Databricks vs Dataiku comparison

Comparison Buyer's Guide

Executive SummaryUpdated on Mar 6, 2024

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.

To learn more, read our detailed Databricks vs. Dataiku Report (Updated: July 2024).

Buyer's Guide

Databricks vs. Dataiku

July 2024

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Categories and Ranking

IBM SPSS Statistics

Mindshare comparison

As of July 2024, in the Data Science Platforms category, the mindshare of IBM SPSS Statistics is 3.0%, up from 2.3% compared to the previous year. The mindshare of Databricks is 21.5%, up from 19.8% compared to the previous year. The mindshare of Dataiku is 12.9%, up from 6.8% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Data Science Platforms

Unique Categories:

Data Mining

21.6%

Streaming Analytics

15.4%

No other categories found

Q&A Highlights

Alphonse Okossi

Lead Data Scientist at a manufacturing company with 10,001+ employees

Sep 17, 2023

Which product would you choose for a data science team: Databricks vs Dataiku?

Databricks and Dataiku are excellent Data Science platforms but have different strengths and weaknesses. Below is a comparison of the two products based on several parameters: Cost It is reported that Databricks is a subscription-based service, and its cost varies based on the level of service you choose. They say the service also offers a free tier for development and testing purposes. Dataiku is also a subscription-based service, as reported, and the cost varies depending on the number of users and the features you need. The service also offers a free trial. Team Collaboration Databricks offers many features that help make team collaborations on Data Science projects easier, including notebooks, projects, and workspaces. Dataiku also offers several features for team collaboration, including shared projects, workspaces, and user permissions. Application Databricks may be a good choice for various Data Science applications, including Machine Learning, Data Engineering, and Data Visualization. Dataiku may also be worth considering for a wide range of Data Science applications, but they say it is particularly well-suited for Machine Learning. Data Security Databricks has several security features, including encryption, access control, and auditing. Dataiku also offers the above-mentioned security features. Databricks is a good choice for a wide range of data science applications and offers several features that make it easy for teams to collaborate. Databricks also offers a good level of data security. However, if your organization needs a Data Science platform more suitable for Machine Learning, Dataiku may be better.

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Featured Reviews

Ali Bin Tahir

Research Executive at NielsenIQ

Mar 1, 2024

Provides comprehensive data analysis and has a simple setup process

We use the product to conduct multiple and diverse statistical analyses across various datasets The software offers consistency across multiple research projects helping us with predictive analytics capabilities. The product’s most valuable capability is to handle large datasets and ensure…

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reviewer2058633

Data Engineering Manager at a pharma/biotech company with 10,001+ employees

Jan 13, 2023

A great and easy-to-use platform for data engineers and data scientists who rely on a large dataset to do advanced analytics reporting

The initial setup was easy to complete and not complex. It may initially be challenging for a new user, but it improves over time. The CICD pipeline works well with the Microsoft Azure platform because the continuous integration, development and deployment come with the Git integration. It makes it easier for Databricks and the CICD. The deployment should be improved from the perspective of auto ML functionality, so it doesn't have intensive automation learning capability. We don't use Databricks directly because we work on a data science project. It requires an auto ML and inbuilt machine learning capability. We found capabilities like the large language model using NLP and other deep learning models that are not that intensive. It is meant for data engineering purposes rather than data science purposes. It'll be great if Databricks could be intensive for data science. We used a third-party, Dataiku platform for the deployment, where we connected to Databricks and completed the ML ops. We required about three people for deployment, and it is easy to maintain the solution.

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RangeshKunnavakkam

Consultant at Netsoft

May 17, 2024

Gives different aspects of modeling approaches and good for multiple teams' collaboration

I used DataRobot. Dataiku has a different kind of structure to it. It's not financially heavy like DataRobot, which caters more to financial companies, like banks. Dataiku doesn't have that yet. I think they are also working on that area. But yeah, there are some key differences between the two products. DataRobot has an additional feature with financial firms that it creates all these financial metrics when you run a time series analysis. Those things I have not seen in Dataiku. If any financial company is choosing between DataRobot and Dataiku, they will definitely go for DataRobot because it creates all these financial metrics. It creates deltas, time series, time difference fields, and things like that. So, that is an added feature that DataRobot has.

