A data powerhouse to manage complex datasets in a jiffy
March 12, 2024
A data powerhouse to manage complex datasets in a jiffy
Score 9 out of 10
Vetted Review
Verified User
Overall Satisfaction with Google BigQuery
We deal with massive datasets – customer transactions, website logs, sensor data from our products – all running into terabytes. Google BigQuery acts as our central data warehouse and ingests data from various sources, like CRM systems, marketing tools and also from internal applications. It's not just the marketing team or data scientists who leverage it. Sales uses it for customer segmentation and churn analysis. The product team relies on it for user behaviour analysis and identifying feature adoption trends. The speed of Google BigQuery is mind-blowing. I can run complex SQL queries on massive datasets and get results almost instantly.
- Its serverless architecture and underlying Dremel technology are incredibly fast even on complex datasets. I can get answers to my questions almost instantly, without waiting hours for traditional data warehouses to churn through the data.
- Previously, our data was scattered across various databases and spreadsheets and getting a holistic view was pretty difficult. Google BigQuery acts as a central repository and consolidates everything in one place to join data sets and find hidden patterns.
- Running reports on our old systems used to take forever. Google BigQuery's crazy fast query speed lets us get insights from massive datasets in seconds.
- Google BigQuery's built-in visualization tools are limited compared to dedicated BI tools. Expanding the options and allowing for more customization would help explore and present data insights.
- Currently, it's hard to track where the data comes from and how it changes as it moves through the pipeline because it lacks data lineage capabilities. It's tough to ensure data quality assurance and regulatory compliance.
- The current access control options are somewhat limited. Granular control over specific datasets or tables within a project would help manage access in collaborative environments.
- Previously, running complex queries on our on-premise data warehouse could take hours. Google BigQuery processes the same queries in minutes. We estimate it saves our team at least 25% of their time.
- We can target our marketing campaigns very easily and understand our customer behaviour. It lets us personalize marketing campaigns and product recommendations and experience at least a 20% improvement in overall campaign performance.
- Now, we only pay for the resources we use. Saved $1 million annually on data infrastructure and data storage costs compared to our previous solution.
First and foremost, Google BigQuery's pricing structure, based on data processing and storage, is more cost-effective for our needs. Secondly, since we already use other Google Cloud services, its tight integration with them especially, with Cloud Storage and Dataflow was a big plus and streamlined data transfer and simplified our workflows. Apart from that, as my team deals with large datasets and complex queries, we need a serverless architecture technology that has an edge in terms of query speed and scalability for our specific needs.
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