Dataiku vs. Iguazio

I would recommend it because it's an amazing tool for different levels of users. From Business Analysts to Data Scientists to Managers, various employees can make use of this tool to make data-driven decisions. I'm not sure about where it would be less appropriate as I'm using it as Data Scientist and so far it pretty much caters to my need.

Incentivized

It is built in a way that supports low latency real-time data processing. The model can be triggered using different streaming engines without the need to write additional codes. It has serverless that enables developers to write code [that] automatically transform to auto-scaling production workload, significantly reducing time to market and resources.

Incentivized

• Very intuitive and easy to use UI, making a lot of types of users can collaborate with each other easily, by visualizing the same workflow.
• Many building blocks can be reused immediately, avoid a lot of non-standard boiler plate implementation.
• Data pre-analysis and feature engineering assistance increase the productivity as well as the efficiency of data scientists.
• Many data connectors support wide range of data storage, from SQL, TeraData, Hadoop Hive, etc.
• Support from research till final MaaS solution deployment.

Incentivized

• Dynamic scaling capacity.
• Central Metadata management.
• Data ingestion and preparation.

Incentivized

• Its community support is very limited at the moment
• Complex to integrate with automation tools such as Blue Prism

Incentivized

• The user interface is not so much user-friendly, and easy-to-use, navigate.

Incentivized

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.

Incentivized

The open source user community is friendly, helpful, and responsive, at times even outdoing commercial software vendors. Documentation is also top notch, and usually resolves issues without the need for human interactions. Great product design, with a focus on user experience, also makes platform use intuitive, thus reducing the need for explicit support.

Incentivized

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.

Incentivized

Iguazio provides a generic and easy to use mechanism to describe and track code,metadata,inputs and outputs of machine learning related tasks(executions). Users is able to track various elements, store them in a database and presents all running jobs as well as historical jobs in a single report. With Iguazio MLOps platform, data engineers,data scientist and MLOps engineers work in an unified environment with processes that increase productivity right out of the box.

Incentivized

• 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.

Incentivized

• Is a fully integrated solution with a user-friendly portal.
• Manage our ML pipeline end-to-end using Full-stack,user friendly environment.
• Iguazio enables our teams to manage all artefacts throughout their lifecycle.
• Enhance team work and collaboration in our teams.

Incentivized