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.
"Databricks is a scalable solution. It is the largest advantage of the solution."
"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."
"I like cloud scalability and data access for any type of user."
"The ability to stream data and the windowing feature are valuable."
"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"The solution is an impressive tool for data migration and integration."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"It can send out large data amounts."
"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."
"The solution is quite stable."
"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."
"Data Science Studio's data science model is very useful."
"Cloud-based process run helps in not keeping the systems on while processes are running."
"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."
"I like the interface, which is probably my favorite part of the solution. It is really user-friendly for an IT person."
"The most valuable feature is the set of visual data preparation tools."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"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."
"It would be great if Databricks could integrate all the cloud platforms."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
"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."
"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."
"Databricks can improve by making the documentation better."
"I think it would help if Data Science Studio added some more features and improved the data model."
"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."
"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."
"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."
"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."
"The ability to have charts right from the explorer would be an improvement."
"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."
"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."
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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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.