Cloud Data Quality

Cloud Data Quality – Create a simple, flexible, yet comprehensive data quality control solution for Google Cloud Dataprep using Cloud Functions, BigQuery and Data Studio channels.

Building a modern database to manage your analytics pipeline—such as Google Cloud and BigQuery databases or data lakes—has many advantages. One such benefit is the ability to directly monitor the quality of your data channel. You can ensure that the right data is fueling your analysis, monitor data quality trends, and, if a data quality problem arises, respond quickly to resolve it.

Cloud Data Quality

Cloud Data Quality

In this article, let’s assume that you are responsible for managing the data pipeline of the Sales Data Warehouse (DWH) and you want to monitor the data quality (DQ) of the Sales Data Warehouse (DWH). special. You should create two separate but connected channels:

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Here is an example of a Google Data Studio report that you will create. This blog explains in detail what they offer.

Here’s an overview of the entire solution used to capture Cloud Dataprep data quality statistics and load them into BigQuery. Part of the automation comes from the Dataprep function, while Data Studio handles the reporting.

Learn the data collection and data quality rules: For the data collection you want to view on DQ, you must enable the “Data Collection Results” and “Data Quality Rules” . Please familiarize yourself with these features. These are the basic statistics that we will use to build the DQ Dashboard. Every time you run a job, data quality statistics are generated and can be accessed via API or JSON files. We will use JSON files to build quality data. You must enable Data and Data Quality Rules for individual tasks and flows. If you don’t have a process to follow yet, don’t worry, we’ll use the DWH sales process below the article.

Download ‘flow_Profiling Quality Rules’ which is a DQ that generates results and quality data and put them in a BigQuery table Download ‘flow_Data’ which is a sample of DWH sales used in this blog this. If you want, you can use your Cloud Dataprep workflow to monitor data quality. Download ‘Advertising_Clickstream.csv’ and ‘import Sales_Data_small.csv’ which is the source of the DWH sales sample.

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You can create a copy of the Data Studio dashboard ‘[PUBLIC] Cloud Dataprep Profiling & Data Dashboard’ to customize your quality control needs.

You need a valid Google account and access to Cloud Dataprep and Google BigQuery to try it out. You can go to Google Console to activate these services.

API Calls: To make API calls, you need an Access Token, which you can configure from the Cloud Dataprep preferences page.

Cloud Data Quality

If you haven’t already, download the ‘flow_Profiling Quality Rules’ DQ flow and import (unzip) it into your Dataprep environment. In the Cloud Dataprep application, click the streaming icon in the justify nav bar. Then, on the streaming page, select Import from the context menu.

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Both flows parse JSON files into a BigQuery columnar table format for easy reporting in Data Studio. If you are interested, you can check the “Profile Rules” and “Profile Check” procedures to understand the logic.

You will need to bring an ID for this Walk. This will be used to remove the 2 DQ functions that you just fetched after the API call.

Now, you need to configure this flow by configuring and installing your own Dataprep. In the next section you will find the output of the Data Quality Regulation and those generated as part of your work (eg DWH sales). Next, add your DQ pipeline to import it into BigQuery.

When profiling is enabled and if you have data quality rules, Dataprep creates a JSON 3 file at the end of the job execution in the Google Cloud storage bucket in this default directory:

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In the current example, the is “dataprep-staging-0b9ad034-9473-4777-98f1-0f3e643d0dce”, and we used the default . In this demonstration we will only use the first two JSON files. You can later add additional statistics to the final data quality solution.

Note that the default used in this example can be changed for your environment. From the User Options menu in the justify nav bar you can check which is used for your account.

We can use the two application files (‘profilerRules.json’ and ‘profilerTypeCheckHistograms.json’) as the basis for creating the imported data for the DQ flow. These hikes are accessed via trails.

Cloud Data Quality

With Cloud Dataprep, when you create a new imported database, you can evaluate certain paths, allowing you to create an imported database that matches all of these files for all applications. . This collection of data is called a database.

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In the previously downloaded “Profiling & Quality Rules” step, you need to update the “profiler input datasets” with and .

And data quality rules are enabled so you can update existing routing and json profiler files.

First select the Dataset Details panel, select the Change parameter… item from the right nav menu “…”.

After the Browse button, you can find your deployment bucket and create 3 variables , table name and to get your Profiler json file as below. I recommend first to find an existing profilerRules.json and replace it with , database name and , with 3 variables.

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After updating your database 2, make sure that you can find some information in the preliminary database in the details section for each recipe.

If you can’t find data, make sure your path is correct and you can find the data in the database.

You can also change the configuration method 2 to check the data and see what went wrong.

Cloud Data Quality

We will not explain these recipes in detail here, but you can modify and change them according to your needs.

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Finally, you need to update the output from method 2 to include (in the form of an attachment) the Data Collection Rules and the data quality of the results of Table 2-BigQuery. 2 BigQuery tables will be automatically created by Dataprep the first time you run the job, and subsequent runs will put new data into these 2 tables. This way you will keep track of your DQ history.

Checkpoint: You’ve successfully downloaded the data quality flow, configured your needs with the correct file path, and run 2 DQ operations to populate the BigQuery table.

Finally, you can run the function to generate these 2 outputs, and see that the 2 BigQuery tables are populated with data quality rule results.

Well, we’re almost done—but not quite. Now we need to create the DWH Sales flow that can be seen here as an example, which contains the data collection that we will use to monitor the quality of our data. You can use one of your existing routes for your own Dataprep project. For the flow you choose to monitor data quality, we’ll call the Dataprep DQ Flows above to generate data quality rules for Google BigQuery tables.

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The DQ flow call is made thanks to the Webhook (external function call – if you are not familiar with webhooks, you can read the documentation here ) information that allows you to define the outgoing HTTP message for the REST() API.

Let’s see what it looks like. Here’s how to configure a Webhook event in your flow that calls your “Profiling & Rules Processing” flow and runs both tasks.

Where is the ID of the “Profiling & Rules Processing” you generated in the previous step.

Cloud Data Quality

Don’t forget to check the results on the print page of your product. If this is not checked, the results will not appear and the purpose of any solution will not be defeated.

Setting Up Data Quality Monitoring For Cloud Dataprep Pipelines

Troubleshooting: Successfully created a Webhook in the DWH Sales Flow that fires when the task completes, the webhook triggers the DQ flow to populate the BigQuery DQ table.

You can configure a Webhook for each of the direct products from DWH if you want to receive and monitor the Data Standards and quality of all your products.

Now you are ready to test the final step by running your Dataprep job and viewing the data quality data in your Data Studio report.

From the DWH sales flow, run a function (by clicking the Run function button) output “Advertising_Clickstream” for example.

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When the “Advertising_Clickstream” task is completed, you can see on the task results page, from the Webhooks tab of the task, that the Webhook was successful:

You can also check on the task results page, that the 2 tasks “Data Collection Rules” and “Profile View” have been started:

And after completing these 2 “Profiler Rules” and “Profiler Check” you can check

Cloud Data Quality

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