Why did we compress the file? The raw, uncompressed file is about 136 MB, whereas the gzipped file is only 18 MB.
#Bigquery json extract example how to
So even though the first part of this chapter covers how to do a one-time load, carefully consider whether you would be better off planning on periodically updating the data and allowing users of the data to know about the version of the data that they are querying. If the state boundary data is to be used by a land title firm that needs to track ownership of land parcels, or if an audit firm needs to validate the state tax paid on shipments made in different years, it is important that there be a way to query the state boundaries as they existed in years past. Ignoring the impact of time on the correctness of the data might not always be possible. The fact that queries could potentially return slightly different results after an update compared to what was returned before the update is ignored.
So when a change does happen, such as through a treaty between states or due to a change in the path of a river channel, the owners of the dataset might decide to replace the table with more up-to-date data. For example, a retail firm might care only about which state a home is in currently to ensure that the correct tax rate is applied to purchases from that home. Analysts query the single table and ignore the fact that the data could change over time. State boundary data is, therefore, the type of data that is often loaded just once.
As of this writing, the last change of US state boundaries occurred on January 1, 2017, and affected 19 home owners and one gas station. In data warehousing lingo, we call this a slowly changing dimension. Data values such as the boundaries of US states change rarely, 1 and the changes are small enough that most applications can afford to ignore them.
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