Will They Blend? Google BigQuery Meets Databricks

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Click to learn more about author Emilio Silvestri.

In the “Will They Blend?” blog series, we experiment with the most interesting blends of data and tools.

Whether it’s mixing traditional sources with modern data lakes, open-source DevOps on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT sensor data with idle chatting, we’re curious to find out: Will they blend? Want to find out what happens when IBM Watson meets Google News, Hadoop Hive meets Excel, R meets Python, or MS Word meets MongoDB?

Read the previous blog in the series here.

The Challenge

“Life is like riding a bicycle. To keep your balance you must keep moving.”Despite its misuse under tons of Instagram pictures, this beautiful quote from Albert Einstein is still relevant today. In addition to physical activity, sustainable and shared mobility have become our weapon against daily traffic and pollution: terms like shared transportbike sharing, and car sharing are now part of our language, and more people than ever use these services on a daily basis. How often are these services used? How is their usage affected by other factors, such as the quality of the service or the weather conditions?

To answer these questions we need to collect data from a wide range of (typically disjointed) data sources, a bit of imagination … and some patience! As an example, today we will mix together bike sharing data provided by Google BigQuery with weather data stored on Databricks, in order to see if and how weather conditions affect how the bikes are used.

For those who don’t know these two platforms, BigQuery is the Google response to the big data challenge. It is part of the Google Cloud Console and offers the ability to store and query large datasets using SQL-like syntax. Databricks is a cloud-based big data tool. Developed by the Apache Spark group, it offers a wide variety of operations – such as building data pipelines and scaling data science to production – tuning the functionalities offered by the Spark open-source software.

Both these platforms are supported by the Analytics Platform, from version 4.1 upwards. You can download and install our BigQuery and Databricks Integration from the our Hub.

Topic: Multivariate visualization of bike-sharing data vs. weather data

Challenge: Investigate how weather influences usage of bike sharing

Access mode:  Google BigQuery Integration and Databricks Integration

The Experiment

The first dataset, hosted on Google Big Query public data, is the Austin Bike Share Trips. It contains more than 600k bike trips during 2013-2019. For every ride it reports the timestamp, the duration, the station of departure and arrival, plus information about the subscriber. The second, smaller, dataset is the Austin Weather dataset, which is hosted on a Databricks platform. It contains daily weather information for the city of Austin, such as temperature, dew point, humidity, wind, and precipitation, as well as adverse weather events.

Google BigQuery

In the upper part of our workflow (which you can download from the Hub here) we access the Austin Bike Share Trips dataset hosted on the Google BigQuery platform as a public dataset. In order to execute this part of the workflow you need:


With the project credentials we are going to configure the Google Authentication (API Key) node. You will be required to provide your service account email and the P12 authentication file. You can find both of these in your Google Cloud Platform Project (once it has been activated) under: APIs & Services -> Credentials. 

If you are starting from scratch with Google Cloud Platform, I recommend this step-by-step guide, which also shows you how to create a new project, generate new credentials, and install the driver on our Analytics Platform.

Connecting to Google BigQuery

After authentication, the Google BigQuery Connector node provides access to the BigQuery platform.

It uses the BigQuery JDBC Driver, the hostname (which is bigquery.cloud.google.com), and the database name, which, in this case, is your Project ID. You can find the Project ID on your project’s dashboard on Google Cloud Platform (Figure 1). 

Figure 1: You can find your Project ID in the Google Cloud Platform. Add this ID in the “Database name” field in the configuration window of the Google BigQuery Connector node. 


At this point, Google BigQuery has become your remote database and you can use all the DB nodes provided by the Analytics Platform in the DB -> Query folder in the Node Repository panel. DB Query nodes are useful to build and execute powerful queries on the data before they are imported into your workflow. This is particularly useful when, as in this case, we are only interested in downloading a portion of the data and not the entire – huge – dataset. 

Figure 2: The section of the workflow that performs custom queries on big data.

Let’s add a DB Table Selector node, and open the configuration window to write a custom query like the one in Figure 3. It will extract features such as year, month, and year fields, which we are using further on in the workflow. 

When typing SQL statements directly, make sure to use the specific quotation marks (“) required by BigQuery.

We can refine our SQL statement by using a few additional GUI-driven DB nodes. In particular, we added a DB Row Filter to extract only the days in [2013, 2017] year range and a DB GroupBy node to produce the trip count for each day.

Tip: If you feel nostalgic about SQL queries, you can open the result window of every DB node (right click on the node -> last entry) and navigate to the “DB Query” tab to check how the SQL statement looks like so far.

Finally, we append the DB Reader node to import the data locally into the workflow.

Figure 3: DB Table Selector node configuration window with a custom query.


The bottom part of the workflow handles the data stored on Databricks. What you need in this section is:

  • A running Databricks cluster storing the Austin Weather dataset. You can download the CSV file from Kaggle and upload it on a Databricks cluster.
  • Credentials and cluster ID in order to connect to the Databricks instance
  • The official JDBC driver provided by Databricks and installed on our Analytics Platform (recommended)
  • Our Databricks Integration and Extension for Apache Spark available on the Hub

Please note that despite the fact that Databricks is a paid service, this part of the experiment is implemented using the Databricks Community Edition, which is free and offers all the functionalities we need for our challenge.

The Analytics Platform provides an open-source Apache Hive driver that you can also use to connect to Databricks. However, we recommend using the official JDBC driver provided by Databricks.

