Google BigQuery: The Reliable Choice for Big Data Analysis
Updated August 14, 2024

Google BigQuery: The Reliable Choice for Big Data Analysis

Anonymous | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User

Overall Satisfaction with Google BigQuery

My company relies on Google BigQuery to manage and analyze our vast datasets effectively. As a DevOps engineer, I recommend my colleagues to choose Google BigQuery over other alternatives. Google BigQuery serves as our central data warehouse. It ingests, stores, and
optimizes large volumes of data, allowing us to perform complex queries
efficiently. Whether it’s historical sales data, customer behavior, or
inventory records, Google BigQuery handles it seamlessly.

Pros

  • Scalability and Speed: Google BigQuery handles large-scale data processing with ease
  • Serverless Architecture , so no infra management
  • Geospatial Analysis
  • Integration with Ecosystem as my company uses Google cloud platform
  • Cost-Effective Pricing

Cons

  • Queries that haven’t been optimised for speed or return redundant data can become expensive. So, cost estimation feature would be great!
  • Google BigQuery lacks robust built-in data visualisation tools. Integration with GCP is seamless, but third party integration would be beneficial for visual dashboards.
  • In my opinion, Google BigQuery schema changes can sometimes be cumbersome, especially for large tables. Simplifying the process of adding, removing, or modifying columns could improve data management workflows.
  • In my company, Market analysis team uses BQ , to keep all the data from different social media platforms, all in one place. They have this approach where they identify trends across platforms, understand audience demographics, and optimise ad targeting. This data-driven approach has led to a 25% increase in average conversion
  • Google BigQuery's user-friendly interface allows our team to extract valuable insights without needing extensive data science expertise. This translates to reduced reliance on costly data analysts, improving our internal efficiency.
  • With improved conversion rates, our clients are generating more revenue from their social media campaigns. This translates to increased customer lifetime value (CLTV)
Amazon Redshift was a likely alternative we were considering , but it needs to be provisioned on cluster and nodes, which increases infrastructure management, whereas Google BigQuery is serverless, so no infra management :) Also, I remember when comparing them we did found out that Google BigQuery had support for 10000 columns , against 1500-2000 columns per table on Redshift . Our decision was favourable towards Google BigQuery, as it also integrates seamlessly with out current GCP infrastructure.

Do you think Google BigQuery delivers good value for the price?

Yes

Are you happy with Google BigQuery's feature set?

Yes

Did Google BigQuery live up to sales and marketing promises?

Yes

Did implementation of Google BigQuery go as expected?

Yes

Would you buy Google BigQuery again?

Yes

1.Google BigQuery stores and analyses massive datasets in my organisation, making it ideal for me , as i can manage Terrabytes of data with it.2.Google BigQuery can ingest and analyze streaming data feeds in near real-time, so it helps to make data-driven decisions very fast. As for less appropriate scenarios:- 1. For very small datasets (in the megabyte range), traditional relational databases or spreadsheets might be more cost-effective and easier to manage. 2. Also, in scenarios where frequent schema changes is needed, it becomes cumbersome.

Google BigQuery Feature Ratings

Database-as-a-Service
8.7
Database scalability
9
Database security provisions
9
Monitoring and metrics
8

Comments

More Reviews of Google BigQuery

  1. Home
  2. Database-as-a-Service (DBaaS)
  3. Google BigQuery Reviews
  4. User Review of Google BigQuery