Link copied to clipboard # Databricks vs Google Cloud Managed Service for Apache Spark Comparison - What are their main differences? Share [ Export ](/compare/databricks/google-cloud-managed-service-for-apache-spark/export.csv) DatabricksGoogle Cloud Managed Service for Apache Spark[+ Add Product](#) Pricing **Databricks** Free Trial **$99.00** Per Feature Per Month · Basic **Google Cloud Managed Service for Apache Spark** No pricing available **Pricing Insights** from reviews Perceived Cost **Databricks** $$$$$ **Google Cloud Managed Service for Apache Spark** $$$$$ Average Discount **Databricks** 14% **Google Cloud Managed Service for Apache Spark** Not enough reviews yet Uncover valuable pricing insights from real user feedback to improve your shopping experience. See it for free Time to Implement **Databricks** 6 months Based on 100 responses **Google Cloud Managed Service for Apache Spark** 6 months Based on 100 responses Return on Investment **Databricks** 6 months Based on 100 responses **Google Cloud Managed Service for Apache Spark** 6 months Based on 100 responses **Pros & Cons** from reviews **Databricks** Features (288) Ease of Use (278) Learning Curve (112) Expensive (97) **Google Cloud Managed Service for Apache Spark** Not enough data Integrations **Databricks** [ ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_f6ee5c9dddbf5c28b136829c8782ed1b/slack.png "Product Avatar Image") Slack ](https://www.g2.com/products/slack/reviews)[ View all ](https://www.g2.com/products/databricks/integrations) **Google Cloud Managed Service for Apache Spark** [ ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_7c23f7efbbad335c53ed6bdca9953572/etl-tools.png "Product Avatar Image") ETL tools ](https://www.g2.com/products/etl-tools/reviews)[ View all ](https://www.g2.com/products/google-cloud-managed-service-for-apache-spark/integrations) Screenshots **Databricks** ![Databricks screenshot](https://images.g2crowd.com/cdn-cgi/image/width=600,height=600,fit=scale-down,format=auto,onerror=redirect,/https://images.g2crowd.com/uploads/attachment/file/1231334/automated-etl-processing-img.png) **Google Cloud Managed Service for Apache Spark** No screenshot available **Ratings** from reviews Meets Requirements **Databricks** **9.0** /10 Based on 684 responses **Google Cloud Managed Service for Apache Spark** **8.6** /10 Based on 11 responses Ease of Use **Databricks** **8.8** /10 Based on 1,072 responses **Google Cloud Managed Service for Apache Spark** **8.8** /10 Based on 11 responses Ease of Setup **Databricks** **8.4** /10 Based on 919 responses **Google Cloud Managed Service for Apache Spark** Not enough data Ease of Admin **Databricks** **8.4** /10 Based on 203 responses **Google Cloud Managed Service for Apache Spark** Not enough data Quality of Support **Databricks** **8.7** /10 Based on 669 responses **Google Cloud Managed Service for Apache Spark** **8.8** /10 Based on 11 responses Has the product been a good partner in doing business? **Databricks** **8.9** /10 Based on 206 responses **Google Cloud Managed Service for Apache Spark** Not enough data Product Direction (% positive) **Databricks** **9.6** /10 Based on 674 responses **Google Cloud Managed Service for Apache Spark** **8.9** /10 Based on 11 responses **Features** ratings from reviews Big Data Processing and Distribution **9.0** /10 Based on 543 responses **8.7** /10 Based on 10 responses Business Intelligence **8.5** /10 Based on 399 responses Not enough data Data Visualization **8.9** /10 Based on 44 responses Not enough data Uncover more feature rated insights from real users to improve your shopping experience. See it for free Analytics Platforms **9.2** /10 Based on 405 responses Not enough data Data Governance **8.8** /10 Based on 144 responses Not enough data ETL Tools **9.0** /10 Based on 220 responses Not enough data MLOps Platforms **9.0** /10 Based on 101 responses Not enough data Data Science and Machine Learning Platforms **8.7** /10 Based on 246 responses Not enough data Data Warehouse **8.9** /10 Based on 246 