Connecting to Databricks 

First of all, let’s connect to Databricks adding the Create Databricks Environment node to the workflow. In the configuration window we are asked to provide a number of parameters:

  1. Databricks URL
  2. Cluster ID 
  3. Workspace ID
  4. Authentication

If you don’t already have this information, go to the Databricks webpage of your project and select the Cluster tab from the menu on the left. Next, select the cluster you want to connect to. At this point, the webpage URL will look like the one in Figure 4.

Figure 4: Databricks URL page in the form <databricks-url>/?o=<workspace-ID>#/setting/clusters/<cluster-id>/configuration.

Copy and paste these settings into the configuration window of the Create Databricks Environment node. 

If you are using Databricks on AWS, the URL will not display the workspace ID and you can leave this field blank. If you are not running the Community Edition, you can choose to use the Token authentication method. Otherwise you need to provide the credentials.

Note: The Databricks Community Edition automatically terminates a cluster after two hours of inactivity. If you want to rerun this example in a later moment, you should create a new cluster and update the Cluster ID in the configuration window of the Create Databricks Environment node.

Figure 5: The configuration window of the Create Databricks Environment node.

Executing this node connects the Analytics Platform to the Databricks cluster where the data are stored. The node has three ports, each of them providing a different access to the data:

  • Red port: the JDBC connection to connect to KNIME database nodes
  • Blue port: the DBFS connection to connect to the remote file handling nodes as well as the Spark nodes
  • Gray port: Spark context to connect to all Spark nodes. Please check in the Advanced tab of the configuration window that the option “Create Spark context” is enabled in order to activate this port

In this example we are going to use some basic Spark operations to retrieve the data. Please refer to the KNIME on DataBricks guide, to explore further Databricks operations in the Analytics Platform, plus more detailed instructions on how to configure Databricks for the first time.

Figure 6: Section of the workflow for manipulating and importing data from Databricks using Spark nodes.

Since we have stored the Austin Weather dataset on the Databricks cluster as a CSV file, let’s add a CSV to Spark node to access it. Double-click the node to open the configuration window. Click “Browse” and select the austin_weather.csv from the cluster.

At this point we are ready to use some of the functionalities offered by the Spark nodes. You can find them all in the Node Repository panel under Tools & Services -> Apache Spark, after installing the Extension for Apache Spark.

Here, we want to extract information regarding the date field. We are going to split the date string into three separate columns: year, month and day. To do so we use the PySpark Script (1 to 1) node and write our simple script directly into the configuration window. After execution, the output port contains the processed Spark data. Finally, a Spark to Table node imports the results into our workflow. 

The Results

In these last steps we have extracted and prepared the data to be blended. 

Let’s add a Joiner node, select the previously extracted yearmonth, and day fields as the joining columns and let the Analytics Platform do its tricks. After execution, right-click the Joiner node, select Joined table from the menu and have a look at the data, which are now blended. 

Following the Joiner node in the workflow, the Visualization component builds a dashboard, which includes different charts, such as bar chart, histogram, sunburst chart, and the table view and the scatter plot shown in Figure 7. Each dot in the scatter plot encodes the data of one day. The color of the dot tells us about the average daily temperature: The colder days are blue, while the hotter are red. 

Figure 7: Scatter plot of the blended data. Each dot encodes the number of bike rides for a specific day. It is colored according to the average daily temperature – blue for lower and red for higher values. We can explore different feature combinations directly from the scatter plot interactive view.

The dashboard also offers some level of interactivity to dig into the exploration, such as an interactive slider to remove days according to the temperature level or a table showing only selected dots from the scatter plot. As shown in Figure 7, we can also change the configuration of the chart directly from the dashboard, choosing different feature combinations by clicking the icon in the upper right corner. For example, we can get information about the relation between bike rides and rain level, choosing the corresponding features – PrecipitationSumInches for the X axis and NumberOfTrips for the Y axis. From the resulting scatter plot we can see that during the days with higher ride numbers there was hardly any rain or no rain at all: The bad weather conditions might have led people to choose different means of transportation. 

Let’s now click again on the icon and select the Date column for the X axis. The scatter plot updates revealing a seasonal trend of the bike rides. We can explore the details of the data points with the higher number of bike rides by selecting them from the scatter plot and then inspecting them in the Table view. It seems as if the peaks we can see mostly take place during March and October – when biking is probably more pleasant than under the rain or with very high or low temperatures.

At this point, we might want to upload the blended data back to the platforms, for future uses. In order to do so, let’s add a DB Table Creator to the workflow. We can connect it either to the Google BigQuery Connector or to the DB connection (Red port) of the Create Databricks Environment node. 

Note that additional steps such as the creation of a new schema in your personal BigQuery project might be necessary. 

Configure the DB Table Creator node by selecting the desired schema and giving a name to the table. Next, append and configure a DB Loader node to upload the data to the new remote table. 

Note: When using BigQuery, remember to delete all the space characters from the column names. They would be automatically renamed during table creation and this will create conflict for the next step, since column names will no longer match.

Figure 8: DB Table Creator configured to create a new table named austin_bike in the default schema in Databricks.

Wrapping Up

In this example we have learned how to access and blend two popular cloud services – Google BigQuery and Databricks – using the extensions available in our Analytics Platform. Together with these datasets, we have explored Austin bikesharing and how its usage is intrinsically related to weather conditions. 

The full workflow for this experiment is shown in Figure 9 and is available for download from the Hub under Google BigQuery meets Databricks.

Figure 9: Final workflow blending data from BigQuery and Databricks. It can be downloaded from the Hub.
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