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Google BigQuery: The Definitive Guide to Understanding

It’s hard to exaggerate the number of new features that Google BigQuery has to offer.

The first time you start the service, you’ll see a list of amazing information about your queries. You can find out about the type of dataset you own, the size of the dataset, and how long it will last. data It has been saved.

You can also instruct Google BigQuery to provide a demo of your dataset immediately. It’s a little scary, but it’s also beneficial in a way. Now that you have a real idea of ​​the data in your dataset, you can think about how to organize your data and how it makes sense.

You can drill down to a more specific dataset by clicking the link in the upper right corner to get more information about the dataset.

Google BigQuery is a fast and very cost-effective way to store and query terabytes or more of data.As you can see in the screenshot, you can see that I have saved it in Google Cloud platform..

Google BigQuery also offers a unique approach for displaying large datasets in a new way called “query performance analysis”.

The acronym QPA isn’t that exciting, but don’t be fooled. This is a great tool to see how fast your queries execute data. When you’re running long queries against a large dataset, you can often see how fast the queries are running in Google BigQuery.

Google BigQuery also provides a way to visualize query latency and throughput. You can use the streaming portal to see the queries that are running, or click the snapshot button to let Google see the response time for each query.

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How do I get started with Google BigQuery?

To use BigQuery, you need a Google Cloud Platform account, email address, and unique private key. It is the one set so far. If you don’t have a GCP account yet, please register.

Then click the Getting Started button and follow the on-screen wizard.

To download big data dumps, Google offers a website where you can download the latest spreadsheets. Download this file and place it in a location that you can easily find.

Then open the Google BigQuery console.

Creating a dataset

The first thing you need to do is create a database and then connect to it.

You can create datasets while you are in the cloud. Start a BigQuery session, go to the data directory and create a new dataset. You can connect to the newly created dataset while in the cloud and wait for the BigQuery server to start. That is, the data is stored locally on the machine.

Get big data dump

After connecting to the BigQuery server, request a big data dump.

Focus on two features that may be useful in the future. First, you can customize your schedule. That is, you can schedule the database dump to be downloaded at a specific date and time. next,[BigQueryアーカイブをキャンセル]You can also select to cancel the dataset.

Let’s do it!

At the top[データの取得]Click on the tab[データの取得]Press the button.

The first option (getting data) allows you to download the entire BigQuery dataset (more on this below).

The second option (Get Data Package) contains a zip file containing the compressed dataset.Select it[OK]Just press.

In a few seconds, the zip file will be downloaded to your machine.

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How to view and load datasets

To load a dataset, select it from the list[ロード]Just press a button.

Then you will see a window containing the data in question. Here you can customize some options.

The first option allows you to download only the selected rows. Select Show only selected rows and press OK.

Select Load only selected rows and press OK.

Then download the filtered data (see below for details).

Select Filter Selected Rows and press OK.

The following option (Show only selected rows) allows you to download only the selected rows.Select it[OK]Press.

The last option (download only selected rows) is very useful for loading preprocessed data. In the same way,[OK]Press.

[選択した行のみをダウンロード]In the box[すべての行]Select an option,[OK]Press.

Use of preprocessed data

Next, you need to convert the compressed data to a type that BigQuery can understand. JSON.

BigQuery accepts many types of data, including JSON format.

[選択した行をダウンロード]From the box[Json]Select an option,[OK]Just press.

Simply click the red button in the lower left corner to connect to the remote server and run your code. You can then check the results by doing the following:

bigquery.github.com/advnat/index.html | JSON_ARRAY:'{“aggregation name”: “count”, “collection name”: “aggregator”, “aggregationType”: “json”}’

If it works, you’ll see something like this:

Google_BigQuery_2

If you’re not sure, you can check out the JSON sample files on the GitHub page.

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Closing note

Overall, my experience with BigQuery was relatively smooth and enjoyable. BigQuery is extremely easy to use and has great flexibility in downloading, sharing and processing large datasets.

This project was created as a starting point for a deeper understanding of BigQuery. If you have any questions, please leave a comment. We will do our best to accommodate you. Please let me know if you have any suggestions.

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