Detect data anomalies

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This tutorial describes how to use Explore to detect anomalies in a retail transaction dataset.

Data exploration workbench, or Explore, allows data analysts to interactively query and explore large datasets in real-time. Explore helps you gain insights from your data, and lets you query data stored in Cloud Storage and BigQuery. Explore is a serverless Spark platform, so you don't need to manage and scale the underlying infrastructure.

Objectives

This tutorial shows you how to complete the following tasks:

• Use Explore's Spark SQL workbench to write and execute Spark SQL queries.
• Use a JupyterLab notebook to view the results.
• Schedule your notebook for recurring execution, allowing you to monitor your data for anomalies.

Costs

This tutorial uses the following billable components of Google Cloud:

• Dataplex
• Cloud Storage

To generate a cost estimate based on your projected usage, use the pricing calculator. New Google Cloud users might be eligible for a free trial.

When you finish this tutorial, you can avoid continued billing by deleting the resources you created. For more information, see Clean up.

Before you begin

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.
2. Install the Google Cloud CLI.

3. To initialize the gcloud CLI, run the following command:

   gcloud init

4. Create or select a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   • Create a Cloud project:

     gcloud projects create PROJECT_ID

   • Select the Cloud project that you created:

     gcloud config set project PROJECT_ID

5. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

6. Install the Google Cloud CLI.

7. To initialize the gcloud CLI, run the following command:

   gcloud init

8. Create or select a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   • Create a Cloud project:

     gcloud projects create PROJECT_ID

   • Select the Cloud project that you created:

     gcloud config set project PROJECT_ID

9. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

Note: Explore is available in the us-central1 and europe-west2 regions only. Make sure that all your data sources (Cloud Storage buckets and BigQuery tables) reside in the same region. Learn more.

Prepare the data for exploration

1. This tutorial uses a parquet file named retail_offline_sales_march. Download the file.

2. Create a Cloud Storage bucket. Name the bucket offlinesales_curated.

3. Upload the parquet file to your bucket: Upload the offlinesales_march_parquet you downloaded to the offlinesales_curated Cloud Storage bucket you created.

4. Create a Dataplex lake and name it operations.

5. In the operations lake, add a zone and name it procurement.

6. In the procurement zone, add an asset: Add the offlinesales_curated Cloud Storage bucket you created earlier as an asset.

Select the table to explore

1. In the Google Cloud console, go to the Dataplex Explore page.

   Go to Explore

2. In the Lake field, select the operations lake.

3. Click the operations lake that appears. Then navigate to the procurement zone and click on the table to explore its metadata.

   

4. In the Spark SQL editor pane, click Add. A Spark SQL script appears.

5. Optional: Open the script in split tab view to see the metadata and the new script side-by-side. Click in the new script tab and select Split tab to the right or Split tab to the left.

Explore the data

An environment provides serverless compute resources for your Spark SQL queries and notebooks to run within a lake. Before you write Spark SQL queries, create an environment in which to execute your queries.

Explore your data using the following SparkSQL queries. Enter the query into the New script pane.

Sample 10 rows of the table

1. Enter the following query:

   select * from procurement.offlinesales where orderid != 'orderid' limit 10;
   

2. Click Run.

Get the total number of transactions in the dataset

1. Enter the following query:

   select count(*) from procurement.offlinesales where orderid!='orderid';
   

2. Click Run.

Find the number of different product types in the dataset

1. Enter the following query:

   select count(distinct product) from procurement.offlinesales where orderid!='orderid';
   

2. Click Run.

Find the products that have a large transaction value

Get a sense of which products have a large transaction value by breaking down the sales by product type and average selling price.

1. Enter the following query:

   select product,avg(quantityordered * unitprice) as avg_sales_amount from procurement.offlinesales where orderid!='orderid' group by product order by avg_sales_amount desc;
   

2. Click Run.

Detect anomalies using coefficient of variation

You observe that a certain product, for example laptops, has a high average transaction amount. The following query shows how to detect laptop transactions that aren't anomalous in the dataset.

The following query uses the metric "coefficient of variation", rsd_value, to find transactions that are not unusual, where the spread of values is low compared to the average value. A lower coefficient of variation indicates fewer anomalies.

