Databricks Data Intelligence Platform vs. Dataiku

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Databricks Data Intelligence Platform
Score 8.6 out of 10
N/A
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run…
$0.07
Per DBU
Dataiku
Score 8.8 out of 10
N/A
Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.N/A
Pricing
Databricks Data Intelligence PlatformDataiku
Editions & Modules
Standard
$0.07
Per DBU
Premium
$0.10
Per DBU
Enterprise
$0.13
Per DBU
Discover
Contact sales team
Business
Contact sales team
Enterprise
Contact sales team
Offerings
Pricing Offerings
Databricks Data Intelligence PlatformDataiku
Free Trial
NoYes
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Databricks Data Intelligence PlatformDataiku
Top Pros
Top Cons
Features
Databricks Data Intelligence PlatformDataiku
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Databricks Data Intelligence Platform
-
Ratings
Dataiku
9.1
4 Ratings
9% above category average
Connect to Multiple Data Sources00 Ratings10.04 Ratings
Extend Existing Data Sources00 Ratings10.04 Ratings
Automatic Data Format Detection00 Ratings10.04 Ratings
MDM Integration00 Ratings6.52 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Databricks Data Intelligence Platform
-
Ratings
Dataiku
10.0
4 Ratings
17% above category average
Visualization00 Ratings9.94 Ratings
Interactive Data Analysis00 Ratings10.04 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Databricks Data Intelligence Platform
-
Ratings
Dataiku
10.0
4 Ratings
20% above category average
Interactive Data Cleaning and Enrichment00 Ratings10.04 Ratings
Data Transformations00 Ratings10.04 Ratings
Data Encryption00 Ratings10.04 Ratings
Built-in Processors00 Ratings10.04 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Databricks Data Intelligence Platform
-
Ratings
Dataiku
8.7
4 Ratings
4% above category average
Multiple Model Development Languages and Tools00 Ratings5.14 Ratings
Automated Machine Learning00 Ratings10.04 Ratings
Single platform for multiple model development00 Ratings10.04 Ratings
Self-Service Model Delivery00 Ratings10.04 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Databricks Data Intelligence Platform
-
Ratings
Dataiku
9.0
4 Ratings
6% above category average
Flexible Model Publishing Options00 Ratings9.04 Ratings
Security, Governance, and Cost Controls00 Ratings9.04 Ratings
Best Alternatives
Databricks Data Intelligence PlatformDataiku
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User Ratings
Databricks Data Intelligence PlatformDataiku
Likelihood to Recommend
8.4
(17 ratings)
10.0
(4 ratings)
Usability
9.5
(3 ratings)
10.0
(1 ratings)
Support Rating
8.6
(2 ratings)
9.4
(3 ratings)
Contract Terms and Pricing Model
8.0
(1 ratings)
-
(0 ratings)
Professional Services
10.0
(1 ratings)
-
(0 ratings)
User Testimonials
Databricks Data Intelligence PlatformDataiku
Likelihood to Recommend
Databricks
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
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Dataiku
Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.
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Pros
Databricks
  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
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Dataiku
  • The intuitiveness of this tool is very good.
  • Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
  • The way you can control things, the set of APIs gives a lot of flexibility to a developer.
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Cons
Databricks
  • Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
  • Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
  • Visualization in MLFLOW experiment can be enhanced
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Dataiku
  • End product deployment.
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Usability
Databricks
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
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Dataiku
As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.
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Support Rating
Databricks
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
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Dataiku
The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.
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Alternatives Considered
Databricks
Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.
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Dataiku
Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.
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Return on Investment
Databricks
  • The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
  • DB has the ability to terminate/time out instances which helps manage cost.
  • The ability to quickly access typical hard to build data scenarios easily is a strength.
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Dataiku
  • Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
  • Platform also ease tracking of data processing workflow, unlike Excel.
  • Build-in data visualizations covers many use cases with minimal customization; time saver.
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ScreenShots