Google BigQuery – a fully managed Cloud Data Warehouse for analytics from Google Cloud Platform (GCP), is one of the most popular Cloud-based analytics solutions. Due to its unique architecture and seamless integration with other services from GCP, there are certain best practices to be considered while configuring Google BigQuery ETL (Extract, Transform, Load) & migrating data to BigQuery. By Faisal K K.
This article will give you a birds-eye view of Google BigQuery, its key features, and how it can enhance the ETL Process in a seamless manner, including:
- What is Google BigQuery?
- Key Features of Google BigQuery
- What is ETL?
- Best Practices to Perform Google BigQuery ETL
- GCS as a Staging Area for BigQuery Upload
- Handling Nested and Repeated Data
- Data Compression Best Practices
- Time Series Data and Table Partitioning
- Streaming Insert
- Bulk Updates
- Transforming Data after Load (ELT)
- Federated Tables for Adhoc Analysis
- Access Control and Data Encryption
- Character Encoding
- Backup and Restore
However, performing these operations manually time and again can be very taxing and is not feasible. You will need to implement them manually, which will consume your time & resources, and writing custom scripts can be error-prone. Moreover, you need full working knowledge of the backend tools to successfully implement the in-house Data transfer mechanism. Nice one!
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