Have you experimented working with very large databases with a huge amount of data and records?
If Yes, then definitely you have faced huge challenges concerning can automatically scale out to thousands of CPUs across petabytes of data.
The Bigquery architecture is developed in such a way that it can be scale independently based on the demand, the computation is Dremel (query system for analysis) storage is Colossus(successor to the Google File System GFS) means Bigquery leverages the columnar storage format and then optimized and stored in Colossus. and for the migration of data from one place to another Google’s Jupiter network is used. This way we get query results in good retrieval speed.
(source: https://cloud.google.com/blog/products/data-analytics/new-blog-series-bigquery-explained-overview)
How to use Bigquery?
To get started in Bigquery you need to know about these 4 steps
- Ingestion
- Storage
- Processing
- Results and visualizations
Ingestion: we can load data from cloud storage, it supports Avro, CSV, JSON formats. Proper data ingestion format and schema are needed for the successful migration of data.
Store: Google cloud storage buckets can be used as storage for your database.
Processing: Bigquery is a REST-based web service that allows you to run analytical queries using Google client so that you can use with your application programming
Explore and visualize: The Bigquery results can be connected with multiple cloud tools like Google data studio, Google sheets for further analysis and visualizations.
Google Cloud Storage is one of the easy ways to ingest data into Bigquery and also helps for storing the data, Google client used for query processing. and the results can be connected across multiple google services and helps in visualizations.
You have to create a dataset in your GCP project and then all tables can be ingested from cloud storage, after that you can query your tables either from interactive UI or Google client. since Bigquery is a REST-based web service that allows you to run complex queries under a large set of data.
Conclusion
Bigquery is solving huge problems and helps in performance improvements but this doesn’t mean it is the best database solution in the world. Because it has its own limitations like limited number of updates in the table per day, limitations on data size per request, and more. Suppose if you have a small database and you just need to perform simple CRUD operations Bigquery is not useful on the other hand if you have a huge dataset and unable to handle and process it, Bigquery may be helpful for your performance optimizations and improvements.
