What's the difference between BigQuery and BigTable? [closed]

Answer 1

The difference is basically this:

BigQuery is a query Engine for datasets that don't change much, or change by appending. It's a great choice when your queries require a "table scan" or the need to look across the entire database. Think sums, averages, counts, groupings. BigQuery is what you use when you have collected a large amount of data, and need to ask questions about it.

BigTable is a database. It is designed to be the foundation for a large, scaleable application. Use BigTable when you are making any kind of app that needs to read and write data, and scale is a potential issue.

Answer 2

BigQuery and Cloud Bigtable are not the same. Bigtable is a Hadoop based NoSQL database whereas BigQuery is a SQL based datawarehouse. They have specific usage scenarios.

In very short and simple terms;

• If you don’t require support for ACID transactions or if your data is not highly structured, consider Cloud Bigtable.
• If you need interactive querying in an online analytical processing (OLAP) system, consider BigQuery.

Answer 3

This may help a bit in deciding between different data stores that Google cloud offers (Disclaimer! Copied from Google Cloud page)

If your requirement is a live database, BigTable is what you need (Not really an OLTP system though). If it is more of an analytics kind of purpose, then BigQuery is what you need!

Think of OLTP vs OLAP; Or if you are familiar with Cassandra vs Hadoop, BigTable roughly equates to Cassandra, BigQuery roughly equates to Hadoop (Agreed, it's not a fair comparison, but you get the idea)

https://cloud.google.com/images/storage-options/flowchart.svg

Note

Please keep in mind that Bigtable is not a relational database and it does not support SQL queries or JOINs, nor does it support multi-row transactions. Also, it is not a good solution for small amounts of data. If you want an RDBMS OLTP, you might need to look at cloudSQL (mysql/ postgres) or spanner.

Cost Perspective

https://stackoverflow.com/a/34845073/6785908. Quoting the relevant parts here.

The overall cost boils down to how often you will 'query' the data. If it's a backup and you don't replay events too often, it'll be dirt cheap. However, if you need to replay it daily once, you will start triggering the 5$/TB scanned very easily. We were surprised too how cheap inserts and storage were, but this is ofc because Google expects you to run expensive queries at some point in time on them. You'll have to design around a few things though. E.g. AFAIK streaming inserts have no guarantees of being written to the table and you have to poll frequently on tail of list to see if it was really written. Tailing can be done efficiently with time range table decorator, though (not paying for scanning whole dataset).

If you don't care about order, you can even list a table for free. No need to run a 'query' then.

Edit 1

Cloud spanner is relatively young, but is powerful and promising (And outrageously expensive too). At least, google marketing claims that it's features are best of both worlds (Traditional RDBMS and noSQL)