Understanding BigQuery Pricing

TL;DR

  • Queries in Google BigQuery are several times more expensive than the same queries in Amazon Athena.
  • We discuss other BigQuery cost, performance and ecosystem advantages that can offset these higher costs.

BigQuery is one of our favorite cloud big data warehouses. It has virtually limitless data storage capacity and delivers extraordinary performance on large data sets. That said, pricing can be a bit problematic if you’re not careful. We frequently hear that query costs for BigQuery and AWS Athena are the same. In this article, we will address that misconception and understand how to calculate prices for BigQuery storage and queries; we’ll also discuss ways to leverage BigQuery cost, performance, and ecosystem advantages to help offset these higher costs.

Unlike AWS Redshift, but similar to AWS Athena, BigQuery is serverless in the sense that you don’t need to reserve or spin up resources to run queries. Instead, you pay for data storage and for the amount of data scanned in each query you run. On the surface, Athena and BigQuery appear to be priced almost identically. As one comparison article puts it,

“Google also charges by the amount of data scanned, and the price is the same as for Athena. The storage is $0.02 per GB, which is more or less the same in AWS (the price tiers depends on the overall amount stored).” https://logz.io/blog/bigquery-vs-athena/

This discussion of prices ignores some key details. In practice, prices for queries and storage in Athena are several times cheaper than equivalent prices in BigQuery. Let’s dig into these pricing differences, call out other ancillary costs specific to Athena, and explain why we still generally recommend BigQuery to our clients.

Athena vs BigQuery – A Pricing Comparison

Both Athena and BigQuery bill at $5/TB queried. However, not all terabytes are created equal. Athena charges for bytes read from S3; compressing data to reduce its size thus saves costs for both queries and storage. The Athena pricing documentation (https://aws.amazon.com/athena/pricing/) mentions this strategy specifically: “Compressing your data allows Athena to scan less data.”

Behind the scenes, BigQuery also compresses data, but this is invisible to the user. Here’s the big difference with Athena – storage and queries are billed by decompressed bytes (https://cloud.google.com/bigquery/pricing#data), and the difference in calculated data size can be dramatic. For example, I recently encountered a column like this in some client data stored in BigQuery.

In fact, this entire column consists of a single repeated value. Typing the query

SELECT activitytype FROM `sample-table`

in the BigQuery console editor returns a query size of 1.3 GB. The table contains over 200 million rows, so the raw data size makes sense, but a quick experiment with gzip compression achieves a ratio of over 100 to 1.

Low cardinality columns, i.e., highly compressible columns containing a small number of distinct values repeated many times, are common in real world data sets. Noisy numeric data, also very common in practical data, doesn’t compress nearly so well, but we typically see compression ratios of 5 to 1 or better for full tables.

To look at another example, we turn to a Bureau of Labor Statistics table in the BigQuery public datasets. (bigquery-public-data:bls.employment_hours_earnings). The reported table size in BigQuery is 1.16 GB. Exporting from BigQuery in CSV format with .gz compression yields 141 MB of files. Using Qubole Spark to convert the table into Parquet columnar format with Snappy compression yields an output size of 222 MB. While csv.gz format gives us a higher compression ratio, Parquet/Snappy format yields substantially better query performance in Athena and allows us to further limit scan size by only paying for data in columns that we query. To put this example in pricing terms, querying the full table in BigQuery is roughly five times more expensive than using Athena to query the data in compressed Parquet format.

BigQuery Freebies

Query and storage pricing tell only part of the cost story for BigQuery and Athena. BigQuery gives away a number of services that entail extra charges with AWS/Athena.

  • Data import – BigQuery imports data from Google Cloud Storage to native tables for free. Athena can directly query data from S3 in a variety of formats, but it is a best practice to convert data to a compressed columnar format like Parquet with Snappy compression for data that will be queried repeatedly. Format conversion in AWS requires running a workload in Glue, Athena or another tool.
  • Data export – BigQuery exports native tables to compressed Avro, CSV and JSON files for free. (Note that BigQuery can read Parquet files, but inconveniently does not export directly to Parquet.)
  • Schema management – standard practice is to manage Athena schemas in Glue Catalog, using a Glue crawler scan for detection. BigQuery can auto-detect schemas for free as part of the ingest process.
  • Query caching – when you run a duplicate query in BigQuery within 24 hours, the database will return cached results at no additional charge.

Other BigQuery Features We Love.

Here are some reasons why we frequently recommend BigQuery to our clients.

  • Ease of management – BigQuery’s project, dataset and table hierarchy makes it exceptionally easy to organize data and manage access. Data management and administration overhead is a key cost driver for any large organization. Athena is an extremely powerful and flexible query engine, but data organization and management is substantially simpler with BigQuery.
  • Flat rate pricing – organizations querying more than 2 petabytes a month, or 20 petabytes a year, can save by signing up for BigQuery flat rate pricing, starting at $10,000 per month or $100,000 a year with an annual commitment.
  • Data sharing – we expect data sharing to grow in importance over the next decade. This can be in the form of open data sets, data shared for collaboration and paid data access. Cloud platforms offer many methods of data sharing, for example ‘requester pays’  S3 buckets and AWS Data Exchange. That being said, we’ve found data sharing in BigQuery to be exceptionally easy. Consumers simply run queries against shared data from their own Google Cloud projects, and can easily union or join with data from other sources. (This is also a key strength of Snowflake.)
  • Integration – BigQuery features deep integration into various Google Cloud Platform data services. We anticipate continued growth of the ecosystem in 2020.

Your Mileage May Vary

Innovation from cloud big data warehouse vendors is driving a sea change in business analytics and data science. Google BigQuery, Amazon Athena, Snowflake, Redshift and Azure Synapse Analytics all offer remarkable technology and performance. The choice of a cloud data warehouse is just one component of an organizational cloud strategy. Consider other factors like operational costs to manage data pipelines and other technologies that support the data warehouse.

About the Author

Matt Housley is one of the cofounders of Ternary Data, a consulting firm specializing in data engineering and processes. Before founding the company, he worked as a math professor, data scientist, and data engineer. He holds certifications on both GCP and AWS.