Databricks BigQuery Connection: 4 Easy Steps

Many enterprise organizations use Databricks to seamlessly store, tidy up, and visualize large amounts of data from multiple sources. The platform is designed to let companies perform a variety of different tasks, primarily including machine learning and AI, both of which are necessary for business intelligence.

BigQuery is a Google Data Warehouse with built-in Geographic Data Intake, Storage, and Analysis tools. To handle complex data and examine massive datasets, it uses ordinary SQL Queries.

This article gives steps for Databricks BigQuery Connection.

What is DataBricks?

Databricks is an enterprise software company that is responsible for the creation of a web-based platform that works with Apache Spark. Databricks lest you easily read and write BigQuery tables in Databricks notebooks. A notebook is simply a web-based interface that lets you connect a document that has runnable code, narrative text, or visualizations. 

Databricks is a popular solution that lets enterprises overcome major challenges when working with larger quantities of siloed data. If you have large amounts of data that you need to break out of silos and analyze on a centralized platform, Databricks is a fantastic platform.

Databricks allow companies to simplify their storage of data in modern warehouses, allowing them access to self-service analytical tools and machine learning options. Databricks rely on four important tools to run efficiently, which are as follows.

1)Apache Spark

Apache Spark is a powerful big data processing engine that allows organizations to compute vast arrays of data, something that was not possible before. The reason why so many companies prefer using Apache Spark is simply that the engine is highly scalable, and lets you unify both streaming and batch data. Plus, it supports SQL, making it incredibly easy to use. 

2)MLFlow

Another important tool in Databricks is MLFlow. This is an open-source tool that lets you manage the lifecycles of all your machine learning applications and pipelines. It’s great for data scientists who want to deploy different ML models and then train the algorithms properly. 

MLFlow simply lets you productionize machine learning, bringing together siloed pipelines, letting you gain deep insights into your data, and thus make decisions that could help you improve the business as a whole.

3)DeltaLake

DeltaLake isn’t a tool, per se. it’s actually an open-source storage layer that sits atop several data lakes, offering better performance, reliability, and security. DeltaLake works seamlessly with Apache Spark APIs and both let you analyze batch or streaming data. 

DeltaLake is ideal when you are working with data that’s stored in the cloud. It offers data management capabilities and better performance, while also expanding the variety of tools that are available at your disposal. If you need optimized business intelligence for your data, as well as AI for managing all your data on a single platform. 

4)Koalas

Koalas is designed to help data scientists improve productivity by letting them work with Apache Spark and big data simultaneously. Most data scientists primarily use Python, which does not have native compatibility with Apache Spark. However, Pandas is a popular data analytics library built using Python that many data scientists like. 

Koalas, on the other hand, is a Pandas API that’s designed to work with Spark. This lets data scientists use it without the need for learning another programming language altogether. It is a handy API that allows data scientists to start using Spark, as long as they know a thing or two about the Pandas Python library. 

What is BigQuery?

Google BigQuery was developed as a flexible, fast, and powerful Data Warehouse that’s tightly integrated with the other services offered by the Google Platform. It offers user-based pricing, is cost-efficient, and has a Serverless Model. Google BigQuery’s Analytics and Data Warehouse platform leverages a built-in Query Engine on top of the Serverless Model that allows it to process terabytes of data in seconds.

With Google BigQuery, you can run analytics at scale with anywhere between 26% to 34% lower Three-year TCO than other Cloud Data Warehouse alternatives. Since there is no infrastructure to manage or set up, you can focus on finding meaningful insights with the help of Standard SQL and flexible pricing models ranging across Flat-rate and On-demand options.

Google BigQuery’s column-based Storage service provided the impetus for the Data Warehouse’s speed and its ability to handle huge volumes of data. Since Column-based Storage allows you to process only the columns of interest, it enables you to obtain faster answers and use resources more efficiently. Therefore, for analytical databases, it is more beneficial to store data by column. 

Key Features of Google BigQuery

Here are a few key features of Google BigQuery:

