Superset BigQuery Connection: 11 Easy Steps

BigQuery is a leading provider of Data Warehousing storage services from the stable of Google. It is known to be robust and can store petabytes of data. It offers many features and is highly functional in various use cases. It provides tools for storing data from various sources and then efficiently retrieving them. By connecting Superset BigQuery you can visualize the retrieved data as well.

Apache Superset is an open-source data visualization platform that supports the processing of petabyte-scale data. It is flexible and efficient with larger amounts of data when compared to its competitors. 

This article gives a comprehensive guide on connecting Apache Superset Bigquery together.

What is Superset?

Apache Superset is a BI (Business Intelligence) tool that is very capable of processing large amounts of data and visualizing it efficiently using graphs and charts. It also provides the provision for a web application that can be used to generate reports on the go and aid business strategy.

Major companies like Udemy, Airbnb, Lyft, etc. use Apache Superset. The tool being an open-source solution provides flexibility as it has large community support and it is easily accessible by developers. It also provides the selection of various parameters such as Webserver, Metadata Database engine, Caching layer, and many more for BI tool optimization.

Apache Superset is a cloud-based system that is compatible with Nginx, Gunicorn, and Apache which are all examples of Web servers. It is also compatible with MySQL, MariaDB, and PostgreSQL which are examples of Metadata Database Engines.

Redis and Memcached are examples of Caching Layers. The results Backend category has options like Memcached, S3, and Redis while Message Queue has options like SQS, RabbitMQ, and many more.

The Features of Apache Superset

• Effective and Efficient Performance: It is very fast but at the same time accurate when processing large amounts of data. It utilizes a no-code environment that is simple to use and can also be used with SQL IDE for data exploration. It can generate visualizations from simple pie charts to complex geospatial charts.
• User-friendly Interface: It provides complex functionality with ease of use. It provides a simple user interface and requires very few prerequisites. This brings a positive user experience to the table. 
• Excellent Visualization System: The tool provides wide options for high-quality visualization making it highly creative and flexible. This makes the data exploration very informative and interesting.
• Scalability: Apache Superset is highly scalable since it allows data of different sizes from various sources optimally. 
• Wide Range of Database Support: Apache Superset is compatible with many databases like Amazon Redshift, Google BigQuery, Snowflake, Firebird, Oracle Database, and many more. Also being an open-source platform provides it with a lot of support for integrations with other platforms.  

What is BigQuery?

BigQuery is a flexible, fast, and robust data warehouse platform from the stable of Google. This makes it readily available to be connected with other tools that the Google Platform provides. It follows a serverless model making it cost-effective which allows for user-specific pricing based on usage.

It also leverages its integrated query engine for performing analytics which when combined with its serverless model can process terabytes of data in seconds.

The study shows that BigQuery is about 26%-35% cheaper than its rivals under similar workloads making the total cost of ownership lower.

Since it follows a serverless approach, there is no requirement to manage and configure complex infrastructure providing a lot more flexibility to gain insights using standard SQL. It also provides flexible pricing ranging from flat-rate options to on-demand ones.

Google’s BigQuery column-based structure allows for handling large amounts of data without compromising on the speed of processing that is associated with the handling of data.

The column-based storage is highly capable of processing relevant columns resulting in faster response times and better use of available resources. Columnar storage is also beneficial for analytical tools to generate insights.

Key Features of Google BigQuery

Here are a few key features of Google BigQuery:

