![]() ![]() Many developers are accustomed to working with relational databases and If loading speed is important to your app and you have a lot of bandwidth to load your data, leave files uncompressed. In general, if bandwidth is limited, gzip compress files before uploading It's important to weigh these tradeoffs depending on your use case. For example, uncompressed files that live on third-party servicesĬan consume considerable bandwidth and time if uploaded to Google Cloud Storageįor loading. ![]() ![]() In size, using them can lead to bandwidth limitations and higher Google Cloud If you plan to load ISO-8859-1 encoded flat data, specify theīigQuery can load uncompressed files significantly faster than compressedįiles due to parallel load operations, but because uncompressed files are larger For detailed information about each data type, seeīigQuery supports UTF-8 encoding for both nested/repeated and flat data, and supports ISO-8859-1 encoding for flat data. Your data can include strings, integers, floats, booleans, nested/repeated records and timestamps. The following example shows sample nested/repeated data: One JSON object, including any nested/repeated fields, must appear on each line. Object to change how BigQuery parses CSV data. You can specify additional properties in the When loading data into BigQuery, specify the data format using the Or, your data might come from a source that only exports in CSV format. If your data contains embedded newlines, BigQuery can load the data much faster in JSON format.įor example, your data might come from a document store database that natively stores data in JSON format. Nested/repeated data can be useful for expressing hierarchical data, and reduces duplication when JSON also supports data with nested/repeated fields. This topic describes the data types and formats that BigQuery expects.Ĭhoose CSV or JSON based upon the following factors:ĬSV and JSON both support flat data. For example, you might need to export your data into a different format, or Depending on your data's structure, you might need to prepare the data before ![]()
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