Databricks vs Google Cloud Dataflow comparison

Databricks and Google are both solutions in the Streaming Analytics category. Databricks is ranked #1 with an average rating of 8.2, while Google is ranked #8 with an average rating of 7.7. Databricks holds a 14.0% mindshare in SA, compared to Google’s 8.3% mindshare. Additionally, 96% of Databricks users are willing to recommend the solution, compared to 90% of Google users who would recommend it.

Databricks

Read 82 Databricks reviews

7,736 Views
5,084 Comparison Views

96% willing to recommend

Google Cloud Dataflow

Read 10 Google Cloud Dataflow reviews

4,001 Views
3,284 Comparison Views

90% willing to recommend

Databricks

Google Cloud Dataflow

Comparison Buyer's Guide

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Executive SummaryUpdated on Oct 8, 2024

Databricks and Google Cloud Dataflow compete in cloud data processing. Databricks appears to have the upper hand due to its collaborative development environment and robust analytics support, while Google Cloud Dataflow is preferred for its serverless architecture and scalability.

Features: Databricks offers a collaborative development environment, strong integration with various languages, and advanced analytics support. Google Cloud Dataflow provides robust stream and batch processing, automatic scaling, and a unified programming model.

Room for Improvement: Databricks could improve resource management, simplify its setup, and enhance usability. Google Cloud Dataflow needs more intuitive configuration, better error handling, and streamlined operational aspects.

Ease of Deployment and Customer Service: Databricks has straightforward deployment and strong support but complex initial configuration. Google Cloud Dataflow integrates easily with Google services but requires technical expertise for full potential. Databricks has higher support satisfaction, while Google Cloud Dataflow shines in integration.

Pricing and ROI: Databricks offers competitive pricing and notable ROI for analytical workloads. Google Cloud Dataflow's pricing is higher but offsets with performance efficiency. Databricks is more cost-effective for smaller teams, whereas Google Cloud Dataflow benefits large-scale operations.

To learn more, read our detailed Databricks vs. Google Cloud Dataflow Report (Updated: October 2024).

Buyer's Guide

Databricks vs. Google Cloud Dataflow

October 2024

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Categories and Ranking

Databricks

Ranking in Streaming Analytics

1st

Average Rating

8.2

Number of Reviews

Ranking in other categories

Data Science Platforms (1st)

Google Cloud Dataflow

Ranking in Streaming Analytics

8th

Average Rating

7.8

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of November 2024, in the Streaming Analytics category, the mindshare of Databricks is 14.0%, up from 9.6% compared to the previous year. The mindshare of Google Cloud Dataflow is 8.3%, up from 6.6% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Streaming Analytics

Featured Reviews

Dunstan Matekenya

Data Scientist at a financial services firm with 10,001+ employees

Jul 10, 2024

Process large-scale data sets and integrates with Apache Spark with notebook environment

I primarily use Databricks to process large-scale data sets with Apache Spark. My main use case is processing large data sets, such as 600 GB or 800 GB Databricks integrates natively with Apache Spark, which I use as a processing engine for large-scale datasets. This native integration is one of…

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Tamer Talal

Satellite System Engineer at NARSS

Feb 14, 2024

A tool useful for data transmission and data storage that needs to improve its authentication area

I use the solution in my company for data transmission and data storage One of the good features of the product is the overall capacity that it provides to its users. Though the speed of the product is good, the main feature of the product that I like is its capacity. The authentication part of…

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"The most valuable features of the solution are the hardware and the resources it quickly provides without much hassle."

"The most valuable aspect of the solution is its notebook. It's quite convenient to use, both terms of the research and the development and also the final deployment, I can just declare the spark jobs by the load tables. It's quite convenient."

"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."

"The load distribution capabilities are good, and you can perform data processing tasks very quickly."

"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."