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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

"Capability analysis is one of the main and valuable functions. We also do some hypothesis testing in Minitab and summary stats. These are the functions that we find very useful."

"SPSS is quite robust and quicker in terms of providing you the output."

"You can find a complete algorithm in the solution and use it. You don't need to write your own algorithms for predictive analytics. That's the most valuable feature and the main one we use."

"The most valuable features are the small learning curve and its ability to hold a lot of data."

"in terms of the simplicity, I think the SPSS basic can handle it."

"The most valuable features mainly include factor analysis, correlation analysis, and geographic analysis."

"It is a modeling tool with helpful automation."

"Custom tables and macros: They allow us to create useful reports quickly for a broad audience."

More IBM SPSS Statistics pros

"Databricks' most valuable feature is the data transformation through PySpark."

"Its lightweight and fast processing are valuable."

"The processing capacity is tremendous in the database."

"I like the ability to use workspaces with other colleagues because you can work together even without seeing the other team's job."

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

"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."

"I haven't heard about any major stability issues. At this time I feel like it's stable."

"Databricks is a scalable solution. It is the largest advantage of the solution."

More Databricks pros

"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."

"The solution is quite stable."

"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."

"Data Science Studio's data science model is very useful."

"If many teams are collaborating and sharing Jupyter notebooks, it's very useful."

"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."

Cons

"Needs more statistical modelling functions."

"The solution needs more planning tools and capabilities."

"Each algorithm could be more adaptable to some industry-specific areas, or, in some cases, adapted for maintenance."

"One of the areas that should be similar to Minitabs is the use of blogs. The Minitabs blog helps users understand the tools and gives lots of practical examples. Following the SPSS manual is cumbersome. It's a good, exhaustive manual, but it's not practical to use. With Minitabs, you can go to the blogs and find specific articles written about various components and it's very helpful. Without blogs, we find SPSS more complicated."

"The design of the experience can be improved."

"The statistics should be more self-explanatory with detailed automated reports."

"Improvements are needed in the user interface, particularly in terms of user-friendliness."

"The technical support should be improved."

More IBM SPSS Statistics cons

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

"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."

"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."

"There would also be benefits if more options were available for workers, or the clusters of the two points."

"Databricks has a lack of debuggers, and it would be good to see more components."

"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."

"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."

"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."

More Databricks cons

"Although known for Big Data, the processing time to process 1.8 billion records was terribly slow (five days)."

"Dataiku still needs some coding, and that could be a difference where business data scientists would go for DataRobot more than Dataiku."

"I think it would help if Data Science Studio added some more features and improved the data model."

"I find that it is a little slow during use. It takes more time than I would expect for operations to complete."

"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."

"Server up-time needs to be improved. Also, query engines like Spark and Hive need to be more stable."

"The ability to have charts right from the explorer would be an improvement."

More Dataiku cons

Pricing and Cost Advice

"It's quite expensive, but they do a special deal for universities."

"SPSS is an expensive piece of software because it's incredibly complex and has been refined over decades, but I would say it's fairly priced."

"If it requires lot of data processing, maybe switching to IBM SPSS Clementine would be better for the buyer."

"The pricing of the modeler is high and can reduce the utility of the product for those who can not afford to adopt it."

"The price of this solution is a little bit high, which was a problem for my company."

"We think that IBM SPSS is expensive for this function."

"I rate the tool's pricing a five out of ten."

"More affordable training for new staff members."

More IBM SPSS Statistics pricing and cost advice

"Licensing on site I would counsel against, as on-site hardware issues tend to really delay and slow down delivery."

"There are different versions."

"The solution uses a pay-per-use model with an annual subscription fee or package. Typically this solution is used on a cloud platform, such as Azure or AWS, but more people are choosing Azure because the price is more reasonable."

"The cost is around $600,000 for 50 users."

"The product pricing is moderate."

"The licensing costs of Databricks depend on how many licenses we need, depending on which Databricks provides a lot of discounts."

"Price-wise, I would rate Databricks a three out of five."

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

More Databricks pricing and cost advice

"Pricing is pretty steep. Dataiku is also not that cheap."

"The annual licensing fees are approximately €20 ($22 USD) per key for the basic version and €40 ($44 USD) per key for the version with everything."