responses Not enough data DataOps Platforms **9.1** /10 Based on 103 responses Not enough data Generative AI Infrastructure **9.3** /10 Based on 32 responses Not enough data AI Agent Builders **10.0** /10 Based on 22 responses Not enough data Big Data Analytics **8.9** /10 Based on 286 responses Not enough data Big Data **8.7** /10 Based on 367 responses Not enough data User Insights **Reviewers' Company Size** Small-Business(50 or fewer emp.) 15.1% Mid-Market(51-1000 emp.) 37.5% Enterprise(\> 1000 emp.) 47.3% Small-Business(50 or fewer emp.) 15.1% Mid-Market(51-1000 emp.) 37.5% Enterprise(\> 1000 emp.) 47.3% Small-Business(50 or fewer emp.) 25.0% Mid-Market(51-1000 emp.) 43.8% Enterprise(\> 1000 emp.) 31.3% Small-Business(50 or fewer emp.) 25.0% Mid-Market(51-1000 emp.) 43.8% Enterprise(\> 1000 emp.) 31.3% **Reviewers' Industry** [![Databricks](https://images.g2crowd.com/uploads/product/hd_favicon/135d1c9e39e19ef56dc8d0bf90fce20f/databricks.svg "Databricks")](https://www.g2.com/products/databricks/reviews) Databricks Information Technology and Services 13.2% Financial Services 12.0% Computer Software 7.6% Consulting 4.8% Manufacturing 4.6% Other 57.6% [![Google Cloud Managed Service for Apache Spark](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_0c51d2c2e5f85fe45126eb818f748267/google-cloud-managed-service-for-apache-spark.png "Google Cloud Managed Service for Apache Spark")](https://www.g2.com/products/google-cloud-managed-service-for-apache-spark/reviews) Google Cloud Managed Service for Apache Spark Information Technology and Services 25.0% Retail 12.5% Marketing and Advertising 6.3% Hospital & Health Care 6.3% Higher Education 6.3% Other 43.8% Alternatives **Databricks Alternatives** ![Cloudera](https://images.g2crowd.com/uploads/product/hd_favicon/dcfb9e6879febb34cef773ddde921835/cloudera.svg "Cloudera") [ Cloudera ](/products/cloudera/reviews) ![Snowflake](https://images.g2crowd.com/uploads/product/hd_favicon/1544409170/snowflake.svg "Snowflake") [ Snowflake ](/products/snowflake/reviews) ![Teradata Autonomous Knowledge Platform](https://images.g2crowd.com/uploads/product/hd_favicon/18effe7340087cf76270e479644d3e42/teradata-autonomous-knowledge-platform.svg "Teradata Autonomous Knowledge Platform") [ Teradata Autonomous Knowledge Platform ](/products/teradata-autonomous-knowledge-platform/reviews) ![Qubole](https://images.g2crowd.com/uploads/product/hd_favicon/1542306394/qubole.svg "Qubole") [ Qubole ](/products/qubole/reviews) **Google Cloud Managed Service for Apache Spark Alternatives** ![Cloudera](https://images.g2crowd.com/uploads/product/hd_favicon/dcfb9e6879febb34cef773ddde921835/cloudera.svg "Cloudera") [ Cloudera ](/products/cloudera/reviews) ![Azure Data Factory](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_f176b4154a751d10150daa67a57b7dc5/azure-data-factory.jpg "Azure Data Factory") [ Azure Data Factory ](/products/azure-data-factory/reviews) ![Amazon EMR](https://images.g2crowd.com/uploads/product/hd_favicon/1528476487/amazon-emr.svg "Amazon EMR") [ Amazon EMR ](/products/amazon-emr/reviews) ![Azure Data Lake Store](https://images.g2crowd.com/uploads/product/image/thumb_square/thumb_square_f176b4154a751d10150daa67a57b7dc5/azure-data-lake-store.jpg "Azure Data Lake Store") [ Azure Data Lake Store ](/products/azure-data-lake-store/reviews) ## Databricks vs Google Cloud Managed Service for Apache Spark FAQs Generated using AI Last updated: August 14, 2026 ### What is the difference between Databricks vs Google Cloud Managed Service for Apache Spark? Databricks stands out for its higher G2 rating, broader review base, and reviewer-cited strengths in unified data governance and AI, while Google Cloud Managed Service for Apache Spark is favored for its managed infrastructure and integration with Google Cloud services. | | [Databricks](https://www.g2.com/products/databricks/reviews) | [Google Cloud Managed Service for Apache Spark](https://www.g2.com/products/google-cloud-managed-service-for-apache-spark/reviews) | | --- | --- | --- | | G2 Rating (reviews) | 