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Titlebook: BigQuery for Data Warehousing; Managed Data Analysi Mark Mucchetti Book 2020 Mark Mucchetti 2020 Big Query.Google Cloud Platform.GCP.Big Da

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樓主: incoherent
31#
發(fā)表于 2025-3-26 23:14:14 | 只看該作者
32#
發(fā)表于 2025-3-27 03:42:26 | 只看該作者
33#
發(fā)表于 2025-3-27 07:58:17 | 只看該作者
34#
發(fā)表于 2025-3-27 12:03:43 | 只看該作者
Querying the Warehouses. You probably feel pretty accomplished, and you know now what you have to do to make your data program a success. You also have a massive amount of data pouring into your system—and I’m sure you’re anxious to do something with it.
35#
發(fā)表于 2025-3-27 14:06:18 | 只看該作者
Cloud Loggingstems we’ve discussed, it is fully managed and supports event collection at scale. It’s also directly integrated with all of the other services we’ve engaged with up until now: BigQuery, Cloud Storage, Cloud Functions, and so forth. In this chapter, we’ll look at how to import and analyze Cloud Logging logs in BigQuery.
36#
發(fā)表于 2025-3-27 18:26:12 | 只看該作者
Adapting to Long-Term Changether by performing new activities or analyzing existing data. Organizations change constantly, with people joining and leaving continually. New stakeholders arrive and depart too. Each of them will make an impact on your data program with their agendas for the business. Handling these changes gracefully is all part of the job.
37#
發(fā)表于 2025-3-28 00:05:53 | 只看該作者
38#
發(fā)表于 2025-3-28 03:01:48 | 只看該作者
All My Datazation and any data collection practices you may already have in place, the purpose of a data warehouse is to centralize how users can access data accurately and reliably. Prior to the data lake concept, it was also to put all that data in a central place. With BigQuery, that last step is not always
39#
發(fā)表于 2025-3-28 07:38:14 | 只看該作者
40#
發(fā)表于 2025-3-28 10:38:42 | 只看該作者
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