1. Enter the following query:

   WITH stats AS (
   SELECT product,
         AVG(quantityordered * unitprice)  AS avg_value,
         STDDEV(quantityordered * unitprice) / AVG(quantityordered * unitprice) AS rsd_value
   FROM procurement.offlinesales
   GROUP BY product)
   SELECT orderid, orderdate, product, (quantityordered * unitprice) as sales_amount,
       ABS(1 - (quantityordered * unitprice)/ avg_value) AS distance_from_avg
   FROM procurement.offlinesales INNER JOIN stats USING (product)
   WHERE rsd_value <= 0.2
   ORDER BY distance_from_avg DESC
   LIMIT 10
   

2. Click Run.

Visualize anomalies using a JupyterLab notebook

Build an ML model to detect and visualize anomalies at scale.

1. Create a notebook.

2. Open the notebook in a separate tab and wait for it to load. The session in which you executed the Spark SQL queries continues.

3. Import the necessary packages and connect to the BigQuery external table that contains the transactions data. Run the following code:

   from google.cloud import bigquery
   from google.api_core.client_options import ClientOptions
   import os
   import warnings
   warnings.filterwarnings('ignore')
   import pandas as pd
   
   project = os.environ['GOOGLE_CLOUD_PROJECT']
   options = ClientOptions(quota_project_id=project)
   client = bigquery.Client(client_options=options)
   client = bigquery.Client()
   
   #Load data into DataFrame
   
   sql = '''select * from procurement.offlinesales limit 100;'''
   df = client.query(sql).to_dataframe()
   

4. Run the isolation forest algorithm to discover the anomalies in the dataset:

   to_model_columns = df.columns[2:4]
   from sklearn.ensemble import IsolationForest
   clf=IsolationForest(n_estimators=100, max_samples='auto', contamination=float(.12), \
                           max_features=1.0, bootstrap=False, n_jobs=-1, random_state=42, verbose=0)
   clf.fit(df[to_model_columns])
   pred = clf.predict(df[to_model_columns])
   df['anomaly']=pred
   outliers=df.loc[df['anomaly']==-1]
   outlier_index=list(outliers.index)
   #print(outlier_index)
   #Find the number of anomalies and normal points here points classified -1 are anomalous
   print(df['anomaly'].value_counts())
   

5. Plot the predicted anomalies using a Matplotlib visualization

   import numpy as np
   from sklearn.decomposition import PCA
   pca = PCA(2)
   pca.fit(df[to_model_columns])
   res=pd.DataFrame(pca.transform(df[to_model_columns]))
   Z = np.array(res)
   plt.title("IsolationForest")
   plt.contourf( Z, cmap=plt.cm.Blues_r)
   b1 = plt.scatter(res[0], res[1], c='green',
                   s=20,label="normal points")
   b1 =plt.scatter(res.iloc[outlier_index,0],res.iloc[outlier_index,1], c='green',s=20,  edgecolor="red",label="predicted outliers")
   plt.legend(loc="upper right")
   plt.show()
   

Schedule the notebook

Explore lets you schedule a notebook to run periodically. Follow the steps to schedule the Jupyter Notebook you created.

Dataplex creates a scheduling task to run your notebook periodically. To monitor the task progress, click View schedules.

Share or export the notebook

Explore lets you share a notebook with others in your organization using IAM roles.

Review the roles. Grant or revoke Viewer, Editor, and Admin roles to users for this notebook. After you share a notebook, users with Viewer or Editor roles at the lake level can navigate to the lake and work on the shared notebook.

To share or export a notebook, see Share a notebook or Export a notebook.

Clean up

To avoid incurring charges to your Google Cloud account for the resources used in this tutorial, either delete the project that contains the resources, or keep the project and delete the individual resources.

Delete the project

Caution: Deleting a project has the following effects:

• Everything in the project is deleted. If you used an existing project for this tutorial, when you delete it, you also delete any other work you've done in the project.
• Custom project IDs are lost. When you created this project, you might have created a custom project ID that you want to use in the future. To preserve the URLs that use the project ID, such as an appspot.com URL, delete selected resources inside the project instead of deleting the whole project.

If you plan to explore multiple tutorials and quickstarts, reusing projects can help you avoid exceeding project quota limits.

Delete a Cloud project:

gcloud projects delete PROJECT_ID

Delete individual resources

1. Delete the bucket:

   gsutil rb BUCKET_NAME

   Important: Your bucket must be empty before you can delete it.
2. Delete the instance:

   gcloud compute instances delete INSTANCE_NAME

What's next

• Learn more about Dataplex Explore.
• Schedule scripts and notebooks.