4.6/5 (1366 reviews) | 4.3/5 (18 reviews) | | Ease of Setup score | 8.4 | — | | Quality of Support score | 8.8 | 8.8 | | Top Reviewer-Cited Strength | Unified data governance, collaborative notebooks, and AI integration | Managed infrastructure, autoscaling, and integration with BigQuery and Cloud Storage | ### How do the pricing models of Databricks and Google Cloud Managed Service for Apache Spark compare? Databricks is rated higher for value, with reviewers expressing more satisfaction with its pricing model compared to Google Cloud Managed Service for Apache Spark. - **Databricks:** Reviewers appreciate the pay-for-what-you-use model, serverless compute, and auto-scaling, but note that costs can increase if compute usage isn't monitored closely. - **Google Cloud Managed Service for Apache Spark:** Reviewers highlight cost savings from idle cluster deletion and autoscaling, but mention that pricing can become confusing when jobs run longer than expected and wish for clearer cost visibility upfront. ### What are the best alternatives to Databricks and Google Cloud Managed Service for Apache Spark? The top three alternatives to Databricks and Google Cloud Managed Service for Apache Spark are Cloudera, Snowflake, and Teradata Autonomous Knowledge Platform. | Product | G2 Rating (reviews) | Largest Segment | Pricing Insight | Top Reviewer-Cited Strength | | --- | --- | --- | --- | --- | | [Databricks](https://www.g2.com/products/databricks/reviews) | 4.6/5 (1366 reviews) | — | Pay-for-what-you-use model with serverless compute and auto-scaling | Unified data governance, collaborative notebooks, and AI integration | | [Google Cloud Managed Service for Apache Spark](https://www.g2.com/products/google-cloud-managed-service-for-apache-spark/reviews) | 4.3/5 (18 reviews) | — | Idle cluster deletion and autoscaling help save costs, but pricing can be confusing for long jobs | Managed infrastructure, autoscaling, and integration with BigQuery and Cloud Storage | | [Cloudera](https://www.g2.com/products/cloudera/reviews) | 4.1/5 (136 reviews) | Enterprise | — | Enterprise-grade big data processing and distribution | | [Snowflake](https://www.g2.com/products/snowflake/reviews) | 4.6/5 (763 reviews) | Mid-Market | — | Highly scalable cloud data warehousing | | [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) | 4.3/5 (376 reviews) | Enterprise | — | Comprehensive analytics and data management for large enterprises | ### Which Big Data Processing and Distribution features should I prioritize when comparing Databricks and Google Cloud Managed Service for Apache Spark? Buyers should prioritize data governance, integration capabilities, scalability, ease of setup, and AI/ML support when comparing Databricks and Google Cloud Managed Service for Apache Spark. - **Data governance:** Databricks is frequently cited for its Unity Catalog and centralized governance features; Google Cloud Managed Service for Apache Spark integrates with Google Cloud IAM but is less frequently mentioned for governance in reviews. - **Integration capabilities:** Databricks is noted for integrations with Git, Tableau, and GitHub; Google Cloud Managed Service for Apache Spark is highlighted for integration with BigQuery and Cloud Storage. - **Scalability:** Both products are praised for autoscaling and handling large data volumes, with Google Cloud Managed Service for Apache Spark reviewers specifically mentioning autoscaling and managed infrastructure. - **Ease of setup:** Databricks has an Ease of Setup score of 8.4, with reviewers noting straightforward onboarding; Google Cloud Managed Service for Apache Spark reviewers mention quick job startup and easy cluster management. - **AI/ML support:** Databricks is frequently cited for AI and machine learning workflows, collaborative notebooks, and built-in AI assistants. ### What are the pros and cons of Databricks vs Google Cloud Managed Service for Apache Spark? Databricks is strongest in unified data governance and AI integration, while Google Cloud Managed Service for Apache