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Donate via BTC. Let's go straight into the query. Use the code below to replace IP addresses with the generic location.
Free IP Lookup Geolocation API
We published our first approach to de-identifying IP addresses four years ago— GeoIP geolocation with Google BigQuery— and it's time for an update that includes the best and latest BigQuery features, like using the latest SQL standards, dealing with nested data, and handling joins much faster. Replacing collected IP addresses with a coarse location is one method to help reduce risk—and BigQuery is ready to help. These are the top cities where users are making edits to Wikipedia, collected from to , followed by the query to use:. These new queries are compliant with the latest SQL standards, enabling a few new tricks that we'll review here.
The downloadable GeoLite2 tables are not based in ranges anymore.
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Using BigQuery, we parsed these into binary IP addresses with integer masks. We also did some pre-processing of the GeoLite2 tables, combining the networks and locations into a single table, and adding the parsed network columns, as shown here:.
Now they use proper IP networks, like in " That looked easy enough, but we need a few more steps to figure out the right mask and joins between the GeoLite2 table more than 3 million rows and a massive source of IP addresses. BigQuery here picks up only one of the masked IPs—the one where the masked IP and the network with that given mask matches.
This is how BigQuery can help you to replace IP addresses with coarse locations and also provide aggregations of individual rows. This is just one technique that can help you reduce the risk of handling your data.
You now have several options to explore and use datasets that let you comply with regulations. What interesting ways are you using de-identified data?