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Top Industries

By visitors reading reviews

University

17%

Educational Organization

13%

Computer Software Company

Financial Services Firm

16%

Computer Software Company

12%

Manufacturing Company

Healthcare Company

Financial Services Firm

18%

Educational Organization

15%

Manufacturing Company

Computer Software Company

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

Questions from the Community

What do you like most about IBM SPSS Statistics?

The software offers consistency across multiple research projects helping us with predictive analytics capabilities.

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What is your experience regarding pricing and costs for IBM SPSS Statistics?

While the pricing of the product may be higher, the accompanying service and features justify the investment. However...

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What needs improvement with IBM SPSS Statistics?

In some cases, the product takes time to load a large dataset. They could improve this particular area.

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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 ...

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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 designe...

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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 analyti...

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What is your experience regarding pricing and costs for Dataiku Data Science Studio?

Pricing is pretty steep. Dataiku is also not that cheap. It depends on the client and how much they want to spend tow...

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What needs improvement with Dataiku Data Science Studio?

The no-code/low-code aspect, where DataRobot doesn't need much coding at all. Dataiku still needs some coding, and th...

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What is your primary use case for Dataiku Data Science Studio?

My current client has Dataiku. We do sentiment analysis and some small large language models right now. We use Dataik...

See all answers

Comparisons

Alteryx vs IBM SPSS Statistics

Compared 16% of the time

TIBCO Statistica vs IBM SPSS Statistics

Compared 14% of the time

Microsoft Azure Machine Learning Studio vs IBM SPSS Statistics

Compared 12% of the time

Weka vs IBM SPSS Statistics

Compared 10% of the time

Anaconda vs IBM SPSS Statistics

Compared 3% of the time

More IBM SPSS Statistics Competitors

Amazon SageMaker vs Databricks

Compared 11% of the time

Informatica PowerCenter vs Databricks

Compared 9% of the time

Dremio vs Databricks

Compared 7% of the time

Azure Stream Analytics vs Databricks

Compared 5% of the time

Microsoft Power BI vs Databricks

Compared 5% of the time

More Databricks Competitors

KNIME vs Dataiku

Compared 13% of the time

Alteryx vs Dataiku

Compared 11% of the time

RapidMiner vs Dataiku

Compared 9% of the time

Microsoft Azure Machine Learning Studio vs Dataiku

Compared 7% of the time

Starburst Galaxy vs Dataiku

Compared 1% of the time

More Dataiku Competitors

Product Reports

Buyer's Guide

IBM SPSS Statistics

July 2024

Download IBM SPSS Statistics product report

Buyer's Guide

Databricks

July 2024

Download Databricks product report

Buyer's Guide

Dataiku

July 2024

Download Dataiku product report

Also Known As

SPSS Statistics

Databricks Unified Analytics, Databricks Unified Analytics Platform, Redash

Dataiku DSS

Learn More

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IBM

Databricks

Dataiku

Overview

IBM SPSS Statistics is a powerful data mining solution that is designed to aid business leaders in making important business decisions. It is designed so that it can be effectively utilized by organizations across a wide range of fields. SPSS Statistics allows users to leverage machine learning algorithms so that they can mine and analyze data in the most effective way possible.

IBM SPSS Statistics Benefits

Some of the ways that organizations can benefit by choosing to deploy IBM SPSS Statistics include:

Ease of use. SPSS Statistics enables users to simply and intuitively take control of their statistical needs. The solution is designed so that analysts who do not know how to code can easily make full use of the various tools and capabilities that SPSS Statistics has to offer. Its command language is so straightforward that it does not require users to undergo special training before they use it.

Comprehensive and flexible build. SPSS Statistics is designed to be both a comprehensive and highly flexible analytics solution. It enables users to utilize a variety of integrations that make it easy for users to add features that they might feel they are missing.

Automation. SPSS Statistics makes it simple for users to automate basic tasks that they might otherwise devote too much time worrying about. Tasks like calculation or data gathering can be delegated to the system while more conceptual tasks like data analysis are given to an organization’s analysts to handle.

