Google BigQuery vs Google Cloud Storage: What are the differences?
Google BigQuery: Analyze terabytes of data in seconds. Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.; Google Cloud Storage: Durable and highly available object storage service. Google Cloud Storage allows world-wide storing and retrieval of any amount of data and at any time. It provides a simple programming interface which enables developers to take advantage of Google's own reliable and fast networking infrastructure to perform data operations in a secure and cost effective manner. If expansion needs arise, developers can benefit from the scalability provided by Google's infrastructure.
Google BigQuery belongs to "Big Data as a Service" category of the tech stack, while Google Cloud Storage can be primarily classified under "Cloud Storage".
Some of the features offered by Google BigQuery are:
- All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.
- Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.
- Affordable big data- The first Terabyte of data processed each month is free.
On the other hand, Google Cloud Storage provides the following key features:
- High Capacity and Scalability
- Strong Data Consistency
- Google Developers Console Projects
"High Performance" is the primary reason why developers consider Google BigQuery over the competitors, whereas "Scalable" was stated as the key factor in picking Google Cloud Storage.
According to the StackShare community, Google Cloud Storage has a broader approval, being mentioned in 183 company stacks & 79 developers stacks; compared to Google BigQuery, which is listed in 160 company stacks and 41 developer stacks.
What is Google BigQuery?
What is Google Cloud Storage?
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I use Google BigQuery because it makes is super easy to query and store data for analytics workloads. If you're using GCP, you're likely using BigQuery. However, running data viz tools directly connected to BigQuery will run pretty slow. They recently announced BI Engine which will hopefully compete well against big players like Snowflake when it comes to concurrency.
What's nice too is that it has SQL-based ML tools, and it has great GIS support!
Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.
Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!
Check Out My Architecture: CLICK ME
Check out the GitHub repo attached
BigQuery allows our team to pull reports quickly using a SQL-like queries against our large store of data about social sharing. We use the information throughout the company, to do everything from making internal product decisions based on usage patterns to sharing certain kinds of custom reports with our publishers.
Aggregation of user events and traits across a marketing website, SaaS web application, user account provisioning backend and Salesforce CRM. Enables full-funnel analysis of campaign ROI, customer acquisition, engagement and retention at both the user and target account level.
Amazon / Google... Google / Amazon ... we decided to take the plunge and go for Google Cloud services as their services seem to be a bit more thought through and structured as they have not developed so organically.
When creating proofs of concept or small personal projects that are hosted primarily in GCP, this is the object storage service I usually pair them with.
We use Google Cloud Storage to store the images and other files that are added (uploaded) or generated in the Flutter application.
All comments, votes, and other actions live here as a highly-scalable, reliable, multi-region storage solution.
Google's insanely fast, feature-rich, zero-maintenance column store. Used for real-time customer data queries.