"Databricks is a unified solution that we can use for streaming. It is supporting open source languages, which are cloud-agnostic. When I do database coding if any other tool has a similar language pack to Excel or SQL, I can use the same knowledge, limiting the need to learn new things. It supports a lot of Python libraries where I can use some very easily."

"The solution offers a free community version."

"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."

More Databricks pros

"The product's installation process is easy...The tool's maintenance part is somewhat easy."

"It is a scalable solution."

"The most valuable features of Google Cloud Dataflow are scalability and connectivity."

"I don't need a server running all the time while using the tool. It is also easy to setup. The product offers a pay-as-you-go service."

"The most valuable features of Google Cloud Dataflow are the integration, it's very simple if you have the complete stack, which we are using. It is overall very easy to use, user-friendly friendly, and cost-effective if you know how to use it. The solution is very flexible for programmers, if you know how to do scripts or program in Python or any other language, it's extremely easy to use."

"The best feature of Google Cloud Dataflow is its practical connectedness."

"The solution allows us to program in any language we desire."

"Google Cloud Dataflow is useful for streaming and data pipelines."

More Google Cloud Dataflow pros

Cons

"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."

"The query plan is not easy with Databrick's job level. If I want to tune any of the code, it is not easily available in the blogs as well."

"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."

"The connectivity with various BI tools could be improved, specifically the performance and real time integration."

"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."

"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."

"Databricks could improve in some of its functionality."

"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."

More Databricks cons

"Google Cloud Data Flow can improve by having full simple integration with Kafka topics. It's not that complicated, but it could improve a bit. The UI is easy to use but the experience could be better. There are other tools available that do a better job."

"I would like Google Cloud Dataflow to be integrated with IT data flow and other related services to make it easier to use as it is a complex tool."

"The technical support has slight room for improvement."

"The deployment time could also be reduced."

"When I deploy the product in local errors, a lot of errors pop up which are not always caught. The solution's error logging is bad. It can take a lot of time to debug the errors. It needs to have better logs."

"Google Cloud Dataflow should include a little cost optimization."

"They should do a market survey and then make improvements."

"The authentication part of the product is an area of concern where improvements are required."

More Google Cloud Dataflow cons

Pricing and Cost Advice

"The price is okay. It's competitive."

"Databricks' cost could be improved."

"We find Databricks to be very expensive, although this improved when we found out how to shut it down at night."

"It is an expensive tool. The licensing model is a pay-as-you-go one."

"The billing of Databricks can be difficult and should improve."

"We have only incurred the cost of our AWS cloud services. This is because during this period, Databricks provided us with an extended evaluation period, and we have not spent much money yet. We are just starting to incur costs this month, I will know more later on the full cost perspective."

"We implement this solution on behalf of our customers who have their own Azure subscription and they pay for Databricks themselves. The pricing is more expensive if you have large volumes of data."

"The cost for Databricks depends on the use case. I work on it as a consultant, so I'm using the client's Databricks, so it depends on how big the client is."

More Databricks pricing and cost advice

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate Google Cloud Dataflow's pricing a four out of ten."

"Google Cloud Dataflow is a cheap solution."

"The price of the solution depends on many factors, such as how they pay for tools in the company and its size."

"On a scale from one to ten, where one is cheap, and ten is expensive, I rate the solution's pricing a seven to eight out of ten."

"Google Cloud is slightly cheaper than AWS."

"The solution is not very expensive."

"The tool is cheap."

"The solution is cost-effective."

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Top Industries

By visitors reading reviews

Financial Services Firm

16%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Financial Services Firm

17%

Computer Software Company

12%

Retailer

12%

Manufacturing Company

11%

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

Which do you prefer - Databricks or Azure Machine Learning Studio?

Databricks gives you the option of working with several different languages, such as SQL, R, Scala, Apache Spark, or Python. It offers many different cluster choices and excellent integration with ...

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How would you compare Databricks vs Amazon SageMaker?

