Pub/Sub’s ingestion of data into BigQuery can be critical to making your latest business data immediately available for analysis. Until today, you had to create intermediate Dataflow jobs before your data could be ingested into BigQuery with the proper schema. While Dataflow pipelines (including ones built with Dataflow Templates) get the job done well, sometimes they can be more than what is needed for use cases that simply require raw data with no transformation to be exported to BigQuery.

BigQuery is Google Cloud’s fully managed serverless data platform that supports querying using ANSI SQL. BigQuery also has a data lake storage engine that unifies SQL queries with other open source processing frameworks such as Apache Spark, Tensorflow, and Dask. BigQuery storage provides an API layer for OSS engines to process data. This API enables mixing and matching programming in languages like Python with structured SQL in the same data platform.

It is not unusual for customers to load very large data sets into their enterprise data warehouse. Whether you are doing an initial data ingestion with hundreds of TB of data or incrementally loading from your systems of record, performance of bulk inserts is key to quicker insights from the data. The most common architecture for batch data loads uses Google Cloud Storage(Object storage) as the staging area for all bulk loads.

Performance considerations for loading data into BigQuery for various file types.