BigQuery

BigQuery is Google Cloud’s fully managed data warehouse. It enables massive interactive storage and analysis of large datasets. It is the central analytics hub of the offering. The main alternatives we use are:

• Amazon Redshift, the pioneer
• Snowflake
• Azure Synapse
• Databricks

BigQuery is a comprehensive and efficient solution, widely adopted, but its offering is also very similar to its main competitors.

Performance ranking depends on the conditions in which the benchmark was conducted, while the advantage, however, is on the side of BigQuery and Snowflake in terms of the number of features. The choice will depend on a set of criteria related to constraints and usage: it will not be limited to the data warehouse aspect but will encompass all infrastructure needs. BigQuery’s strength lies in its ease of use and flexibility. The default pricing model is on-demand: the compute bill amount depends on the volume of data scanned in the input tables of each query. The allocated power and cost adjust according to the queries.

This also presents the risk of an uncontrolled bill, but Google provides tools for setting up quotas, monitoring dashboards, and alerting. To maintain budget control and a good level of performance, these measures must be coupled with best practices and optimizations. Moreover, for better predictability, it is also possible to opt for capacity pricing, provided that the need is stabilized and a team can manage slot reservations. In terms of machine learning, it allows the creation, training, and execution of models, and BigQuery DataFrames provides a pandas-compatible API for analytics and a scikit-learn-like API.

On the BI side, the workspace provides direct access to Looker Studio visualizations. Finally, BigQuery, like its competitors, is constantly evolving and following market trends while becoming increasingly open, notably with BigLake tables that support Delta Lake, Iceberg, and Hudi formats. With the Omni feature, it is also possible to run queries on external sources such as Amazon S3 or Azure Blob Storage.

MDN’S POINT OF VIEW

A very good data warehouse technology, similar to Snowflake in its approach. For now, it is limited to the GCP ecosystem. It allows processing available data without index management on tables but offers more advanced configuration possibilities in certain cases. Billing is per query (in this case, budget monitoring is crucial) + table storage + ingestion.

THEODO’S POINT OF VIEW

We recommend BigQuery for its flexibility, its rich set of features, and its capabilities in BI and ML. It is suitable for handling both small and large data volumes and integrates perfectly with other Google Cloud services. It can be a strong argument for choosing this cloud provider.