• Serverless Services: When setting up a data warehouse, an organization needs to specify the server hardware, the technical requirements, the calculation speed required, ensure proper administration to maintain the data warehouse along with proper reliability security performance, and many more factors. These all can be avoided in Google BigQuery since the serverless approach distributes the task to a different machine and run them parallelly. Also being a cloud solution allows the user to focus efforts on server provisioning rather than infrastructure providing more time to generate and look into the insights. 
• SQL and programming language support: Standard SQL is supported by BigQuery. It also has the provision for libraries that can be used to create applications using Python, C#, and Java. PHP and many more.
• Tree Architecture: Google BigQuery follows a tree architecture, that can be extended to thousands of computers and creates an execution tree that can structure calculations. The incoming requests are processed by the root server and forwarded to the mixer which is nothing but the branches of the structure. These mixers then modify the requests to the slot which is the leaf node of the structure. Slots work in parallel and are the basic units that handle the request by reading and processing the data. The results are accumulated and sent back to the root which outputs the result of the query. and are responsible for processing  Google BigQuery and Dremel can easily be extended
• Multiple data types: Data types like Integer, Float, Boolean, and Strings are all supported by Google BigQuery. 
• Security: The Data in Google BigQuery is automatically encrypted when the transfer from storage takes place. It also isolates the Jobs so that it can secure multi-user activity. The Security of other Google Cloud Platforms is integrated into BigQuery to provide enterprises with a big picture of security. Access Management (IAM) can set permissions for users to access tables, views, and, records. Users can also share Google Cloud Identity and Access Management(IAM).  

Steps to Connect Superset BigQuery

Superset BigQuery connection: Prerequisites

1. Before proceeding to connect Superset BigQuery, you need to follow the steps provided in Google Cloud Page, to provide the Authentication.
2. Install pybigquery.
3. Download your Google Cloud authorization JSON key file.
4. From your terminal instance, set GOOGLE_APPLICATION_CREDENTIALS env. var to the path of your JSON key file.

Superset BigQuery connection: Steps

To Connect Superset BigQuery for querying BigQuery Datasets, we will use the pybigquery SQLAlchemy plugin.

1. Download the pybigquery plugin for connecting Superset BigQuery.
2. Use this guide to install new database drivers in docker for installing plugins.
3. Use the following command to install pybigquery for the Superset BigQuery connection.

echo "pybigquery">> ./docker/requirements-local.txt

4. This will boot up the Superset services and after all the images are rebuilt, go to the sources tab and select databases. This will be sued to select Bigquery as a database for the Superset BigQuery connection.
5. In the navigation bar click on the + button that is present next to Filter List. This will open the page where details of the database are required for the Superset BigQuery connection.

6. On the next page fill out the details of the database
   1. Database: This is the name of the database. Select a name that is easy to remember
   2. SQLAlchemy URL: The SQLAlchemy URI for BigQuery looks like: bigquery://{project_id}. To find your BigQuery project_id, navigate back to the BigQuery console and you’ll find it under Resources.

7. Make sure to tick the option Expose in SQL Lab for Superset BigQuery connection.

8. In the Extra field add the BigQuery credentials JSON file structured in the given format. This will ensure the Superset BigQuery connection.

 {
          "credentials_info": {
            <DATA_FROM_CREDENTIALS_FILE>
          }
        }
   Here's a more tangible example (obviously you'd replace the blanks with data from your JSON file):
       {
          "credentials_info": {
            "type": "service_account",
            "project_id": "_____",
            "private_key_id": "____",
            "private_key": "-----BEGIN PRIVATE KEY-----
            n____
            ______
            END PRIVATE KEY-----n",
            "client_email": "___",
            "client_id": "____",
            "auth_uri": "____",
            "token_uri": "____",
            "auth_provider_x509_cert_url": "____",
            "client_x509_cert_url": "____"
          }
        }

9. After all the fields are filled, click the Test Connection button. This will test the Superset Bigquery connection we just set up.
10. This will test if your Superset instance is talking to BigQuery Project in the Superset BigQuery connection.

11. If the OK sign comes, this represents that the connection of Superset BigQuery is established.

Conclusion

BigQuery is a flexible, easy-to-use, and cost-effective data warehouse. This is used by many enterprises to store large amounts of data. BigQuery is very efficient in processing queries for these data and is backed by Google means it has wide support, provides better connections to others, and is highly secure.

Apache Superset is a Business Intelligence tool that is known for its efficiency in processing petabytes of scale data.

It is highly efficient in visualizing large amounts of data that are not possible in regular BI tools. Connecting Superset BigQuery allows utilizing the vast amounts of data stored in BigQuery efficiently. This article gives a step-by-step guide on Superset BigQuery Connection.

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