Databricks vs BigQuery: 5 Critical Differences

Exponentially growing data and the need to quickly process it to gain valuable insights is an ongoing challenge that most businesses face. An efficient solution is to switch from the traditional on-premises data storage solutions to cloud-based data Warehousing platforms like Google BigQuery and Databricks.

When comparing Databricks vs BigQuery, performance, ease-of-use & cost are some of the most crucial factors to decide between these two Cloud Data Warehousing giants. In this article, you will learn about 5 major differences between Databricks vs BigQuery.  

What is Databricks?

Databricks is a flexible Cloud Data Lakehousing Engine that allows you to prepare & process data, train models, and manage the entire Machine Learning Lifecycle, from testing to production. Built on top of Apache Spark, a fast and generic engine for Large-Scale Data Processing, Databricks delivers reliable, top-notch performance. 

Key Features of Databricks

Databricks offers the following eye-catching features:

• Popular Integrations: Databricks is also integrated with major Cloud service providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. This allows you to start using Databricks on top of your desired Cloud Storage platform, giving you more control over your data as it remains in your Cloud account and Data Sources. 
• Data Science Capabilities: Acting as a unified solution for all your Data Science, Machine Learning, and enterprise teams, it offers features like MFlow for complete ML Lifecycle Management, BI Reporting on Delta Lake for Real-time Business Analytics, and the Databricks Workspace that promotes workplace collaboration, where several teams can interact and work at the same time.
• User Friendly: In addition to being easy to use, it also supports programming languages ​​like Python, R, Java, and SQL. 
• Delta Lake: On top of your Data lakes, Databricks provides Delta Lake, an Open Format Storage Layer that assists in ACID transactions, Scalable Metadata Handling, and unifies Streaming and Batch Data Processing.

What is Google BigQuery?

Launched in 2010, BigQuery is a Cloud-Based Data Warehouse service offered by Google. It is built to handle petabytes of data and can automatically scale as your business flourishes. Developers at Google have designed its architecture to keep the storage and computing resources separate. This makes querying more fluid as you can scale them independently without sacrificing performance.

Since there is no physical infrastructure present similar to the conventional server rooms for you to manage and maintain, you can focus all your workforce and effort on important business goals. Using standard SQL, you can accurately analyze your data and execute complex queries from multiple users simultaneously.

Key Features of Google BigQuery

Google BigQuery has continuously evolved over the years and is offering some of the most intuitive features :

• User Friendly: With just a few clicks, you can start storing and analyzing your data in Big Query. An easy-to-understand interface with simple instructions at every step allows you to set up your cloud data warehouse quickly as you don’t need to deploy clusters, set your storage size, or compression and encryption settings.    
• On-Demand Storage Scaling: With ever-growing data needs, you can rest assured that it will scale automatically when required. Based on Colossus (Google Global Storage System), it stores data in a columnar format with the ability to directly work on the compressed data without decompressing the files on the go.
• Real-Time Analytics: Stay updated with real-time data transfer and accelerated analytics as BigQuery optimally allocates any number of resources to provide the best performance and provide results so that you can generate business reports when requested.
• BigQuery ML: Armed with machine learning capabilities, you can effectively design and build data models using existing SQL Commands. This eliminates the need for technical know-how of machine learning and empowers your data analysts to directly evaluate ML models.

Databricks vs BigQuery: Quick Comparison Table

Feature	Databricks	BigQuery
Architecture	Built on Apache Spark, combines a data lake and data warehouse (Lakehouse)	Serverless architecture, separates storage and compute resources
Integration	Integrates with AWS, Azure, and GCP, supports Spark-based workflows	Deep integration with Google Cloud Platform (GCP) services
Data Processing	Supports real-time, batch processing, and advanced analytics	Optimized for high-performance SQL queries and large-scale analytics
Machine Learning	Native support for MLflow and Delta Lake for the full ML lifecycle	Built-in BigQuery ML for SQL-based machine learning
Usability	Flexible, supports multiple languages (Python, R, SQL, Scala)	Beginner-friendly UI with SQL-based querying
Pricing	Pay-as-you-go for compute, with discounts for committed use	Pay-per-use or flat-rate pricing for queries and storage

Databricks vs BigQuery: 5 Key Differences

Architecture

BigQuery’s unique serverless architecture separates storage and computing resources, thereby enabling them to scale independently on demand. This structure provides customers with both incredible flexibility and cost control because they don’t have to keep running expensive computing resources all the time. 

• BigQuery uses Dremel to quickly carry out SQL queries on massive datasets. Dremel converts your SQL Queries to Execution trees containing leaves known as slots. Slots take care of compute-intensive tasks like reading data from the storage and performing the necessary aggregations. To make the whole process more efficient and fluid, Dremels assigns the slots to queries on an as-required basis while maintaining the performance for concurrent queries from multiple users. 
• For Storage, BigQuery uses Colossus for data storage to leverage its columnar storage format and compression algorithm optimized for reading large amounts of structured data. 

When comparing Databricks vs BigQuery, you can observe at a high level that Databricks operates out of a Control plane that consists of several of the backend services managed by Databricks in its own AWS account & a data plane that processes your data.

Unlike Google BigQuery, Databricks offers a Classic data plane where the compute resources are used from your AWS Account as well as a Serverless Data plane where the compute layer exists in the Databricks cloud account rather than the customer’s cloud account.

