Google's BigQuery is a serverless, scalable, and cheap multi-cloud data warehouse created for enterprise agility. It also has some basic integrations with Google's ecosystem of applications.
Of course, you can also integrate with your data warehouse or statistical tool to import your data and then work with it, but that will cost you even more time.
PaaS companies such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) have their SQL databases optimized for specific workloads. Still, BigQuery is a top pick for high-volume analytical data storage and querying.
BigQuery's low cost makes it ideal for government, finance, and industry. It has robust tooling, including an industry-specific UI layer, that simplifies data ingestion and analysis.
Pricing starts at around $0.02 per hour. You can use your existing data, but if you have a large volume of it, you might prefer to integrate with a BigQuery data warehouse to avoid storing your data locally.
What Can You Do With BigQuery?
First and foremost, BigQuery is great for batch processing of many data. You can dump a table into a local or Google Cloud Storage (GCS) bucket and use BigQuery to take it through several aggregations and query steps, which can be executed in parallel and then transformed back into the original data.
For example, you could run a query that will count the total number of clicks from an article about an NBA star's 2017-18 stats. While this doesn't sound all that exciting, if you want to run 50,000 queries this way, you can see your results in about an hour.
That might seem like a significant time investment for such a small data set, but bear in mind that running a massive batch query takes only a few minutes.
Finally, if you need to analyze 10TB of data in under 10 minutes, BigQuery might be a good fit for your significant data needs.
And that's just a tiny slice of what you can do with BigQuery. BigQuery was developed by Google's engineers specifically for querying and processing big data. And if you look at the documentation, you can see that Google has put considerable effort into providing out-of-the-box capabilities for most of the queries people commonly use.
The main problem with BigQuery is its closed source, which prevents me from testing its performance. However, I imagine that it's pretty good. If it performs like it says, BigQuery will be one of the most potent extensive data warehouse systems on the planet.
Pricing, Performance, and Versatility
When Google first announced BigQuery in 2014, the query language was quite limited. But it's been evolving rapidly, and it now includes many of the powerful features that Google users expect from a cloud-native data warehouse.
BigQuery offers some tools available only in a handful of other clouds. For example, it can import structured and unstructured data (like documents or JSON), whereas most PaaS services can only import structured data (like SQL files).
And if that wasn't enough, you can use BigQuery to create an ad hoc integration with services from Google's ecosystem of applications. You can automatically import data from Google Docs, AdWords, Sheets, and Huddle with a single SQL query.
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Data warehouses have always been challenging for single users or small businesses to build and maintain. BigQuery is Google's answer to all that. It's fast and has low entry costs for individuals and small companies that need to analyze large amounts of data quickly.
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