com/anu...
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