Reasons to move to Column Based System - Google BigQuery!
July 03, 2021

Reasons to move to Column Based System - Google BigQuery!

Anomita Chandra | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Google BigQuery

Google Big Query is used by product and services department at my organization. It is used to maintain the various services like bookstore, market place, recreation etc. It is used to maintain the information about inventory, the various vendors and product details. Since it is serverless and can handle large datasets it gives us quick results , work with real time data and helps to handle transactional data.
  • Google BigQuery is column based, therefore it has high speed and easily accessible.
  • As I work with inventory related data, it gives me real time updates which helps to resolve many blocks which could cause problems if delayed.
  • Being serverless, it is easy to handle large size data.
  • Google BigQuery charge according to the quality of the code. So if it is long and lengthy and not the most efficient it can be costly.
  • The UI/UX is little difficult to use at the beginning on a small screen because of the layout.
  • Serverless - My organization works on realtime data, therefore it helps to stay updated.
  • Columnbased - Since the data is columnbased, it doesn't take time to load the data.
  • Positive - My organization has faced great profits and has trubleshooted many issues before hand because of the realtime data to work on. It keeps the data up to date.
  • Negative - As there are various Google BigQuer proficiency people in the organization. Some very experienced and some novice. It has a negative impact because people who are new write longer and inefficient codes so the company is charged more.
At my previous organization we used server based SQL server. There were days when the server was down and we couldn't work or access the data. This caused multiple reports and processes which were fed from the server to fail. Google BigQuery doesn't have such problems.

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Google BigQuery is suitable for scenarios where the dataset is large and needs to be analyzed based on real time data. Google BigQuer has been very useful when I was working on the inventory data for the bookstore. At the beginning of semester there was always high demand for school materials, this high demand caused a steady decline in the inventory. Getting updated real time helped us to restock the warehouse beforehand with products in higher demand and thereby led to higher sales.

Google BigQuery Feature Ratings

Automatic software patching
Database scalability
Automated backups
Database security provisions
Monitoring and metrics
Automatic host deployment