• Serverless Service: Generally in a Data Warehouse environment, organizations need to commit and specify the server hardware on which computations will run. Administrators then have to provision for performance, reliability, elasticity, and security. A Serverless Model helps overcome this constraint. In a Serverless Model, the processing is automatically distributed across a large number of machines working in parallel. By using Google BigQuery’s Serverless model, Database Administrators and Data Engineers focus less on infrastructure and more on provisioning servers. This allows them to gain more valuable insights from data.
• SQL and Programming Language Support: Users can access Google BigQuery through Standard SQL. Apart from this, Google BigQuery also has client libraries for writing applications that access data using Python, C#, Java, PHP, Node.js, Ruby and Go.  
• Tree Architecture: Google BigQuery and Dremel can easily scale to thousands of machines by structuring computations as an Execution Tree. A Root Server obtains incoming queries and relays them to branches, called Mixers. These branches then modify the incoming queries and deliver them to Leaf Nodes, also known as Slots. The Leaf Nodes then take care of filtering and reading the data while working in parallel. The results are moved back down the tree followed by Mixers accumulating the results and finally sending them to the root as the answer to the query.
• Multiple Data Types: Google BigQuery offers support for a vast array of data types including strings, numeric, boolean, struct, array, and a few more.
• Security: Data in Google BigQuery is automatically encrypted either in transit or at rest. Google BigQuery can also isolate jobs and handle security for multi-tenant activity. Since Google BigQuery is integrated with other GCP products’ security features, organizations can take a holistic view of Data Security. It also allows users to share datasets using Google Cloud Identity and Access Management (IAM). Administrators can establish permissions for individuals and groups to access tables, views, and datasets.

How to Connect DataBricks BigQuery?

Now, if you are interested in Databricks BigQuery Connection, there’s a simple process that you can follow. Here’s how it works. 

1)Databricks BigQuery: Enable the BigQuery Storage API

Your first step in DataBricks BigQuery connection is to enable the BigQuery Storage API. In most cases, the BigQuery Storage API is generally enabled by default, as long as you are on a project where BigQuery is being used. However, if there’s an existing project and the API isn’t enabled, you can do so easily.

First of all, access your Cloud Console, then go to the page for the BigQuery Storage API. 

As shown in the screenshot above, you just need to confirm whether the API is enabled or not. 

2)Databricks BigQuery: Create a Databricks Account

Your next step is to create a service account through Identity and Access Management. This will then give permission to any Databricks cluster to run queries against BigQuery. Ideally, you will want to allow some basic permissions to this account so that it runs efficiently. 

TO do so, just head on to the Cloud Console, and click on the page for Service Accounts.

Then, just create a service account, and name it something like “databricks.” You will want to add a small description so that you remember what the account is for. You will have to provide permissions for specific roles to allow service access to Databricks for the project. This will then make it easy for Databricks to read data, and will also let you bring any BigQuery tables within similar projects too. 

Ideally, the following permissions should be granted:

• BigQuery Job User
• BigQuery Read Session User
• BigQuery Data Editor
• BigQuery Data Viewer

Before you close this, do record the email address that you used for creating the service account. 

3)Databricks BigQuery: Creating a Cloud Storage Bucket

A Cloud Storage bucket is required for Databricks BigQuery. This is necessary as the data will have to be buffered. This step is easy; just go to the Cloud Storage Browser, and select CREATE BUCKET. 

You will have to give the bucket a name. This name must be globally unique and must adhere to specific naming guidelines. In case the name has already been used before, you’ll simply get an error message. Once you have added the bucket, you will have to provide the necessary Permissions. 

You can add members and specify their roles using the Service Accounts section, as shown below. 

4)Databricks BigQuery: Deploy Databricks!

Before you can deploy Databricks on Google Cloud, you will have to first set it up on your Google Cloud account. Once you have registered, you can then create a workspace, cluster, or notebook in Python to quickly write code that lets you access BigQuery. 

Just head on to your account console on Databricks, and then select “Create Workspace,” as shown below. 

You will have to enter the Cloud project ID, which is visible in your Google Cloud Console dashboard. Once you’re done with that, the next step is to save your workspace in Databricks. 

Now, once you are done, you will be able to easily connect Databricks BigQuery. 

Important Things to Know 

An important thing to know is that since BigQuery primarily charges you for resources used, you need to make sure that you don’t end up overpaying. As you connect Databricks BigQuery, you will end up using resources from BigQuery. To avoid incurring a charge for such resources, you should consider deleting the project. 

Or, you can either keep the project, but get rid of any discrete resources that were used. Obviously, as is good practice, you should always backup your notebooks and your data before you get rid of Databricks. To clean and remove Databricks, you will have to first start off by canceling the subscription for Databricks in your Google Cloud Console. 

Then, any additional related resources that you had created in the Cloud Console must also be deleted. But, keep in mind that when you remove a Databricks workspace, the Cloud Storage buckets that you created from Databricks are unlikely to be deleted, especially if there is some data in there. 

To remove those, you will have to delete them manually from the Cloud Console. If you are interested in learning more about Databricks and how it works, you can easily access a variety of customer training that is offered through the Databricks Academy.

To access that, just visit the Databricks Academy and then select Academy Login. You just have to select Learning Pathway to display all the courses accordingly. 

Conclusion

This article gives a step-by-step guide on setting up DataBricks BigQuery Connection along with a comprehensive guide on DataBricks and Bigquery.

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