Usability

Databricks is being widely used by the organizations globally due to the following facts:

• Since Databricks is built on top of Open Source Tools, there is well-developed support & community for documentation & tutorials.
• Databricks provides SQL Endpoints that allow to easily connect to almost anything stored in AWS S3 in a secure way.
• It also offers full compatibility with popular modern data formats like Avro, Parquet, and JSON.
• Databricks allows you to collaborate on a development project using its notebooks. You can also share these notebooks with your business analysts so that they can use your SQL queries and gain insights from the data.

There is a small learning curve while configuring the spark cluster if you are new to Apache Spark. In comparing Databricks vs BigQuery, BigQuery also offers an easy-to-use interface with the following advantages:

• Since you get the first 10 GB of storage per month and the first 1 TB per month of querying for free, you have more flexibility to conduct experiments.
• BigQuery UI is easy, beginner-friendly, and convenient. 
• You also get a wide range of smooth integrations with RDBMS clients such as aqua data studio, Dbeaver data grid, etc.
• Acts as an excellent SQL data management tool fully managed & maintained instances & clusters with on-demand scaling of both storage and compute resources.

Use Cases

When comparing Databricks vs BigQuery, you will observe that Databricks is most preferred for the following scenarios:

• Native integration with a highly optimized Apache Spark Engine and MLflow to carry out your Data Science projects involving data analysis/wrangling to feature creation, training, finetuning and model test and validation, and finally to deployment.
• When you need better collaboration over your data projects, you can use Databricks’s cross-company Shared Workspaces to provide better visibility to your results.
• More flexibility for users as it allows you to code in multiple languages (SQL, Python, Scala, R).

Google BigQuery can be quite handy in the following cases:

• In business cases where you are using tools from the Google Suite like Google Analytics & Google Data Studio, opting for BigQuery to process and query your data is a fast and smooth job because of the seamless integration within the Google Cloud Environment.
• Easy to store and manage multiple Data Warehouses when your business is scaling.
• With in-built ML capabilities, Google BigQuery is also used to quickly execute machine learning models in BigQuery using SQL queries. 

Return on Investment

When comparing Databricks vs BigQuery, Databricks can prove to be cost-effective due to the following reasons based on Return on Investment:

• Dataset version management became much easier via the Time Travel feature.
• Executing Spark jobs became much faster when compared to self-managed clusters.
• You may find Production code management a bit complicated as only notebooks are allowed to be executed.
• With compute resources independently scalable from the storage resources, you can carry out data analysis with lower idle time from query execution.
• Automated report & dashboard creation without manually running a script every time saves valuable resources.
• Easy scale in and scale out helps in increasing the overall ROI.

Opting for BigQuery may be efficient because of the following reasons:

• With basic knowledge of SQL commands, you can save time by quickly running queries and pulling out reports.
• You can directly execute automatic rules in Firebase from simple queries in Bigquery.
• Bigquery allows you to eliminate dependency on Data Engineering, thereby adding to the cost savings.
• The pricing model follows the Pay per Use approach which makes it more cost-effective, especially for cases where limited users are accessing the data.
• Ease of use so reduced time to get to use on a daily basis
• Seamless Integration with Google Data Visualization tools i.e Google Data Studio saves a lot of cost on licensing expenses of other tools.
• With a unique data retrieval and storage service, you can assign data access permissions allowing the desired employees across the organization to easily fetch data.

Pricing

When comparing Databricks vs BigQuery, Databricks offers a Pay as you Go pricing model that charges you per second for your compute resources with no upfront costs. Adding to its flexibility, Databricks is available on 3 popular Cloud Service Providers i.e Azure, AWS, and Google Cloud. Databricks also offers committed-use discounts after you commit to a particular level of usage. Also, this is applicable across multiple clouds.

To try out Databricks for your use-cases, you can opt for the 14-day free trial that includes a collaborative environment using Apache SparkTM, SQL, Python, Scala, Delta Lake, MLflow, TensorFlow, Keras, scikit-learn, and more. Since Databricks pricing is based on the compute resource, the Storage, Networking, and related costs will depend on your cloud service provider.

When comparing Databricks vs BigQuery, the pricing model for BigQuery is separate for the storage and the compute resources. For all your SQL Queries, BigQuery offers on-demand pricing where you are charged for the number of bytes processed by each query and flat-rate pricing where you pay for the virtual CPU slots you have bought for running your queries. Google also allows you to combine these two pricing models according to your business needs. For storage, you can opt for Active storage pricing if the table or table partition has been modified in the last 90 days, else you can go for the long-term pricing. 

Conclusion

In this article, you have learned about the 5 critical differences between Databricks vs BigQuery. Databricks is available on 3 different Cloud Service Providers namely, Azure, AWS & Google Cloud. It is built on top of a highly optimized Spark Analytics Engine with a pay-as-you-go pricing model. Google BigQuery offers flexible pricing models that suit businesses of all sizes. With an easy-to-use interface, you can quickly get started with BigQuery. It is fully managed & hence doesn’t require you to have deep technical knowledge about Data Engineering. 

Learn the key differences between Microsoft Fabric and Databricks, and how they compare to BigQuery for data management and analytics.

Though, getting data into Databricks or BigQuery can be a time-consuming and resource-intensive task, especially if you have multiple data sources. To manage & manage the ever-changing data connectors, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse like Databricks, Google BigQuery, or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data. Sign up for Hevo’s 14-day free trial and experience seamless data migration.

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