Cloud Functions - How to Read PDF Files on GCS Events and Store in BigQuery

In this tutorial, you will learn "How to create an event-driven Cloud Function that reads PDF files from Google Cloud Storage (GCS) and pushes their contents into BigQuery" in GCP.

Google Cloud Platform - How to Create Gen2 Cloud Function

In this article, you will learn how to Create Python based Gen2 Cloud function in Google Cloud Platform.

Cloud Functions are a serverless computing service offered by Google Cloud Platform (GCP) which are an easy way to run your code in the cloud. 

It supports Java, Python, Ruby, Node.js, Go, and .Net.

Currently, Google Cloud Functions support events from the following providers- HTTP, Cloud Storage, Cloud Firestore, Pub/Sub, Firebase, and Stackdriver.

Gen1 is more lightweight, one concurrency per instance, simple features and less knob to tweak, cheaper, it's pretty much deploy and forget, it is actually an AppEngine standard, 

while gen2 is on Cloud Run (on GKE), you have more control, up to 1k concurrency per instance, larger resources, longer timeouts, etc, If you don't need it, just use gen1.

To complete the tasks outlined above, you must have a GCP account and appropriate access. 

Data validation — Data validation is the process of checking the data against predefined rules and standards, such as data types, formats, ranges, and constraints.

1. 💫Schema Validation: Verify data adherence to predefined schemas, checking types, formats, and structures.
2. 💫Integrity Constraints: Enforce rules and constraints to maintain data integrity, preventing inconsistencies.
3. 💫Cross-Field Validation: Validate relationships and dependencies between different fields to ensure logical coherence.
4. 💫Data Quality Metrics: Define and track quality metrics, such as completeness, accuracy, and consistency.
5. 💫Automated Validation Scripts: Develop and run automated scripts to check data against predefined rules and criteria.

 

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