![]() ![]() The ingested data is then queried for creating reports or combined with other sources including real-time.īigQuery batch load jobs are free. They are typically ingested at specific regular frequencies, and all the data arrives at once or not at all. Batch Ingestionīatch ingestion involves loading large, bounded, data sets that don’t have to be processed in real-time. We will have dedicated blog posts in future for other ingestion mechanisms. In this post, we will dig into batch ingestion and introduce other methods at a high level. ![]() Loading Data into BigQuery (click here to see the image for better resolution) Here is a quick map with options to get your data into BigQuery (not an exhaustive list). At a high level following are the ways you can ingest data into BigQuery: There are multiple ways to load data into BigQuery depending on data sources, data formats, load methods and use cases such as batch, streaming or data transfer. The performance of a federated query depends on the performance of the external storage engine that actually holds the data. If query speed is a priority, then load the data into BigQuery. One key difference is that performance of querying external data sources may not be equivalent to querying data in a native BigQuery table. You can query across Google services such as Google Sheets, Google Drive, Google Cloud Storage, Cloud SQL or Cloud BigTable without having to import the data into BigQuery. Query without Loading (External Tables): Using a federated query is one of the options to query external data sources directly without loading into BigQuery storage. BigQuery native storage is fully managed by Google-this includes replication, backups, scaling out size, and much more. Let’s dive into it!īefore we start, let’s look at the difference between loading data into BigQuery and querying directly from an external data source without loading into BigQuery.ĭirect Import (Managed Tables): BigQuery can ingest datasets from a variety of different formats directly into its native storage. In this post, we will see how to load or ingest data into BigQuery and analyze them. So far we have only queried or used datasets that already existed within BigQuery. We looked into BigQuery’s storage management, partitioning and clustering tables to improve query performance and optimize cost. Previously in the BigQuery Explained series, we have reviewed how the decoupled storage and compute architecture helps BigQuery to scale seamlessly. ![]()
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