Spark excels in managed infrastructure and autoscaling. - **Databricks strengths:** Unified data governance with Unity Catalog, collaborative notebooks, AI and machine learning integration, broad data source connectivity, and a pay-for-what-you-use pricing model. - **Databricks trade-offs:** Reviewers note a steep learning curve for new users, potential for rising costs if compute usage is not monitored, and occasional challenges with documentation and onboarding. - **Google Cloud Managed Service for Apache Spark strengths:** Managed infrastructure that eliminates manual cluster management, autoscaling for peak loads, integration with BigQuery and Cloud Storage, and cost savings from idle cluster deletion. - **Google Cloud Managed Service for Apache Spark trade-offs:** Reviewers mention confusing pricing for long-running jobs, documentation that could be improved, and occasional delays in autoscaling or cluster startup. ### Is Databricks or Google Cloud Managed Service for Apache Spark better for small businesses? Databricks is the better fit for small businesses, with reviewers highlighting its ease of use and onboarding for smaller teams. - **Databricks:** Reviewers from small businesses frequently cite the platform's user-friendly interface, straightforward onboarding, and collaborative features as valuable for smaller teams. - **Google Cloud Managed Service for Apache Spark:** Reviewers note the benefits of managed infrastructure and cost-saving features, but do not emphasize small-business fit as strongly as Databricks reviewers. ### Which Big Data Processing and Distribution platform has better integrations? Databricks is favored for integrations, with reviewers citing a broader range of third-party tool connections. - **Databricks:** Integrations with Git (30+ all-time mentions), Tableau, and GitHub are frequently cited by reviewers. - **Google Cloud Managed Service for Apache Spark:** Reviewers highlight integration with BigQuery and Cloud Storage, but do not mention as many third-party integrations as Databricks. ### How do Databricks and Google Cloud Managed Service for Apache Spark compare on customer support? Databricks and Google Cloud Managed Service for Apache Spark are rated equally on Quality of Support, both scoring 8.8, indicating parity in reviewer satisfaction. - **Databricks:** Reviewers describe helpful support teams and smooth onboarding, with some noting a desire for more detailed documentation and onboarding guidance. - **Google Cloud Managed Service for Apache Spark:** Reviewers mention helpful GCP support for critical issues and generally positive experiences, with occasional feedback about documentation improvements. ### Which is easier to implement, Databricks or Google Cloud Managed Service for Apache Spark? Databricks has a published Ease of Setup score of 8.4, while Google Cloud Managed Service for Apache Spark does not have a published score, indicating Databricks is more frequently cited for ease of implementation. - **Databricks:** Reviewers consistently mention straightforward onboarding, simple initial setup, and efficient integration into workflows, with scores of 6-7 out of 7 in recent reviews. - **Google Cloud Managed Service for Apache Spark:** Reviewers note that cluster setup is easy and job startup is quick, but also mention occasional delays in cluster creation and a learning curve for those new to the GCP ecosystem. ### Which product has better Cloud? Databricks is more frequently cited for its cloud capabilities, with reviewers highlighting its cloud-based architecture and scalability, while Google Cloud Managed Service for Apache Spark is noted for its managed cloud infrastructure and integration with Google Cloud services. - **Databricks:** Reviewers frequently mention its cloud-based platform, scalability, and ability to handle large data volumes efficiently. - **Google Cloud Managed Service for Apache Spark:** Reviewers highlight managed cloud infrastructure, autoscaling, and integration with other Google Cloud services as key strengths.