Google BigQuery vs Census: What are the differences?
Developers describe Google BigQuery as "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.. On the other hand, Census is detailed as "Sync your warehouse data to any app". It syncs your data warehouse with CRM & go-to-market tools. Get your customer success, sales & marketing teams on the same page by sharing the same customer data.
Google BigQuery and Census can be categorized as "Big Data as a Service" tools.
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, Census provides the following key features:
- Turn your warehouse into a Customer Data Platform
- Sync with customer facing tools
- No more data outages
What is Census?
What is Google BigQuery?
Need advice about which tool to choose?Ask the StackShare community!
Why do developers choose Census?
Sign up to add, upvote and see more prosMake informed product decisions
What are the cons of using Census?
What companies use Census?
Sign up to get full access to all the companiesMake informed product decisions
Sign up to get full access to all the tool integrationsMake informed product decisions
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!
Currently, we need to ingest the data from Amazon S3 to DB either Amazon Athena or Amazon Redshift. But the problem with the data is, it is in .PSV (pipe separated values) format and the size is also above 200 GB. The query performance of the timeout in Athena/Redshift is not up to the mark, too slow while compared to Google BigQuery. How would I optimize the performance and query result time? Can anyone please help me out?
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.