Google云端平台数据显示 [英] Google Cloud Platform Data Display

问题描述

我目前在Google云端平台上运行的应用程序正在收集大量数据并将其存储在Google Cloud Datastore中，并且我正在使用Google Data Studio以图表形式显示此数据。但是，为了这样做，我必须将数据备份发送到Google Cloud Storage，然后将这些备份加载到Google BiqQuery中，然后将这些备份绘制到Google Data Studio上。由于我的应用程序正在实时收集实时数据，因此我不能在我的图上查看实时数据：只有我已经备份和加载的数据。我想知道是否有更好的方法来做到这一点，以便我可以直接在Google Data Studio中显示实时数据？我正在考虑的另一种方法是为我的应用程序编写一个前端页面，并在Python中手动执行此操作，如果可能，我宁愿避免此操作。

I currently have an app running on Google Cloud Platform that is collecting a decently large amount of data and storing it in Google Cloud Datastore, and I am displaying this data in plots using Google Data Studio. However, in order to do so, I have to send backups of the data to Google Cloud Storage, and then load these backups into Google BiqQuery, which can then be plotted on Google Data Studio. Since my app is collecting live data in real time, this is a less-than-ideal solution, as I cannot view the live data on my plots: only the data I have already backed up and loaded. I was wondering if there is a better way to do this so that I could show the live data directly in Google Data Studio? The alternative that I'm considering is writing a front-end page for my app and doing this all manually in Python, which I'd prefer to avoid if possible.

任何建议都非常感谢！

推荐答案

我会调查将数据从应用直接推送到BiqQuery中。从将数据流式传输到BigQuery ：

I'd investigate pushing data from the app directly into BiqQuery. From Streaming Data into BigQuery:

不使用作业将数据加载到BigQuery中，您可以选择
，通过使用
tabledata（）。insertAll（）方法。这种方法可以在不延迟运行加载作业的情况下查询
数据。本文讨论
在选择方法之前需要考虑的几个重要权衡，
包括流配额，数据可用性和数据一致性。

Instead of using a job to load data into BigQuery, you can choose to stream your data into BigQuery one record at a time by using the tabledata().insertAll() method. This approach enables querying data without the delay of running a load job. This document discusses several important trade-offs to consider before choosing an approach, including streaming quotas, data availability, and data consistency.

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