IBM SPSS Statistics Features

Intuitive user interface. SPSS Statistics enables users to deploy an intuitive interface that makes the process of system management simple. Among the other components of this interface is a drag-and-drop feature that makes analysis and management possible for anyone who wants to use it.

Advanced data visualizations. Analysts that employ SPSS Statistics gain access to tools that empower them to create and export data visualizations. These visualizations can be formatted in many different ways depending on what the user needs.

Local data storage. SPSS Statistics has the ability to securely store data on a user’s computer. This enables them to add layers of security that would not necessarily be present if the data was stored in the cloud.

Reviews from Real Users

IBM SPSS Statistics is a highly effective solution that stands out when compared to many of its competitors. Two major advantages it offers are the wealth of functionalities that it provides and its high level of accessibility.

An Emeritus Professor of Health Services Research at a university writes, "The most valuable feature of IBM SPSS Statistics is all the functionality it provides. Additionally, it is simple to do the five-way analysis that you can in a multidimensional setup space. It's the multidimensional space facility that is most useful."

A Director of Systems Management & MIS Operations at a university, says, “The SPSS interface is very accessible and user-friendly. It's really easy to get information from it. I've shared it with experts and beginners, and everyone can navigate it.”

Databricks is an industry-leading data analytics platform which is a one-stop product for all data requirements. Databricks is made by the creators of Apache Spark, Delta Lake, ML Flow, and Koalas. It builds on these technologies to deliver a true lakehouse data architecture, making it a robust platform that is reliable, scalable, and fast. Databricks speeds up innovations by synthesizing storage, engineering, business operations, security, and data science.

Databricks is integrated with Microsoft Azure, Amazon Web Services, and Google Cloud Platform. This enables users to easily manage a colossal amount of data and to continuously train and deploy machine learning models for AI applications. The platform handles all analytic deployments, ranging from ETL to models training and deployment.

Databricks deciphers the complexities of processing data to empower data scientists, engineers, and analysts with a simple collaborative environment to run interactive and scheduled data analysis workloads. The program takes advantage of AI’s cost-effectivity, flexibility, and cloud storage.

Databricks Key Features

Some of Databricks key features include:

Cloud-native: Works well on any prominent cloud provider.
Data storage: Stores a broad range of data, including structured, unstructured, and streaming.
Self-governance: Built-in governance and security controls.
Flexibility: Flexible for small-scale jobs as well as running large-scale jobs like Big Data processing because it’s built from Spark and is specifically optimized for Cloud environments.
Data science tools: Production-ready data tooling, from engineering to BI, AI, and ML.
Familiar languages: While Databricks is Spark-based, it allows commonly used programming languages like R, SQL, Scala, and Python to be used.
Team sharing workspaces: Creates an environment that provides interactive workspaces for collaboration, which allow multiple members to collaborate for data model creation, machine learning, and data extraction.
Data source: Performs limitless Big Data analytics by connecting to Cloud providers AWS, Azure, and Google, as well as on-premises SQL servers, JSON and CSV.

Reviews from Real Users

Databricks stands out from its competitors for several reasons. Two striking features are its collaborative ability and its ability to streamline multiple programming languages.

PeerSpot users take note of the advantages of these features. A Chief Research Officer in consumer goods writes, “We work with multiple people on notebooks and it enables us to work collaboratively in an easy way without having to worry about the infrastructure. I think the solution is very intuitive, very easy to use. And that's what you pay for.”

A business intelligence coordinator in construction notes, “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.”

An Associate Manager who works in consultancy mentions, “The technology that allows us to write scripts within the solution is extremely beneficial. If I was, for example, able to script in SQL, R, Scala, Apache Spark, or Python, I would be able to use my knowledge to make a script in this solution. It is very user-friendly and you can also process the records and validation point of view. The ability to migrate from one environment to another is useful.”

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.

Sample Customers

LDB Group, RightShip, Tennessee Highway Patrol, Capgemini Consulting, TEAC Corporation, Ironside, nViso SA, Razorsight, Si.mobil, University Hospitals of Leicester, CROOZ Inc., GFS Fundraising Solutions, Nedbank Ltd., IDS-TILDA

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

BGL BNP Paribas, Dentsu Aegis, Link Mobility Group, AramisAuto

Buyer's Guide

Databricks vs. Dataiku

July 2024

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