“Data Warehousing Market revenue to cross $30billion2025″– Global Market Insights, Inc.
Data is ubiquitous in the digital world. Knowing how to orchestrate and derive insights from business datasets is paramount to staying at the competitive edge.
To orchestrate and manage all these datasets, businesses must have a cloud data warehouse platform that accommodates huge varieties, velocity & volume of data!
Among the top performers of cloud data platforms, Snowflake & BigQuery are inevitable. These Data-as-a-service (DaaS) providers have many features in common regarding cloud-native benefits. Exploring the significant differences around technical aspects such as architecture, pricing, elasticity, usability, performance, data sharing, and other workloads helps you identify the best-suited cloud data warehouse for your needs!
In this blog post, let’s discuss the significant differences between Snowflake & BigQuery.
Forrester’s research predicts that 70% of employees are expected to work heavily with data by 2025. So, it’s paramount to consider factors such as on-demand elasticity, intuitive web UI, near-zero maintenance, native connectors for tools of your choice, and much more while choosing the cloud data warehouse for your business.
Here’s a quick sneak-peek of the significant differences between Snowflake & BigQuery!
If you’re a decision-maker & not a tech geek, with these key differences you can weigh the pros & cons of Snowflake & BigQuery to find your cloud data warehouse destination!
Still craving for in-depth technical aspects to gauge Snowflake & BigQuery?
Check out our next blog on Snowflake VS BigQuery – Who’s the winner of data warehousing race?
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