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BigQuery is a serverless big data warehouse available as a part of Google Cloud Platform. It's highly scalable, meaning that it can process tiny datasets as well as petabytes of data in seconds, using more cloud capacity as needed. (However, due to BigQuery's distributed architecture, you can't possibly expect it to have a sub-second query response time.). - Source: dev.to / 4 months ago
Alternatively, you can look into Google Cloud BigQuery (https://cloud.google.com/bigquery). There is a limit on how much data that can be processed per month with a free account although they do provide a $300 credit. They have a public GitHub repos data set here: https://console.cloud.google.com/marketplace/product/github/github-repos. This is an example on how the dataset can be used:... - Source: Reddit / 3 months ago
"Google Cloud’s databases and analytics products such as BigQuery, Dataflow, Pub/Sub and Firestore brought Theta Labs unlimited scale and performance, allowing them to: ...". - Source: Reddit / 3 months ago
Https://cloud.google.com/bigquery#section-8 - Store all programmatic log levels on Google Bigquery, Build and deploy a propensity to purchase a model for predicting customer purchasing behavior using BigQuery ML and AI Platform. - Source: Reddit / about 2 months ago
This is a collection of most common bash scripts to automate Databricks. - Source: dev.to / 3 months ago
With the growing popularity of open source technology, venture capital (VC) investments in open source technology have increased. For instance, the company Databricks is the largest contributor to the open source Apache Spark project. Recently, Databricks received a $1 billion series G investment! - Source: dev.to / about 2 months ago
I recently stumbled on this talk from a few years ago by joel grus on why notebooks suck. there's been a lot of innovation in the space since (with products like databricks, deepnote, hex, etc.), but a lot of the fundamental flaws of notebooks still exist. - Source: Reddit / about 1 month ago
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