Dataiku and Dremio are leading competitors in the data analytics and data management sector. Dataiku has an advantage with its comprehensive feature set and user-friendly interface, while Dremio is noted for superior performance and advanced data processing.
Features: Dataiku focuses on collaborative data science, facilitating data preparation, machine learning, and deployment. It integrates seamlessly with various data storage systems and provides extensive automation capabilities. Dremio emphasizes data virtualization and acceleration for high-speed querying and advanced transformation. It stands out for enhancing data performance, making it ideal for performance-driven tasks.
Room for Improvement: Dataiku could enhance scalability for larger datasets and improve integration with non-traditional data sources. Increased flexibility in its automation capabilities and more robust real-time data processing would be beneficial. Dremio would benefit from simplifying its deployment process, enriching support for more data sources, and enhancing the user interface for easier navigation, especially for users with less technical expertise.
Ease of Deployment and Customer Service: Dataiku offers straightforward deployment and strong support services, ensuring easy implementation and maintenance. Its customer service is responsive and thorough. Dremio's deployment process can be more complex, focusing heavily on optimizing performance. However, its customer support is noted for technical expertise, though it requires deeper engagement from users.
Pricing and ROI: Dataiku is associated with a higher initial setup cost but provides significant ROI through efficiency in data preparation and machine learning. The pricing reflects its all-in-one platform benefits. Dremio presents a more cost-effective option when prioritizing data performance, offering value through reduced query times and enhanced processing capabilities. Each product's pricing aligns with its strengths: Dataiku for comprehensive utility and Dremio for speed and optimization.
The market is competitive, and Dataiku must adopt a consumption-based model instead of the current monthly model.
Dremio surely saves time, reduces costs, and all those things because we don't have to worry so much about the infrastructure to make the different tools communicate.
Dataiku partners with local industry experts who understand the business better and provide support.
The support team does not provide adequate assistance.
The customer service team is helpful and responsive, more or less on time.
We have had to reach out for customer support many times, and they respond, so they are pretty supportive about some long-term issues.
Dremio's scalability can handle growing data and user demands easily.
Internally, if it's on Docker or Kubernetes, scalability will be built into the system.
In terms of stabilization, if my data has no outlier creation in the raw data, then it is quite stable.
I rate Dremio a nine in terms of stability.
The license is very expensive.
I would love for Dataiku to allow more flexibility with code-based components and provide the possibility to extend it by developing and integrating custom components easily with existing ones.
Dataiku's pricing is very high, and commercial transparency is a challenge.
Starburst comes with around 50 connectors now.
I see that many times the new versions of Dremio have not fixed old bugs, and in some new versions, old problems that were previously fixed come back again, so I think the upgrade part could use improvement.
It should be easier to get Arctic or an open-source version of Arctic onto the software version so that development teams can experiment with it.
I find the pricing of Dataiku quite affordable for our customers, as they are usually large companies.
The pricing for Dataiku is very high, which is its biggest downside.
There are no extra expenses beyond the existing licensing cost.
Dataiku primarily enhances the speed at which our customers can develop or train their machine learning models because it is a drag-and-drop platform.
This feature is useful because it simplifies tasks and eliminates the need for a data scientist.
It offers most of the capabilities required for data science, MLOps, and LLMOps.
Having everything under one system and an easier-to-work-with interface, along with having API integrations, adds significant value to working with Dremio.
You just get the source, connect the data, get visualization, get connected, and do whatever you want.
The first feature that stands out for me in Dremio is the federated type of query, which allows the possibility to use multiple endpoints without worrying about writing custom SQL that runs only for SQL Server or for Postgres and Redshift.
| Product | Market Share (%) |
|---|---|
| Dataiku | 10.5% |
| Dremio | 2.9% |
| Other | 86.6% |
| Company Size | Count |
|---|---|
| Small Business | 4 |
| Midsize Enterprise | 1 |
| Large Enterprise | 8 |
| Company Size | Count |
|---|---|
| Small Business | 1 |
| Midsize Enterprise | 5 |
| Large Enterprise | 5 |
Dataiku Data Science Studio is acclaimed for its versatile capabilities in advanced analytics, data preparation, machine learning, and visualization. It streamlines complex data tasks with an intuitive visual interface, supports multiple languages like Python, R, SQL, and scales efficiently for large dataset handling, boosting organizational efficiency and collaboration.
Dremio is a data analytics platform designed to simplify and expedite the data analysis process by enabling direct querying across multiple data sources without the need for data replication. This solution stands out due to its approach to data lake transformation, offering tools that allow users to access and query data stored in various formats and locations as if it were all in a single relational database.
At its core, Dremio facilitates a more streamlined data management experience. It integrates easily with existing data lakes, allowing organizations to continue using their storage of choice, such as AWS S3, Microsoft ADLS, or Hadoop, without data migration. Dremio supports SQL queries, which means it seamlessly integrates with familiar BI tools and data science frameworks, enhancing user accessibility and reducing the learning curve typically associated with adopting new data technologies.
What Are Dremio's Key Features?
What Benefits Should Users Expect?
When evaluating Dremio, potential users should look for feedback on its query performance, especially in environments with large and complex data sets. Reviews might highlight the efficiency gains from using Dremio’s data reflections and its ability to integrate with existing BI tools without significant changes to underlying data structures. Also, check how other users evaluate its ease of deployment and scalability, particularly in hybrid and cloud environments.
How is Dremio Implemented Across Different Industries?
Dremio is widely applicable across various industries, including finance, healthcare, and retail, where organizations benefit from rapid, on-demand access to large volumes of data spread across disparate systems. For instance, in healthcare, Dremio can be used to analyze patient outcomes across different data repositories, improving treatment strategies and operational efficiencies.
What About Dremio’s Pricing, Licensing, and Support?
Dremio offers a flexible pricing model that caters to different sizes and types of businesses, including a free community version for smaller teams and proof-of-concept projects. Their enterprise version is subscription-based, with pricing varying based on the deployment scale and support needs. Customer support is comprehensive, featuring dedicated assistance, online resources, and community support.
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