We researched AWS SageMaker, but in the end, we chose Databricks. Databricks is a Unified Analytics Platform designed to accelerate innovation projects. It is based on Spark so it is very fast. It...

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Which would you choose - Databricks or Azure Stream Analytics?

Databricks is an easy-to-set-up and versatile tool for data management, analysis, and business analytics. For analytics teams that have to interpret data to further the business goals of their orga...

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What do you like most about Google Cloud Dataflow?

The product's installation process is easy...The tool's maintenance part is somewhat easy.

See all answers

What is your experience regarding pricing and costs for Google Cloud Dataflow?

The solution is not very expensive.

See all answers

What needs improvement with Google Cloud Dataflow?

The authentication part of the product is an area of concern where improvements are required. For some common users, the solution's authentication part is difficult to use. The scalability of the p...

See all answers

Comparisons

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Informatica PowerCenter vs Databricks

Compared 8% of the time

Amazon SageMaker vs Databricks

Compared 8% of the time

Dremio vs Databricks

Compared 8% of the time

Azure Stream Analytics vs Databricks

Compared 5% of the time

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Compared 12% of the time

Apache NiFi vs Google Cloud Dataflow

Compared 11% of the time

Apache Flink vs Google Cloud Dataflow

Compared 9% of the time

Spring Cloud Data Flow vs Google Cloud Dataflow

Compared 8% of the time

Amazon Kinesis vs Google Cloud Dataflow

Compared 5% of the time

More Google Cloud Dataflow Competitors

Product Reports

Buyer's Guide

Databricks

November 2024

Download Databricks product report

Buyer's Guide

Streaming Analytics

October 2024

Download Google Cloud Dataflow product report

Also Known As

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Google Dataflow

Learn More

Databricks

Google

Overview

Databricks is utilized for advanced analytics, big data processing, machine learning models, ETL operations, data engineering, streaming analytics, and integrating multiple data sources.

Organizations leverage Databricks for predictive analysis, data pipelines, data science, and unifying data architectures. It is also used for consulting projects, financial reporting, and creating APIs. Industries like insurance, retail, manufacturing, and pharmaceuticals use Databricks for data management and analytics due to its user-friendly interface, built-in machine learning libraries, support for multiple programming languages, scalability, and fast processing.

What are the key features of Databricks?

User-friendly interface: Simplifies operations and usability.
Built-in machine learning libraries: Facilitates machine learning tasks.
Support for multiple programming languages: Enhances flexibility.
Scalability: Efficiently handles growing data needs.
Fast processing: Improves performance and speed.
Automated optimization: Reduces manual efforts.
Data visualization: Provides insightful visuals.
Collaborative features: Enhances teamwork.
Delta Lake performance: Boosts data management.
Seamless cluster management: Simplifies system operations.

What are the benefits or ROI to look for in Databricks reviews?

Efficiency in handling large datasets: Ensures smooth processing.
Interactive workspace environment: Improves user collaboration.
Integration with platforms: Provides connectivity benefits.
Performance optimization: Enhances overall system performance.
Support for data governance and security: Ensures data integrity and protection.

Databricks is implemented in insurance for risk analysis and claims processing; in retail for customer analytics and inventory management; in manufacturing for predictive maintenance and supply chain optimization; and in pharmaceuticals for drug discovery and patient data analysis. Users value its scalability, machine learning support, collaboration tools, and Delta Lake performance but seek improvements in visualization, pricing, and integration with BI tools.

Google Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Sample Customers

Elsevier, MyFitnessPal, Sharethrough, Automatic Labs, Celtra, Radius Intelligence, Yesware

Absolutdata, Backflip Studios, Bluecore, Claritics, Crystalloids, Energyworx, GenieConnect, Leanplum, Nomanini, Redbus, Streak, TabTale

Buyer's Guide

Databricks vs. Google Cloud Dataflow

October 2024

Free Report: Databricks vs. Google Cloud Dataflow

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