We compared Databricks and Dataiku Data Science Studio based on our user's reviews in several parameters.
In summary, Databricks is praised for its seamless integration and advanced analytics capabilities, while also receiving positive feedback on customer service and pricing. Dataiku Data Science Studio, on the other hand, is appreciated for its intuitive interface and powerful machine learning tools, with users expressing satisfaction with customer support and pricing flexibility. Both platforms offer valuable solutions for data management and analytics, with room for improvement in areas such as data visualization and feature development.
Features: Databricks stands out for its seamless integration with data sources and platforms, collaborative features, advanced analytics, and machine learning capabilities. Dataiku's key strengths lie in its intuitive interface, powerful machine learning capabilities, and seamless integration with various data sources and tools. Users appreciate Dataiku's ease of navigation, efficient machine learning functionalities, and the ability to connect with preferred systems for enhanced workflow efficiency.
Pricing and ROI: Databricks has positive user feedback on pricing, setup cost, and licensing. The pricing is reasonable and competitive, and the setup cost is straightforward. The license terms are flexible. Dataiku Data Science Studio users find the pricing plans affordable and suitable, and the setup cost manageable. The licensing options allow for seamless integration., Databricks users appreciate its value in increasing efficiency, productivity, and data analysis capabilities. Dataiku Data Science Studio users report significant cost savings, improved decision making, increased revenue generation, and valuable investments. Integrations and collaboration contribute to a positive ROI.
Room for Improvement: Databricks needs improvements in data visualization, monitoring and debugging tools, integration with external data sources, documentation for beginners, and pricing flexibility. Dataiku Data Science Studio requires enhancements in various features to optimize its platform.
Deployment and customer support: The user reviews for Databricks show varying durations for deployment, setup, and implementation. Some users mention spending three months on deployment and an additional week on setup, while others mention just a week for both. On the other hand, the reviews for Dataiku Data Science Studio mention different durations for each phase, but suggest considering deployment and setup together if they are within a short timeframe., Databricks provides efficient, helpful, and prompt customer service with knowledgeable and responsive staff. Their support team is proactive in solving issues. Dataiku also offers satisfactory customer service, with prompt and effective staff who provide knowledgeable and friendly assistance.
The summary above is based on 48 interviews we conducted recently with Databricks and Dataiku Data Science Studio users. To access the review's full transcripts, download our report.
"It is a cost-effective solution."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"It is fast, it's scalable, and it does the job it needs to do."
"Databricks' most valuable feature is the data transformation through PySpark."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well."
"Automation with Databricks is very easy when using the API."
"The solution is quite stable."
"The most valuable feature of this solution is that it is one tool that can do everything, and you have the ability to very easily push your design to prediction."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"Extremely easy to use with its GUI-based functionality and large compatibility with various data sources. Also, maintenance processes are much more automated than ever, with fewer errors."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"The most valuable feature is the set of visual data preparation tools."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"Data Science Studio's data science model is very useful."
"Databricks is not geared towards the end-user, but rather it is for data engineers or data scientists."
"There is room for improvement in visualization."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."
"The pricing of Databricks could be cheaper."
"There is room for improvement in the documentation of processes and how it works."
"The solution has some scalability and integration limitations when consolidating legacy systems."
"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"I think it would help if Data Science Studio added some more features and improved the data model."
"In the next release of this solution, I would like to see deep learning better integrated into the tool and not simply an extension or plugin."
"The ability to have charts right from the explorer would be an improvement."
"The interface for the web app can be a bit difficult. It needs to have better capabilities, at least for developers who like to code. This is due to the fact that everything is enabled in a single window with different tabs. For them to actually develop and do the concurrent testing that needs to be done, it takes a bit of time. That is one improvement that I would like to see - from a web app developer perspective."
"There were stability issues: 1) SQL operations, such as partitioning, had bugs and showed wrong results. 2) Due to server downtime, scheduled processes used to fail. 3) Access to project folders was compromised (privacy issue) with wrong people getting access to confidential project folders."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Dataiku is ranked 11th in Data Science Platforms with 7 reviews. Databricks is rated 8.2, while Dataiku is rated 8.2. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Dataiku writes "Gives different aspects of modeling approaches and good for multiple teams' collaboration". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dremio, Microsoft Azure Machine Learning Studio and Azure Stream Analytics, whereas Dataiku is most compared with KNIME, Alteryx, RapidMiner, Microsoft Azure Machine Learning Studio and Amazon SageMaker. See our Databricks vs. Dataiku report.
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