BigQuery now has 10 GB of free storage, and 1 TB of free monthly queries (try it today)
By Felipe Hoffa, Developer Advocate
If you have data to analyze, you have to try Google BigQuery. Where else can you analyze terabytes of data in seconds, without thinking about servers, indexing, distribution strategies or maintenance headaches? And today we’re making the getting started experience even easier, by adding a free storage tier — so you can try BigQuery with your own data at no cost.
Step 1: Run queries now
To experience BigQuery’s power in the next 5 minutes, just create an account and start running queries on any of the public datasets already available. Every month you’ll get a free terabyte to keep running any queries you’d like.
Step 2: Play with your own data
With the free storage tier we’re announcing here, you’ll be able to load your own data at no cost — up to 10GB of it. Loading data into BigQuery is simple — just point it to any CSV, JSON or Apache Avro files you have, and BigQuery can detect the schema automatically. You can even start streaming data into it (though streaming has no free tier for now). You’ll notice how quick the process is, and that no extra steps or planning are needed to start querying it. Even better: Your data will be more securely stored, encrypted at rest and highly available with no further configuration.
Step 3: Discover all the ways in which BigQuery will rock your data analysis world
BigQuery is open for everyone. You can load data, analyze and visualize it all on your own. You can follow blog posts like this one for fun analyses to get started. Additionally, you can rely on a multitude of partners than can help you do even more — whether visualizing data, analyzing it or bringing it in from multiple sources. You can also count on the community — from asking technical questions on Stack Overflow to sharing all kinds of BigQuery news on reddit.com/r/bigquery.
So who’s this for?
With the free storage tier we’re making BigQuery easier to try for a multitude of groups. For example:
- Data science students and teachers: Load your own big data and start learning how to analyze it.
- Firebase developers: Firebase can stream your data straight into BigQuery — you’ll be ready to analyze your users as soon as they act. Check out ”What’s new in Firebase (2017 )” to learn more and see what Firebase announced at Google I/O. (Note: Streaming into BigQuery is not free. Try batch loads for free ingest).
- Researchers: If you have data to share, you can do so — and your audience can instantly start querying it. BigQuery has strong Identity and Access Management (IAM) security provisions, including the ability to set a dataset as public if you want.
- Your proof of concept at work: Maybe your team already has a big data solution you can use your own data to test a workload on BigQuery. We want to make it easy for you to show your colleagues how much BigQuery can do for your company.
- Visualization: Tools like Google Data Studio can quickly connect to BigQuery, and allow you to create dashboards and visualizations with ease.
Finally, a note about free trials and free tiers. Google has two different ways for users to get access to our products for testing:
- Free trial: Google offers new users a $300 credit that expires in 12 months to use as a free trial. This trial is available to any and all Google Cloud Platform products. If you want to test a whole bunch of products on GCP, this is what you want to do.
- Free tiers are product-specific offerings of free usage for new or existing customers, and they never expire. BigQuery has two free tiers: one for storage (10GB) and one for analysis 1TB/month. So, if you keep your usage under those limits, you’ll never get charged. To prevent exceeding these limits, it is wise to monitor usage with Google Stackdriver charts and alerts; furthermore, users can put spending limits on their accounts.
So come join us — you might quickly discover why users love BigQuery so much. And if you want to tell us more, we’ll be ready to interact through Google Cloud Support and on Stack Overflow, reddit and twitter.
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Originally published at cloud.google.com.